Many AI purveyors and proponents claim that the arrival of generative AI (GenAI) will give organizations that adopt it a tremendous (or “huge,” “massive,” or “lasting”) competitive advantage. Here is one example from Capitalizing on the Gen AI Enterprise Software Opportunity, a publication from Harvard Business Review’s Analytic Services group that Amazon Web Services sponsored:

“Adding generative AI functionality to enterprise software holds the potential to create huge competitive advantage for software companies.”

As former football coach and current ESPN College Gameday analyst Lee Corso would say to those making such pronouncements, “Not so fast, my friends.”

While some companies in some industries will derive competitive advantages from GenAI and other forms of machine intelligence, most will not. The most likely outcome for enterprise software makers and others is that they will create a tremendous amount of “consumer surplus” and end up in the same relative positions they were in before the introduction of GenAI.

As Michael Porter argued in “What is Strategy?”, competitive advantage is built on differences that can be sustained. If every software vendor adds a chatbot or AI Assistant to its software, which they will, then no specific vendor will gain an advantage.

This video1 explains the most likely outcome, given that these software vendors have access to the same or equivalent GenAI technologies.

Paraphrasing Porter from the same article, while adding an AI Assistant results in absolute improvement for all, it leads to relative improvement for none. Generic GenAI applications will become a cost of doing business rather than a source of competitive advantage.

Any company, not just enterprise software developers, pursuing advantages through AI and other machine intelligence will need a much more comprehensive and sophisticated strategy than simply adding an AI chatbot to their products or processes.

What is Theory of Mind?

Theory of Mind (ToM) is a human’s ability to understand that people have thoughts and beliefs that may be different from their own. Testing and improving a large language model’s (LLM) ability to understand human intentions and behaviors can dramatically improve its responses.

There is “explicit ToM,” which defines the degree to which an LLM understands the user’s mental state, and “applied ToM,” which defines how well the LLM understands human intentions and decision-making.

Why It Matters

Improving AI’s understanding of human intentions, emotions, and behaviors enables these systems to respond more intuitively to human cues, predict customer needs, and make more contextually aware recommendations. Systems with enhanced ToM can dramatically improve business performance in (for example) customer interactions, product/service design, team dynamics, and decision-making.

Ways to Improve Business Performance

1. Improve Customer Experiences

• Improved ToM enables AI to better understand customer emotions, tone, and intent, allowing it to adapt its responses for more personalized and empathetic interactions.

• This can help businesses improve customer experiences and build stronger customer loyalty.

2. Improve Product and Service Design

• Improved ToM enables AI to provide deeper insights into user behavior, anticipating preferences and uncovering unmet needs in ways traditional research methods can’t match.

• This capability allows businesses to design products and services that feel more personalized, intuitive, and responsive to users, increasing customer satisfaction.

3. Enhance Team Productivity

• With a more nuanced understanding of individual and team dynamics, AI with improved ToM can provide feedback that aligns with each employee’s strengths, stressors, and motivators.

• This can improve team performance by identifying collaboration opportunities and suggesting team structures that reflect each member’s unique attributes.

4. Improve Decision-Making

• Improved ToM can better interpret factors like urgency, stress levels, and decision-makers' confidence, offering advice that considers both “hard” data and “soft” human factors.

• This leads to richer, more reliable insights, allowing leaders to make better-informed, more responsive decisions in dynamic situations.

Action Plan

• Start with Customer Service and Support: Run experiments in customer-facing functions to assess how improved ToM impacts service satisfaction and customer retention.

• Incorporate into Product and Service Development: Use AI with improved ToM to gather insights on user preferences and predict emerging needs, guiding design teams toward more intuitive and personalized products and services.

• Embed in Leadership Training: Educate leaders on effectively interpreting and implementing AI-derived insights in decision-making and organizational design frameworks.

• Monitor Closely: Implement regular evaluations to ensure these AI models adapt accurately to evolving team and customer behaviors.

Takeaway

Improved ToM leads to a future where machines don’t just compute—they empathize, anticipate, and adapt. For senior leaders, now is the time to explore how this capability could improve organizational performance. 

How could this capability improve performance in your business?

Dive In

• Theory of Mind in AI – Allen Institute for AI

• The Emergence of Theory of Mind in AI Models – arXiv

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Best business article(s) I read this week…

• A Practical Guide to Gaining Value From LLMs

  This article provides business leaders with a high-level, structured approach to effectively using large language models (LLMs). It stresses the importance of identifying suitable business tasks for automation by breaking processes into smaller steps and assessing the cost-effectiveness of each task. Key strategies include using simple prompting, retrieval-augmented generation, and instruction fine-tuning to align LLM outputs with specific business needs. The author argues that by adopting a thoughtful, cost-aware approach, companies can pilot LLM-based solutions that potentially enhance productivity while managing the associated risks.

Best technical article(s) I read this week…

• Artificial Intelligence and Strategic Decision-Making

  The article examines how artificial intelligence (AI), especially large language models (LLMs), could transform strategic decision-making (SDM) by enhancing tools like Porter’s Five Forces and scenario planning. Empirical studies show that LLMs can generate and evaluate business strategies at a level comparable to entrepreneurs and investors, potentially making strategy development faster and more scalable. However, the article highlights uncertainties about AI’s ability to create novel strategies and how competitive dynamics might shift as AI capabilities improve. This article is valuable for business leaders because it offers a realistic view of AI's current and potential roles in SDM, providing a framework to understand how AI might affect competitive advantage, innovation, and firm performance.

Other items I found valuable/interesting…

• Claude AI 3.5 Haiku Dropped. How Reading Feynman Reveals AI Trends

  This article analogizes the ideas of Nobel-prize-winning physicist Richard Feynman, specifically his 1959 talk titled “There’s plenty of room at the bottom,” with past, present, and future developments in AI.

• Microsoft Wants to Patent a Fix for AI Hallucinations

  Microsoft has filed a patent application for a system that reduces AI hallucinations. The proposed system cross-references AI-generated content with trusted data sources to ensure accuracy. This initiative aims to enhance the reliability of AI outputs, particularly in critical applications like healthcare and finance.

• Radio Station's Attempt to Replace Hosts with AI Ends in Absolute Disaster

  This article recounts the many missteps of a Polish radio station that replaced all of its human on-air talent with AI-generated personalities. It may be considered a good example of what not to do with AI.

Coolest thing I saw…

• Molmo

  This is the new multimodal (voice, text, images) model from The Allen Institute for Artificial Intelligence (Ai2). Various benchmarks show that it outperforms OpenAI’s ChatGPT-4o model. It’s also open, so you can use and download it for free. Its interface includes voice transcription, so you can say your prompts rather than type them. This capability is not available on ChatGPT or Claude but is on Gemini.
  
  The coolest feature is its ability to analyze images and identify objects. It does this by overlaying dots on the items that match the criteria. I tried a similar experiment on ChatGPT, Gemini, and Claude, but they returned text responses that missed the mark.

A company that caught my eye…

• Physical Intelligence

  The company, which makes AI-based software for robotic control, just closed a $400M round led by Jeff Bezos and OpenAI. The videos included in this blog post show early signs of what is possible when robots operate under a “generalist policy.”

In a previous article, I introduced a capability maturity model for organizational adoption and use of AI and other collaborative technologies (aka “collaborative intelligence” or CI).

This is the final article in this series. It describes Maturity Level 6, named “Renewing.” It will provide more detail about the observable characteristics at this level.

Now that the entire model is articulated, I will provide an assessment tool to help organizations determine their maturity level. In a future post, I will announce its availability and describe it.

Organizations at Maturity Level 5 have successfully transformed themselves into CI-driven businesses. However, the rate of growth and expansion of machine intelligence will continue apace for many more years. Thus, organizations must continually renew themselves (e.g., reach Maturity Level 6) to maintain their competitive positions and avoid the need for future transformations.

Characteristics of Organizations with "Renewing" Collaborative Intelligence

Organizational Awareness/Knowledge of CI Technologies

At Maturity Level 6, the organization has embedded mechanisms for continual, autonomous learning and adaptation that maintain its cutting-edge CI capabilities. CI systems autonomously scan industry advancements, research publications, and competitive landscapes to identify relevant CI innovations. These systems autonomously assess and distribute insights to relevant teams while adjusting internal learning resources in response to new CI developments.

Human leaders focus on evaluating and aligning the strategic implications of these discoveries rather than managing learning initiatives. This structure ensures that the organization remains aware of emerging CI technologies and can seamlessly integrate them, keeping it at the forefront of CI advancements without needing future large-scale transformation efforts.

CI-Related Skills/Experience and CI-Specific Skills Management Processes

The organization has established a self-sustaining skills ecosystem in which CI systems continuously evaluate emerging CI-related skills from the broader industry and automatically integrate relevant ones into employee training programs. These CI-driven processes assess skill gaps, track competency development, and assign advanced learning tasks autonomously as needed. CI systems provide employees with real-time feedback on their skill progression and suggest skill-enhancement opportunities tied to organizational needs. Human talent leaders oversee high-level workforce planning and strategic skills alignment, while CI systems maintain skill relevance and alignment.

Influence of CI on Business Strategy

CI systems autonomously analyze and interpret large-scale market, operational, and competitive data, providing strategic recommendations and real-time adjustments to business strategies. These CI systems independently generate strategic insights, propose adjustments, and simulate potential outcomes, allowing the organization to respond quickly to changes without manual intervention. Human leaders focus on high-level validation and refinement of these strategies, leveraging CI-generated insights for strategic alignment with long-term goals.

By continually monitoring the external environment, CI systems anticipate shifts that could impact strategic priorities. They autonomously initiate minor course corrections and inform leaders of significant trends. This approach ensures the organization’s strategies remain adaptive and forward-looking, minimizing the need for disruptive transformation.

CI Technologies Deployed

At Maturity Level 6, CI technologies are integrated across all critical functions with adaptive mechanisms for ongoing optimization. These CI systems monitor their performance and usage, autonomously initiating adjustments or recommending upgrades for review based on operational data, emerging standards, and evolving business needs. While some minor updates are handled autonomously, significant changes or upgrades require validation from human leaders, ensuring that all deployments align with strategic priorities.

Human oversight remains focused on evaluating and approving major technology shifts, while CI systems autonomously handle more minor optimizations and routine assessments. This balanced approach allows the organization to maintain cutting-edge capabilities while ensuring alignment with broader strategic objectives, minimizing the need for disruptive overhauls.

CI-Enabled Business Processes, Job Designs, and Team Structures

CI-enabled business processes, job designs, and team structures are highly adaptable and self-optimizing, enabling continuous alignment with evolving organizational goals. CI systems autonomously monitor workflow efficiency, task allocation, and team collaboration patterns, identifying areas for improvement and proposing adjustments to processes or team structures. Routine adjustments are implemented autonomously, while significant process changes or restructuring recommendations are flagged for human review to ensure strategic alignment.

Human roles focus on strategic guidance, complex decision-making, and areas requiring creativity or judgment, while CI systems manage and optimize day-to-day operations. This structure fosters a fluid, adaptive work environment where team configurations, processes, and roles can be adjusted as needed to maintain peak efficiency and alignment with current objectives, reducing the need for large-scale restructuring.

CI-Enabled or Embedded Products/Services

The organization’s products and services are designed to incorporate new CI technologies continually, with integration driven by economic justification and human approval. CI systems autonomously monitor advancements in CI capabilities and assess their potential impact on existing offerings, conducting cost-benefit analyses to determine the viability of upgrades or feature enhancements. For minor enhancements, CI systems may implement changes automatically; however, more significant updates require human evaluation to ensure alignment with strategic goals and market positioning.

This framework allows products and services to evolve dynamically based on real-time user feedback, usage patterns, and market trends. Human oversight ensures that only economically viable and strategically relevant innovations are introduced, while CI systems handle the day-to-day monitoring and implementation of minor improvements. This adaptive approach enables the organization to maintain a competitive edge by continuously refreshing its offerings without disruptive redevelopment cycles.

CI-Aware Management Processes/Metrics

At Maturity Level 6, management processes and metrics are optimized for continuous improvement. CI systems autonomously monitor, analyze, and report human and machine performance. These systems track metrics that assess the impact of CI on strategic objectives, operational efficiencies, and decision quality, generating insights that allow for ongoing adjustments to maximize performance.

Routine performance optimizations are handled autonomously, while significant trends or anomalies are flagged for human review. This enables leaders to make strategic adjustments based on real-time data. Human management focuses on refining high-level metrics and adapting long-term strategies as organizational needs evolve, while CI systems autonomously drive day-to-day performance tracking and reporting. This structure enables a continuously adaptive organization that proactively aligns with emerging opportunities and challenges.

CI Governance Processes and Policies

CI governance processes and policies are structured to ensure ongoing alignment with emerging CI technologies and regulatory standards. CI systems autonomously track compliance with internal policies and external regulations, managing routine audits and flagging potential policy deviations for human review. Policies are designed to evolve with minimal disruption, with CI systems recommending updates as new CI capabilities, risks, or regulations arise.

Human governance teams assess and approve recommendations for significant policy changes, focusing on strategic and ethical considerations. This adaptive governance framework allows the organization to stay compliant and ethically grounded, proactively addressing risks and seizing opportunities presented by CI advancements. Human oversight ensures that governance policies remain aligned with organizational values, while CI systems manage day-to-day compliance, supporting a seamless, secure CI environment without frequent manual intervention.

Organizational Culture

The organizational culture aligns with the core principles of collaborative intelligence, fostering an environment where continuous renewal and adaptation are core values. Employees view CI systems as essential collaborators and actively participate in refining human-machine workflows. CI-driven insights regularly inform team practices and decision-making. The culture emphasizes agility, openness to change, and a proactive approach to leveraging CI innovations.

Leadership reinforces this culture by emphasizing the strategic importance of human expertise and machine intelligence. It promotes a forward-looking mindset that ensures the organization remains adaptable and primed for ongoing advancement without needing large-scale cultural shifts.

Data Models, Architectures, Workflows, and Management Processes/Policies

Data models, architectures, workflows, and management processes are designed to evolve continuously, supporting seamless integration of new data sources, formats, and CI capabilities. Systems autonomously manage data ingestion, processing, and storage, optimizing data flows to support operational and strategic objectives. These systems monitor data usage patterns, assess data quality, and adapt workflows in real time to ensure optimal accessibility and performance.

Significant architectural changes or data policy updates are proposed for human review, ensuring alignment with broader organizational goals and regulatory requirements. Human oversight focuses on long-term data strategy and high-level data governance, while CI systems autonomously handle daily data management tasks and incremental improvements. This continuous adaptation ensures the organization’s data infrastructure remains agile, secure, and capable of supporting future CI needs without requiring large-scale restructuring.

Computing and Cybersecurity Infrastructure

The organization’s computing and cybersecurity infrastructure is designed for continual self-assessment and adaptive scaling, ensuring secure and efficient support for autonomous and collaborative CI functions. Systems monitor infrastructure performance, identifying capacity needs, potential vulnerabilities, and areas for optimization. Routine adjustments, such as resource allocation and minor security patches, are handled autonomously to maintain peak efficiency and resilience.

For more extensive infrastructure upgrades or significant security enhancements, the CI systems provide data-driven recommendations that human IT leaders review to ensure strategic alignment and compliance. Human oversight is focused on high-level cybersecurity planning and long-term infrastructure strategy, while systems autonomously manage real-time monitoring, response, and minor infrastructure adjustments. This approach allows the infrastructure to remain robust, scalable, and responsive to evolving CI demands without requiring major overhauls.

The Collaborative Intelligence Capability Maturity Model (CI-CMM) provides a practical framework for organizations adopting collaborative intelligence (CI), guiding them from initial exploration to continuously adaptive CI-driven enterprises. As organizations advance through each maturity level, they systematically integrate CI technologies, refine processes, and realign roles to support efficient, data-driven decision-making and optimized human-machine collaboration. By following the CMM, businesses address the specific challenges of CI integration at each stage, building a stable foundation for future growth.

Reaching Maturity Level 6—the "Renewing" stage—means that an organization has achieved a state of ongoing adaptability, with mechanisms in place to assess and integrate new advancements as they become relevant. This eliminates the need for disruptive transformations, allowing the organization to remain responsive to change and sustain its competitive edge. The CMM thus serves as both a roadmap and a continuous improvement strategy, helping organizations move confidently toward a future of resilience and sustained innovation.

Collaborative Intelligence is a Transformativ, LLC publication. If you’d like to learn more about how to become an AI-powered enterprise, please contact us here.

In a previous article, I introduced a capability maturity model for organizational adoption and use of AI and other collaborative technologies (aka “collaborative intelligence” or CI).

This article focuses on Maturity Level 5, named “Transformed.” It will:

1. provide more detail about the observable characteristics at this level,

2. present strategies and actions that will enable organizations to move to Maturity Level 6.

The final article in this series will focus on Maturity Level 6, after which I will provide an assessment tool that helps organizations determine their current maturity level.

When organizations have been “optimized” for CI (Level 4), the transformation into an AI-enabled organization (Level 5) is not too far away. Thus, several of the organizational characteristics of companies at Level 5 will be the same or very similar to those at Level 4. The main distinction is the use of autonomous systems across the organization.

Characteristics of Organizations with "Transformed" Collaborative Intelligence

Organizational Awareness/Knowledge of CI Technologies

At Maturity Level 5, awareness of CI technologies is embedded throughout the organization. Leaders, teams, and employees maintain an in-depth, evolving understanding of the latest CI advancements, focusing on how these technologies can directly contribute to strategic goals.

Employees actively engage in CI learning programs, with CI technologies autonomously identifying and recommending relevant training based on role-specific needs. This continuous knowledge reinforcement ensures that all levels of the organization are equipped to recognize and harness new CI developments as they emerge. At the same time, human leaders oversee the strategic alignment of these learning initiatives with long-term organizational objectives.

CI-Related Skills/Experience and CI-Specific Skills Management Processes

CI-related skills are deeply integrated into talent management processes, ensuring that new and existing employees develop and refine skills critical to collaborative intelligence. The organization has a dynamic skills management system where CI technologies autonomously assess skill gaps, suggest personalized development paths, and assign learning tasks.

Human talent managers focus on strategic oversight and long-term skill requirements, allowing CI systems to track day-to-day development and skills. This framework ensures a continuously evolving workforce with skills aligned with current CI capabilities and emerging technologies.

Influence of CI on Business Strategy

CI is foundational to the organization’s strategic direction. Autonomous CI systems continuously analyze market trends, operational data, and competitor activities, providing strategic insights that inform executive decision-making and long-term planning.

Human leaders collaborate with these CI systems, using their insights to refine and adapt strategies dynamically. CI systems can execute routine strategic adjustments autonomously, while human oversight remains focused on high-level strategic direction and innovation, ensuring that the organization stays agile and ahead in a competitive environment.

CI Technologies Deployed

At Maturity Level 5, CI technologies are omnipresent, embedded across all critical business functions, and capable of autonomously driving key decisions. These systems analyze real-time data, independently making and executing routine decisions, including task assignments and performance optimizations.

Human roles are redefined to focus on strategic oversight, complex decision-making, and addressing ethical considerations, while CI systems seamlessly manage operational tasks. This integrated deployment of CI technologies enables the organization to scale efficiently and remain responsive to changes in the business environment.

CI-Enabled Business Processes, Job Designs, and Team Structures

Business processes, job designs, and team structures are fully transformed to enable seamless human-machine collaboration and machine autonomy. Processes are designed to allow CI systems to independently handle routine tasks, analyze complex data, and make real-time decisions, which they execute without human intervention. In this structure, human roles focus on high-level oversight, strategic planning, and handling unique situations that require judgment, creativity, or ethical considerations.

Team structures adapt dynamically to CI capabilities, allowing machines to autonomously assign tasks and provide updates directly to human counterparts as needed. Human workers and CI systems collaborate through established protocols that optimize each other's strengths, fostering an agile work environment where CI and human agents continuously support each other’s tasks for greater productivity and innovation.

CI-Enabled or Embedded Products/Services

The organization’s products and services are fully CI-enabled, with embedded intelligence and autonomous capabilities that enhance value for end-users and the organization. These products and services leverage CI to continuously adapt to real-time user behaviors, environment changes, and operational data. They respond to inputs and proactively optimize performance, predict user needs, and offer dynamic features that evolve based on usage patterns and contextual insights.

Human oversight focuses on strategic direction and innovation, while CI systems autonomously handle routine updates, error detection, and performance enhancements. This allows the organization to deliver high-value, adaptive products and services that distinguish them in the market by continually learning and improving without requiring significant manual intervention.

CI-Aware Management Process/Metrics

At Maturity Level 5, management processes and metrics are fully adapted to a CI-driven organization. Metrics track both human and CI system performance, emphasizing collaborative outcomes and autonomous system contributions. Key performance indicators (KPIs) assess CI’s impact on strategic objectives, operational efficiency, and decision-making quality, creating a clear view of CI’s organizational value.

Management processes leverage CI systems to autonomously analyze performance data, identify improvement areas, and suggest adjustments. These processes support a feedback loop where CI systems continuously optimize based on real-time metrics, minimizing the need for human intervention in routine evaluations. Human oversight remains focused on strategic decision-making and refining CI-enabled metrics as technologies evolve.

CI Governance Processes and Policies

CI governance is embedded across all organizational levels, supporting secure, ethical, and compliant CI operations. Governance policies cover autonomous decision-making capabilities, outlining protocols for accountability, transparency, and risk management. CI systems are monitored to ensure they operate within established guidelines, with automated mechanisms for detecting and addressing policy deviations.

The organization employs adaptive governance structures that evolve with CI advancements, ensuring policies remain relevant as new capabilities emerge. Human governance roles focus on overseeing policy refinement, while CI systems autonomously track compliance, manage routine audits, and flag significant issues for human review.

Organizational Culture

The organizational culture aligns with a CI-driven environment, fostering collaboration between human employees and autonomous systems. The culture values continuous learning, adaptability, and data-informed decision-making, encouraging employees to rely on CI insights while contributing strategic thinking and creativity. Employees view CI as essential partners in achieving organizational goals, with trust in autonomous systems for executing routine decisions and assigning tasks.

Cultural incentives promote innovation in human-CI collaboration, rewarding employees for identifying new applications of CI and improving human-machine workflows. Leadership reinforces this culture by actively demonstrating CI’s role in the organization’s success and supporting employees in their roles alongside CI systems.

Data Models, Architectures, Workflows, and Management Processes/Policies

At Maturity Level 5, data models, architectures, workflows, and management processes are fully integrated to support a CI-driven organization. Data architectures are designed for real-time data flows, enabling CI systems to autonomously access, process, and act on data as needed. These infrastructures support seamless collaboration between CI systems and human employees, ensuring data availability, accuracy, and relevance for automated decision-making and human analysis.

Workflows are adaptive, allowing CI systems to autonomously update data models based on new information and performance insights. Management processes oversee data quality, compliance, and security. Human oversight focuses on strategic improvements to data processes, while CI systems handle routine data management tasks, ensuring efficient, secure, and scalable CI operations.

Computing and Cybersecurity Infrastructure

The computing and cybersecurity infrastructure is fully equipped to support a CI-driven organization, enabling secure and efficient operations for both autonomous and collaborative CI systems. The highly scalable and resilient infrastructure allows CI systems to handle complex computations and large data sets in real-time while maintaining robust cybersecurity measures to protect sensitive data and ensure compliance.

Autonomous monitoring systems are in place to detect and respond to security threats without human intervention, and CI-driven diagnostic tools identify potential infrastructure improvements. Human oversight is primarily focused on strategic cybersecurity planning and infrastructure upgrades. At the same time, CI systems manage routine security monitoring and computing resource allocation, ensuring a secure and adaptable environment for all CI applications.

Key Strategies/Actions to Reach Maturity Level 6

At Maturity Level 5, organizations have transformed into CI-driven enterprises. The only remaining challenge is designing and implementing organizational mechanisms to ensure the organization stays at the forefront of collaborative intelligence advancements so that it can renew itself continually, eliminating the need for future transformations.

Organizations that want to move to Level 6 should take the following actions:

1. Establish Autonomous CI-Driven Innovation Hubs

2. Develop Strategic CI Alliance Networks

3. Establish Autonomous CI Resource Allocation Systems

4. Introduce Predictive CI Lifecycle Management Systems

5. Establish Embedded CI Feedback Loops for Strategic Insight

Establish CI Innovation Hubs

Create innovation hubs within the organization where teams can independently experiment with emerging CI technologies, techniques, and applications. These hubs would function as exploratory units that continuously test, assess and prototype new CI-driven processes, products, and services. Managerial oversight is only needed to evaluate successful prototypes and decide on their broader organizational implementation. This structure allows the organization to stay agile and ready to integrate the latest CI advancements at scale.

Develop Strategic CI Alliance Networks

Form alliances with external CI leaders, such as AI-focused research institutions, startups, and industry consortiums, to access and co-develop cutting-edge CI innovations. This goes beyond standard partnerships by creating a decentralized network of CI intelligence that operates autonomously and funnels relevant breakthroughs directly into the organization. Through these alliances, CI systems can autonomously evaluate and integrate external CI advancements in real time, enabling the organization to stay aligned with the frontier of CI innovation while minimizing reliance on internal resources alone.

Establish Autonomous CI Resource Allocation Systems

Develop CI-driven resource allocation systems that autonomously manage and optimize the organization’s investments in new CI technologies, R&D, and operational scaling. These systems would dynamically assess current CI capabilities, market demands, and potential technology improvements, redistributing resources in real time to areas with the highest projected impact. Human oversight focuses on strategic priorities and longer-term investments. CI systems autonomously handle resource adjustments to ensure funding, staffing, and infrastructure align with evolving CI objectives and technological advancements.

Introduce Predictive CI Lifecycle Management Systems

Create lifecycle management systems that anticipate the end-of-life and obsolescence of current CI technologies and autonomously initiate the replacement or upgrade process. These systems would analyze performance data, emerging technological trends, and operational impacts to predict when a CI system or technology will likely reach its limit. By autonomously initiating technology upgrades or replacements, the organization always operates with optimal CI tools and capabilities, eliminating downtime and reducing the need for reactive overhauls.

Establish Embedded CI Feedback Loops for Strategic Insight

To ensure ongoing alignment with the business environment, implement CI-driven feedback loops that autonomously gather and analyze data from market trends, user feedback, and competitive intelligence. These feedback loops provide strategic insights to the organization in real time, allowing CI systems to proactively adjust business strategies, product offerings, and operational processes. Human leaders can use this continuously refreshed intelligence to steer high-level strategy, while CI systems autonomously manage day-to-day adjustments based on feedback.

Collaborative Intelligence is a Transformativ, LLC publication. If you’d like to learn more about how to become an AI-powered enterprise, please contact us here.

Best business article(s) I read this week…

• Where’s the value in AI?

  This analysis from the Boston Consulting Group finds that “only 22% of companies have advanced beyond the [AI] proof-of-concept stage to generate some value, and only 4% are creating substantial value.” They categorize all companies studied as “not doing much”(25%), “still focusing on proof of concepts” (49%), “scaling value” (22%), and “operating value engines” (4%).

• How AI agents are reshaping the future of work

  This report from the Deloitte AI Institute describes how AI agents and multiagent systems are transforming work processes. Unlike generative AI, which primarily assists with single tasks, AI agents can autonomously manage entire workflows, use the experience to adapt over time, and connect with multiple digital tools to execute complex, multistep processes. Organizations using AI agents see benefits such as improved collaboration, faster decision-making, enhanced personalization, and significant scalability improvements. Key applications include automated customer support, personalized financial advising, dynamic pricing, and talent acquisition.

Best technical article(s) I read this week…

• GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

  The paper, referenced in the next section, describes the limitations of large language models (including OpenAI’s new o1 model) in performing genuine mathematical reasoning because current LLMs rely on pattern-matching rather than formal logical processing. This lack of reasoning leads to inconsistent results, especially as complexity increases, and reveals that LLMs struggle with tasks that require logical depth beyond surface-level associations.

Other items I found valuable/interesting…

• I Took a ‘Decision Holiday’ and Put AI in Charge of My Life

  The author of this New York Times article “used two dozen generative AI tools for daily tasks and nearly 100 decisions” for a week. “It told me what to eat, what to wear, and what to do with my kids. It chose my haircut and what color to paint my office.” She includes text chains, photos, and other details. I won’t spoil the ending, where she assesses what worked and what didn’t.

• These Apple researchers just showed that AI bots can’t think and possibly never will

  This Los Angeles Times article discusses an Apple research paper (see previous section) highlighting significant limitations in AI models' reasoning abilities. Apple researchers demonstrated that large language models (including OpenAI’s new o1 model) often fail to solve math problems reliably, especially when problems contain irrelevant details. These AI systems frequently treat this irrelevant information as significant, leading to incorrect answers. The paper casts doubt on whether LLMs can ever handle abstract problem-solving reliably and raises concerns about their use in applications where “empirically perfect calculations are necessary​.” (This quote is from an email to me from the article’s author.)

• Walmart and Amazon are rolling out AI shopping assistants for Holiday 2024

  Glimpse the future of online shopping experiences this holiday by using Walmart’s AI-driven shopping assistant (still in beta) or Amazon’s “Rufus,” which launched in Europe this week.

Coolest thing I saw…

• Robots powered by insect brains could be used on Mars

  Opteran, a company specializing in neuromorphic AI (inspired by insect brains), partnered with Airbus and space agencies to test its AI platform in Mars rover simulations. This technology aims to enable autonomous navigation in challenging Martian terrains, reducing reliance on human control.

A company that caught my eye…

• Beta Technologies

  It’s not quite flying cars, but they’re getting closer. This Vermont-based company, which makes small electric vertical take-off and landing (VTOL) planes, closed a $318M Series C round led by Qatar’s sovereign wealth fund. A spokesperson said the new money will move its aircraft closer to certification and commercialization.

Best business article(s) I read this week…

• How to Avoid Strategy Myopia

  This article, by best-selling author Seth Godin, summarizes key points from his latest book, This is Strategy: Make Better Plans. While the short headings below may not provide much guidance (you’ll have to read the article), they are:

  • Refuse false proxies.

  • Choose your customers, choose your future.

  • Choose your team.

  • Big problems demand small solutions.

Best technical article(s) I read this week…

• On the use of Large Language Models in Model-Driven Engineering

  Model-driven engineering (MDE) uses models to specify, analyze, and produce complex software systems. This paper describes how LLMs can be used to automate modeling tasks and develop and test software systems.

Other items I found valuable/interesting…

• The AI Hype Index

  MIT Technology Review has created a humorous look at the state of AI. It plots advancements on a 2x2 matrix ranging from “Doom” to “Utopia” (y-axis) and “Hype” to “Reality” (x-axis).

• With AI, Dead Celebrities Are Working Again—And Making Millions

  This article describes how companies are contracting with the estates of dead celebrities to license their voices or likenesses to create new offerings (such as Julie Garland narrating The Wonderful Wizard of Oz) using generative AI.

• The AI expert who says artificial general intelligence is nonsense

  This is an interview with Neil Lawrence, DeepMind Professor of Machine Learning at the University of Cambridge, about his new book The Atomic Human: Understanding Ourselves in the Age of AI. Lawrence makes the case that we can make the most of both by better understanding human intelligence and how wildly different it is from AI.

• Siemens USA CEO explains how digital twins work to optimize manufacturing

  In this interview with CNBC’s Jim Cramer, Siemens USA CEO Barbara Humpton describes how manufacturers can benefit from using digital twins to solve many manufacturing challenges before they “begin to bend metal.”

Coolest thing I saw…

• L-Ring 2

  I’m unsure whether this will work as advertised, so it may not be cool, but it is interesting. The ring is a smart wearable that uses GPT 4o to provide such things as Q&A and real-time translation. It has a 3-axis gyroscope and accelerometer that can interpret 16 gestures, so you can use it as a mouse, among other things.

A company that caught my eye…

• Newcleo
  The AI revolution needs power. London-based Newcleo hopes to provide it. The company, which is focused on developing advanced nuclear energy technologies, raised nearly $148 million in a Series A round.

Best business article(s) I read this week…

• Generative AI: tech du-jour or the next big thing?

  This is a link to a recording of a talk by AWS Product Management Director Massimo Re Ferre, recorded on May 18, 2024. It’s a pragmatic discussion of the business potential of generative AI and a warning to avoid what he terms “the danger zone.”

Best technical article(s) I read this week…

• Know Your Data to Harness Federated Machine Learning

  This article covers a technical topic, but it is written for businesspeople. It talks about strategies for improving results from AI models by augmenting the model’s training data with complementary third-party data while keeping your data private.

Other items I found valuable/interesting…

• Harnessing Generative AI for Competitive Edge in Financial Services

  The article presents five ways financial services firms can deploy generative AI to improve customer experience and operational effectiveness. They are:

  • Improving operational efficiency

  • Improving customer onboarding

  • Improving customer engagement

  • Helping customers and insiders better understand analytic insights

  • Enhancing security and compliance

• Drones “Game Changing” for Aramco Shell, Chevron & bp

  This article describes how energy companies are deploying autonomous drones to improve operational efficiency while also making them safer and more sustainable.

• How MathGPT Uses Generative AI To Make Mathematics More Accessible To Everyone

  MathGPT is an AI-driven platform designed to make math more accessible by offering step-by-step solutions and personalized tutoring, helping students from high school to college better understand complex concepts. It provides instant, clear explanations of math problems while focusing on building a strong conceptual foundation rather than just giving answers. It’s a great example of how generative AI enables personalized learning and how it will ultimately transform the education market.

Coolest thing I saw…

• L2F Middleby Pizza Robot

  My friend John Gaston invited me to the Grand Reopening of Sam’s Club in Grapevine, Texas. John led the expansion, redesign, and rebuild after a tornado hit the store in 2022. The company decided to transform this location into its first all-digital store. Many cool technologies were on display, including a pizza robot capable of making 100 pizzas per hour (when paired with Middeby’s TurboChef rapid-cook ovens). Varun Rallabandi, a development team member, gave me a demo. Unfortunately, my video was ruined by the plastic glove I was wearing for food safety. Unbeknownst to me, it was covering 70% of the lens. You’ll have to click on the link to see a demo.

A company that caught my eye…

• Lightmatter

  The company makes 3D stacked photonic chips for AI applications. It recently completed a $400M Series D round, led by T. Rowe Price, quadrupling its value to $4.4B.

In a previous article, I introduced a capability maturity model for organizational adoption and use of AI and other collaborative technologies (aka “collaborative intelligence” or CI).

This article focuses on Maturity Level 4, named “Optimized.” It will:

1. provide more detail about the observable characteristics at this level,

2. present strategies and actions that will enable organizations to move to Maturity Level 5.

Subsequent articles will focus on Maturity Levels 5-6. When this series of articles concludes, I will provide an assessment tool that helps organizations determine their current maturity level.

TL;DR (written by ChatGPT-4o with light edits)

At Maturity Level 4 (Optimized), organizations have implemented and optimized Collaborative Intelligence (CI) across various business functions, including processes, governance, and products. CI technologies are effectively used to enhance decision-making, operational efficiency, and competitive positioning. The organizational culture supports continuous improvement and collaboration between humans and machines. However, businesses at this level are not yet wholly reliant on autonomous, self-learning systems.

To reach Maturity Level 5 (Transformed), organizations should focus on advancing four in key areas:

• Adopt autonomous decision-making systems to automate critical business functions.

• Enhance governance with AI-driven, self-regulating frameworks that ensure compliance and mitigate risks without human intervention.

• Develop self-learning, scalable CI platforms that continuously improve and adapt to changing demands with minimal oversight.

• Cultivate a human-machine symbiosis where employees and intelligent systems collaborate seamlessly.

These steps will transform organizations into CI-driven enterprises that leverage machine intelligence for strategic innovation, greater agility, and enhanced market leadership.

Characteristics of Organizations with "Optimized" Collaborative Intelligence

Organizational Awareness/Knowledge of CI Technologies

At Maturity Level 4, the organization possesses a comprehensive and in-depth understanding of CI technologies—from senior leadership to frontline employees. This advanced awareness goes beyond basic familiarity; leaders and staff know how various CI technologies can be strategically applied to optimize business processes and outcomes.

The organization has established robust training programs and knowledge-sharing platforms to ensure that awareness of CI technologies is continuously updated and disseminated across all departments. Employees are encouraged to stay informed about the latest advancements in CI, fostering a culture of continuous learning and innovation.

This optimized organizational awareness enables the company to proactively identify and assess opportunities where CI technologies can drive efficiency, enhance decision-making, and create competitive advantages. The collective knowledge allows for more informed and strategic implementation of CI solutions, aligning closely with business objectives.

By fully optimizing their awareness and knowledge of CI technologies, the organization can maximize the benefits of CI integration, effectively enhancing operations and performance.

CI-Related Skills/Experience and CI-Specific Skills Management Processes

The organization has fully optimized its CI-related skills and experience across all levels. Employees possess deep expertise in CI technologies, and this knowledge is widespread rather than confined to specific teams or individuals.

Comprehensive training programs are well-established and regularly updated to reflect the latest advancements in CI, ensuring that staff skills remain current and relevant.

The organization has institutionalized formal skills management processes, promptly identifying and addressing skill gaps. Regular assessments, personalized development plans, and clear career progression pathways are in place for CI-related roles. CI competencies are integrated into performance evaluations, reinforcing the importance of these skills to the organization's success.

A culture of collaboration and knowledge sharing is deeply ingrained, with cross-functional teams working together to leverage CI technologies effectively. Best practices and insights are routinely shared across departments, enhancing collective expertise and fostering innovation.

The organization maximizes the benefits of CI technologies by optimizing its CI-related skills and systematically managing them. This enables more efficient design and implementation of CI-enabled workflows, tasks, and jobs, enhancing overall performance.

Influence of CI on Business Strategy

The organization has integrated Collaborative Intelligence into its corporate and business unit strategies and optimized its strategic influence for maximum effectiveness. CI is deeply embedded in all strategic planning processes, with a refined focus on aligning CI initiatives closely with long-term business objectives and competitive positioning.

The organization employs advanced analytics and feedback mechanisms to continuously assess the impact of CI on strategic goals. This allows for real-time adjustments and enhancements, ensuring CI technologies are leveraged to their fullest potential in achieving desired outcomes. Strategic plans are regularly reviewed and updated to incorporate the latest advancements in CI, reflecting a commitment to staying at the forefront of technological innovation.

Senior leadership actively promotes a culture where CI is considered an integral component of the organization's strategic framework. This holistic approach enables the organization to identify synergies between different CI initiatives, optimize resource allocation, and maximize return on investment.

By fully optimizing CI's influence on its business strategy, the organization enhances operational efficiencies, creates innovative value propositions, and explores transformative business models. This strategic optimization positions the company to achieve sustained competitive advantages and better respond to market dynamics.

CI Technologies Deployed

At Maturity Level 4, the organization has optimized its deployment of CI technologies across the enterprise. Building on the integrated foundation of Level 3, the company now focuses on enhancing the performance and effectiveness of these technologies to achieve maximum value.

CI technologies are embedded in key business processes and continuously monitored and fine-tuned using advanced analytics and performance metrics. The organization has established standardized processes and best practices for CI deployment, ensuring consistency and quality across all applications. A robust management infrastructure supports these efforts, facilitating proactive maintenance, updates, and improvements to the CI systems.

Teams regularly review and optimize CI applications, leveraging real-time data to make informed adjustments that enhance efficiency and effectiveness. The organization stays abreast of the latest advancements in CI technologies, incorporating new features and capabilities that align with strategic objectives.

By optimizing its CI technologies, the organization achieves significant gains in operational efficiency, decision-making, and customer satisfaction. The focus is maximizing the return on investment from CI deployments, ensuring they contribute effectively to business goals.

CI-Enabled Business Processes, Job Designs, and Team Structures

The organization has optimized its CI-enabled business processes, job designs, and team structures. Building upon the integration achieved at Level 3, the company now focuses on enhancing efficiency, effectiveness, and adaptability in these areas to maximize the benefits of Collaborative Intelligence.

Business processes are continuously refined using data-driven insights and feedback mechanisms. The organization employs standardized methodologies for process optimization, ensuring that CI technologies are utilized to their fullest potential across all operations. Best practices are documented and shared widely, promoting consistency and excellence.

Job roles are further adjusted to align with the optimized processes, enabling seamless collaboration between employees and CI technologies. Ongoing training and support help staff adapt to their evolving roles, emphasizing skill development in areas that complement CI capabilities. This ensures that human expertise and machine intelligence are effectively integrated, enhancing overall performance.

Team structures are optimized to foster greater collaboration, innovation, and responsiveness. Cross-functional teams are common and strategically organized to leverage CI technologies effectively. Communication channels and collaboration tools are enhanced to support efficient information flow and agile decision-making.

By fully optimizing its CI-enabled business processes, job designs, and team structures, the organization significantly improves productivity, quality, and agility, enabling it to extract maximum value from its CI investments.

CI-Enabled or Embedded Products/Services

The organization has optimized its products and services to maximize their effectiveness and market impact. Building upon the integrated foundation of Level 3, the company now focuses on continuous improvement and refinement of its CI-enhanced offerings.

Product development teams utilize advanced analytics and customer feedback to iteratively enhance CI features, ensuring they deliver superior value and align with evolving customer needs. The organization has established standardized processes and best practices for developing and maintaining CI-enabled products and services, promoting consistency and high quality across its portfolio.

Cross-functional collaboration is deeply ingrained, with product managers, engineers, data scientists, and customer service teams working closely to optimize CI capabilities within offerings. The company actively monitors market trends and technological advancements in CI, rapidly incorporating relevant innovations to stay ahead of competitors.

By optimizing its CI-enabled products and services, the organization achieves significant improvements in customer satisfaction, product performance, and market share.

CI-Aware Management Process/Metrics

At Maturity Level 4, the organization has optimized its management processes and metrics to effectively support and enhance the integration of CI technologies. Building upon the changes made at Level 3, the company now employs standardized and refined management practices specifically designed to maximize the benefits of human-machine collaboration.

Performance management systems incentivize the effective use of CI tools. Metrics are expanded to capture the nuanced contributions of CI to individual, team, and organizational performance. These metrics provide deeper insights into how CI technologies enhance productivity, innovation, and efficiency, enabling more informed decision-making at all levels of management.

Management processes are proactive and data-driven, utilizing real-time analytics to monitor the effectiveness of CI-enabled workflows and collaborations. Feedback loops are established to continually assess and adjust strategies, ensuring that human and machine resources are aligned with business objectives. Training and development programs are closely tied to performance metrics, promoting a culture of continuous improvement and learning.

By optimizing its management processes and metrics, the organization enhances its ability to leverage CI technologies effectively. This optimization improves operational efficiency and decision-making and strengthens the organization's competitive position.

CI Governance Processes and Policies

The organization has optimized its CI governance processes and policies to support responsible use and technological innovation. Building upon the approaches established at Level 3, the company now employs comprehensive and standardized governance frameworks across all functions.

Governance policies are regularly reviewed and updated to keep pace with technological advancements and evolving regulatory landscapes. The organization has established clear, actionable guidelines that cover a wide range of CI technologies, not just generative AI. These guidelines are designed to be flexible yet robust, allowing for swift adaptation while maintaining legal compliance standards, adhering to ethical frameworks, and managing risk effectively.

By optimizing its CI governance processes and policies, the organization ensures that it can leverage CI technologies effectively and responsibly. This enhances its ability to innovate and respond quickly to market changes, maximizing the benefits of CI while minimizing potential risks.

Organizational Culture

The organization has optimized its culture to support and enhance the effective use of Collaborative Intelligence. The culture now embeds values and behaviors that maximize the CI’s benefits.

The culture actively encourages continuous improvement, innovation, and adaptability, recognizing these as essential for effectively leveraging CI. Employees at all levels are empowered to experiment with CI technologies, share insights, and collaborate openly, fostering an environment of collective learning and growth. Human-machine collaboration is seamlessly integrated into daily workflows, strongly emphasizing using CI to achieve strategic objectives.

Leadership reinforces this culture through consistent communication, recognition programs, and modeling the desired behaviors. The values of collaboration, agility, experimentation, and continuous learning are ingrained and consistently promoted across the organization. Feedback mechanisms are established to assess cultural alignment with CI initiatives, allowing for proactive adjustments when needed.

This strong organizational culture enhances the organization’s ability to leverage CI technologies effectively, driving innovation, efficiency, and competitive advantage.

Data Models, Architectures, Workflows, and Management Processes/Policies

At Maturity Level 4, the organization has optimized its data models, architectures, workflows, and management processes/policies to maximize the effectiveness of CI technologies. Building upon the integration established at Level 3, the company now focuses on enhancing its data infrastructure's efficiency, scalability, and adaptability.

Data models are thoroughly standardized and refined, ensuring consistency and compatibility across all systems and platforms. Advanced data modeling techniques accommodate various data types, including structured, semi-structured, unstructured, binary, and streaming data. This comprehensive approach allows for seamless integration and utilization of diverse data sources, enhancing the quality and accessibility of data for CI applications.

Data architectures are optimized for high performance and scalability, utilizing cutting-edge technologies and best practices. The organization implements robust data pipelines and real-time data processing capabilities, enabling swift and efficient data flow from collection to analysis. This optimized architecture supports the demanding requirements of CI technologies, facilitating real-time analytics and predictive modeling.

Data workflows are fully automated and streamlined, minimizing manual intervention and reducing potential errors. Advanced workflow management tools and techniques are adopted to ensure that data processing is efficient, reliable, and adaptable to changing needs. These optimized workflows enhance the organization's ability to rapidly develop and deploy CI solutions across various domains.

Management processes and policies are institutionalized and continuously improved. Comprehensive data governance frameworks are in place to ensure data quality, consistency, security, and compliance with relevant regulations. Policies are regularly reviewed and updated to align with evolving technological capabilities and business objectives, promoting a proactive approach to data management.

By fully optimizing its data models, architectures, workflows, and management processes/policies, the organization maximizes the benefits of CI technologies. This optimization significantly improves operational efficiency and decision-making while strengthening competitive advantage.

Computing and Cybersecurity Infrastructure

The organization has optimized its computing and cybersecurity infrastructure to support the advanced demands of CI technologies. The computing infrastructure is now highly scalable and flexible, efficiently handling complex CI applications and CI-driven products or services across the enterprise.

The organization employs cutting-edge computing resources, such as high-performance computing clusters, cloud-based platforms, and edge computing solutions, to ensure optimal performance and responsiveness of CI systems. Resource allocation is dynamically managed to meet the fluctuating demands of CI workloads, maximizing efficiency and minimizing downtime.

Cybersecurity measures are deeply integrated and proactive, providing comprehensive protection against sophisticated threats that specifically target CI technologies. Advanced security protocols safeguard all data types, including non-traditional and sensitive data used in CI applications. The organization utilizes AI-driven cybersecurity tools for real-time threat detection, predictive analytics for threat anticipation, and automated incident response mechanisms to mitigate risks quickly.

Security practices are standardized and institutionalized across all CI applications and systems. Regular security audits, vulnerability assessments, and penetration testing are conducted to ensure the integrity and resilience of the CI infrastructure. Encryption is applied universally to data at rest and in transit, and secure coding practices are rigorously followed in CI system development.

Cybersecurity policies and procedures are continuously updated to address emerging threats and comply with evolving regulations. Employees receive ongoing training tailored to the specific security considerations of CI technologies, fostering a security-aware culture throughout the organization.

By fully optimizing its computing and cybersecurity infrastructure, the organization ensures that its CI technologies operate at peak performance while maintaining robust security. This optimization enables the company to leverage CI effectively and efficiently, enhancing its operational capabilities.

Key Strategies/Actions to Reach Maturity Level 5

At Maturity Level 4, organizations have optimized their use of CI. However, these organizations do not rely on autonomous, self-learning technologies, so they have yet to transform into CI-driven enterprises.

Organizations that want to move to Level 5 (and beyond) should take the following actions:

1. Embrace Autonomous Decision-Making Across Functions

2. Build AI-Enhanced Governance and Self-Regulation Frameworks

3. Develop and Deploy Self-Learning, Scalable CI Systems

4. Foster Human-Machine Symbiosis in Organizational Culture

Embrace Autonomous Decision-Making Across Functions

Organizations should develop/deploy autonomous machine intelligence-driven systems that assume responsibility for decision-making in key business areas, moving beyond supporting tools to fully self-sufficient entities. This means deploying CI technologies that manage tasks, optimize processes, and make real-time adjustments without human intervention (although humans still monitor/validate them and implement the decisions).

Key Implementation Steps:

1. Identify workflows wherein decisions can be automated.

2. Develop autonomous systems that use predictive analytics and real-time data to make decisions independently.

3. Establish feedback loops where human oversight shifts from controlling the system to monitoring and auditing the machine’s performance.

Build AI-Enhanced Governance and Self-Regulation Frameworks

Organizations should design and implement AI-driven governance frameworks that regulate, assess, and ensure CI systems' compliance, ethics, and safety with human oversight. These systems should be capable of self-regulation—identifying risks, correcting biases, and autonomously adapting to new policies or regulations.

Key Implementation Steps:

1. Implement real-time governance tools that utilize machine learning models to assess risks, compliance, and ethical considerations.

2. Create self-regulating systems that flag and address issues such as algorithmic bias, privacy concerns, and regulatory violations.

3. Establish frameworks where governance policies and compliance updates are automatically implemented without manual intervention (but are still monitored by humans).

Develop and Deploy Self-Learning, Scalable CI Systems

Organizations should shift from optimizing CI systems to developing self-learning, autonomous CI platforms that continuously improve their capabilities, requiring minimal human intervention. These systems should be capable of scaling on demand, updating themselves, and refining workflows or processes as they learn from real-time data.

Key Implementation Steps:

1. Invest in self-learning CI technologies that utilize advanced neural networks, reinforcement learning, or other machine learning models to adjust and improve over time without human reprogramming.

2. Deploy scalable infrastructure that dynamically adjusts to business needs, allocating computing and data resources automatically based on real-time demands.

3. Integrate systems capable of auto-updating and self-healing, where AI can address inefficiencies, maintain performance, and autonomously deploy new features (specified by product managers or gathered via customer feedback).

Foster Human-Machine Symbiosis in Organizational Culture

Develop a culture that embraces AI as an equal partner by fostering a deep understanding and trust in CI technologies across all organizational levels. Employees should actively collaborate with intelligent machines, with both parties contributing to innovation and decision-making processes.

Key Implementation Steps:

1. Establish training programs to teach employees to work alongside autonomous systems, focusing on AI oversight and decision augmentation rather than manual task management.

2. Encourage an experimental mindset where employees rely on CI insights to solve problems and continuously optimize their roles.

3. Develop incentives that reward effective human-machine collaboration, making CI central to achieving goals and pursuing innovations.

Implementing these strategies will enable companies to transition from Maturity Level 4 to Maturity Level 5 by allowing Collaborative Intelligence technologies to take the lead in many areas of decision-making, governance, and operational performance while fostering a culture that trusts and leverages autonomous systems' capabilities.

Collaborative Intelligence is a Transformativ, LLC publication. If you’d like to learn more about how to become an AI-powered enterprise, please contact us here.

Best business article(s) I read this week…

• Product leadership in the age of AI

  This is a McKinsey & Company interview with Inbal Shani, chief product officer at Twilio, wherein she discusses the challenges of introducing AI into products, its impact on customer experience, and how to deal with the hype surrounding AI.

• Companies had fun experimenting with AI; now they have to show the returns

  This article summarizes discussions held at the Wall Street Journal’s CIO Network Summit about what technology leaders have learned about AI and the need for AI projects to begin creating value.

Best technical article(s) I read this week…

• Measuring Human Contribution in AI-Assisted Content Generation

  This article presents a new framework for measuring human contributions in AI-assisted content generation—an increasingly relevant issue in performance measurement. The authors’ framework is grounded in information theory. It quantifies human contribution by comparing the mutual information between human input and AI-generated content with the self-information of the AI output. Through experiments across multiple creative domains, the study demonstrates that this method effectively distinguishes varying levels of human input in AI-assisted outputs.

Other items I found valuable/interesting…

• Geoffrey Hinton and John Hopfield win the Nobel Prize in Physics for their work in Machine Learning

  The scientists applied tools from physics to train artificial neural networks to recognize patterns in large data sets, making machine learning tasks like facial recognition and language translation possible.

• DeepMind's Demis Hassabis and John Jumper win the Nobel Prize in Chemistry for AlphaFold

  The DeepMind team was awarded their share of the prize for their work in predicting protein structures in 3D (using AlphaFold). The duo shared their prize with David Baker for his work in computational protein design (computational intelligence is another Collaborative Intelligence technology).

• How digital innovation is reshaping healthcare in the Middle East

  This World Economic Forum article provides an overview of how health systems in the Middle East are using many technologies, including Digital Twins and Artificial Intelligence, to improve (transform) healthcare in the region.

Coolest thing I saw…

• SpaceX Super Heavy Booster’s Successful Test Launch/Return to Earth

  This isn’t about AI, but space travel is cool. SpaceX released photos and a video of its launch from its Starbase facility in Texas. The rocket was fired, and seven minutes later, it landed back on Earth (with the help of a mechanical arm).

A company that caught my eye…

• InFactory

  The company, which fact-checks generative AI, recently closed a $4M seed round. It caught my eye because I have long been a proponent of finding opportunities by offering valuable complementary services within a business ecosystem centered on a rapidly growing platform (in this case, generative AI).

Please note that beginning with this issue, the publication day of this newsletter changes from Friday to Monday and includes items published on the weekend.

Best business article(s) I read this week…

• How Machine Learning Will Transform Supply Chain Management

  The article discusses how ML can improve supply chain management by addressing traditional planning challenges such as inaccurate demand forecasts and inefficient inventory optimization. It introduces a new approach called Optimal Machine Learning (OML) that uses historical data and supply chain inputs to make decisions rather than just improving forecasts. The OML model combines a decision-support engine, a digital twin, and an end-to-end data architecture to improve agility and resilience in supply chains. The authors argue that this approach can help companies reduce costs, increase efficiency, and enhance customer satisfaction by optimizing inventory and managing disruptions more effectively.

Best technical article(s) I read this week…

• Intelligence at the Edge of Chaos

  This article may not appeal to many. It appealed to me because I have extensively researched the application of chaos and complexity theory to strategic management. The paper describes how complex rule-based systems can foster the emergence of intelligent behavior in artificial models, specifically through training large language models (LLMs) on elementary cellular automata (ECA). The study shows that as the complexity of the ECA rules increases, the models exhibit better performance on reasoning and prediction tasks, suggesting that complexity is essential for developing intelligence. However, overly chaotic systems hinder performance, highlighting a "sweet spot" of complexity—referred to as the "edge of chaos"—most conducive to intelligence. The findings indicate that exposing models to structured yet unpredictable systems can help generate sophisticated, generalizable behaviors without requiring inherently intelligent data.

Other items I found valuable/interesting…

• Microsoft’s AI Story Is Getting Complicated

  This WSJ article describes the challenges Microsoft faces in maintaining its lead in AI. For example, Microsoft spent $55.7 billion (23% of revenue) in the recently ended fiscal year on capital investments and leases, much of which supported AI. This is up from 14% of revenue just five years ago. This level of expenditure affects other financial measures, such as free cash flow (FCF). Investors and analysts are trying to gauge the long-term impact of increased investment on the company’s financial performance.

• AI assistants are ratting you out for badmouthing your coworkers

  I included this as a “buyer beware” piece. It describes how AI assistants capturing and transcribing online meetings continue to record the meeting as long as it is active. It describes several instances where people who had left a meeting got a transcript that included co-workers or prospective business partners bad-mouthing them after they left. Remember: your AI assistant hears and records everything.

• Why the OpenAI-to-Anthropic pipeline remains so strong

  This article details the history of Anthropic (started by former OpenAI employees) and describes why so many people from OpenAI (co-founders, executives, and staff) move to Anthropic.

• National Science Foundation Funds Development of Digital Twins for Healthcare

  The NSF grants will fund seven projects, including the development of models for virtual clinical trials of cardiovascular medical devices, digital twin-based studies of neurodegenerative diseases, and AI-informed decision-making linked to glucose metabolism.

Coolest thing I saw…

• Clio

  MIT researchers have developed a new system that helps robots quickly map a scene and identify items they need to complete tasks (such as “get a first aid kit” or “move a rack of magazines”). There’s no demo, but the description and their approach are cool.

A company that caught my eye…

• Augmented Intelligence

  The company recently partnered with Google to accelerate the deployment of AI agents built on its “neuro-symbolic architecture.” The company claims its new architecture combines generative AI’s conversational skills with the predictability and traceability of rule-based AI.

Best business article(s) I read this week…

• AI Can (Mostly) Outperform Human CEOs

  The authors note a running joke that companies wanting to use AI to bring down costs should start by replacing their CEOs. The researchers decided to see how that might work out. As you can guess from the title, they found that generative AI was much than humans on most strategic tasks. However, it had one fatal flaw: it did not respond well to novel (or “black swan”) events, such as coping with the Covid-19 pandemic.

Best technical article(s) I read this week…

• Turn Every Application into an Agent

  The authors describe their AXIS framework, which makes software applications (like Microsoft Word) act like intelligent agents by utilizing APIs rather than traditional user interfaces (UI). According to the authors, “AXIS reduces task completion time by 65%-70% and cognitive workload by 38%-53% while maintaining an accuracy of 97%-98% compared to humans.”

Other items I found valuable/interesting…

• Onboarding the AI workforce: How digital agents will redefine work itself

  This article describes how AI agents are transforming the workplace and projects what things will be like in 2030. According to the author, digital agents will take on complex tasks, provide real-time insights, and participate in decision-making, reshaping how businesses operate. AI-driven digital humans will improve customer interactions with human-like empathy, while AI's role in creativity and leadership will push innovation. The future workplace will rely on human-AI collaboration, emphasizing reskilling, ethical AI use, and the balance between human oversight and automation to unlock new opportunities.

• AI at Work: Why GenAI is more likely to support workers than replace them

  This recent study by Indeed examined more than 2,800 job skills to see which were most susceptible to being replaced by GenAI. They concluded that “Despite rapid advances in generative artificial intelligence (GenAI) technology and fears of an impending mass displacement of human workers, human work skills won’t be easily replaced in the workforce any time soon.”

• AI Model Is Better at Pricing Currencies Than Humans, ING Says

  Perhaps Indeed’s analysis is too optimistic: ING reports that a new currency trading bot outperforms human traders. They have announced plans for expansion of AI into other trading domains.

• Seeing double: The heart of digital twins

  Follow the link to a video describing how Japanese researchers are using digital twins of human hearts to save the lives of Japanese children born with heart problems.

Coolest thing I saw…

• The world’s first AI-driven autonomous subsea inspection vehicle

A company that caught my eye…

• OpenAI

  OpenAI is becoming a for-profit company. This article answers several key questions regarding the move. The company is seeking a $6.5 billion investment round at a $150 billion valuation.

Best business article(s) I read this week…

• How Your CEO Is Thinking About AI

  A recent survey from Gartner found that 86% of CEOs think AI can help their companies grow their revenue this year and next. Unfortunately, they are not clear about how best to achieve this goal. I have a few ideas. Contact me.

Best technical article(s) I read this week…

• Can LLMs Generate Novel Research Ideas?

  This article explores whether LLMs can generate original, expert-level research ideas. A study involving over 100 researchers compared AI-generated ideas with human ones, finding that AI-generated ideas were judged more novel but slightly less feasible. The study highlights the strengths and weaknesses of LLMs in ideation.

Other items I found valuable/interesting…

• Scientists Make ‘Cyborg Worms’ with a Brain Guided by AI

  Scientists have developed "cyborg worms" by connecting AI to the nervous systems of tiny worms, guiding them toward food using AI-directed light stimulation. This method allowed the worms and AI to collaborate in navigating obstacles. The broader implications of this technology extend to potential applications in medical fields, such as enhancing deep-brain stimulation treatments for conditions like Parkinson’s. This breakthrough showcases how AI and biological systems could work together, opening up possibilities for advanced brain-machine interfaces and medical interventions.

• The Objects of Our Life

  This web page is about Steve Jobs's talk at the 1983 International Design Conference in Aspen, CO (it includes a video of the talk). The Steve Jobs Archive recently released it. In his talk, Jobs made several prescient predictions about the impact of computers on our lives and hinted at the role AI would play in the future. It’s worth a look.

• Generative AI’s Disruption: Is This Time Really Different?

  In a podcast with EconoFact, economist Michael Strain discusses the impact of generative AI on the workforce. He argues that while technology may displace some jobs, it also creates new roles and opportunities, similar to past technological advancements. Strain emphasizes that AI will likely change the nature of jobs rather than eliminate them. He points out how historical patterns of innovation have led to new tasks, increased productivity, and higher wages, suggesting that AI will similarly transform the economy without catastrophic job losses.

• Google’s Gemini AI demo video was fabricated

  Google’s recently released promotional video for Gemini shows it recognizing visual cues and interacting vocally with a person in real time. After being questioned by AI experts who used Gemini, Google admitted that its researchers had fed still images to the model and edited successful responses together, misrepresenting the model’s capabilities.

• The Brennan Center for Justice Creates an AI Legislation Tracker

  According to the Brennan Center for Justice, more than 120 pieces of AI-related legislation are currently being considered by Congress, everything from restricting its use to creating a regulatory authority to oversee AI. Use this tracker to keep up with what’s happening.

Coolest thing I saw…

• Google’s GameNGen

  GameNGen uses diffusion models to simulate human game playing (in this case, DOOM), collects the simulation data, and uses it to train another generative model to create the game. Beyond gaming, this approach signals a future where content creation, simulations, and interactive experiences are driven by AI models rather than traditional code. This approach will reduce development time and cost while enabling more intuitive design methods. Beyond gaming, this has applications in education, training, and simulation of high-risk operating environments (military, healthcare, etc.).

A company that caught my eye…

• Cybord

  The company recently closed its Series A funding round ($8.7M), led by Capri Ventures. The company caught my eye because it has built a visual AI that inspects electronic circuit boards for defects and low-quality components. It is trained on a dataset of over 4 billion components. It’s a novel approach that shows a lot of promise.

Best business article(s) I read this week…

• Embracing Gen AI at Work

  This article explores the impact of generative AI on various business sectors and the skills professionals need to succeed in this era. It points out that non-technical users can leverage generative AI in 40%+ of U.S. work activities across industries such as legal, finance, retail, and healthcare. To excel, workers must develop "fusion skills" that involve collaborating with AI systems to achieve better outcomes.

Best technical article(s) I read this week…

• Google DeepMind releases new papers on how they are improving robot dexterity

  While you may not be interested in the technical papers about ALOHA Unleashed and DemoStart, you may enjoy the videos of dexterous robots tying shoes and hanging polo shirts on hangars.

Other items I found valuable/interesting…

• OpenAI launches new models that can reason about complex tasks

  OpenAI announced the release of two new models: o1 and o1-mini. The company trained the models to spend more time thinking before responding. OpenAI claims these new models “can reason through complex tasks and solve harder problems than previous models in science, coding, and math.” Professor Ethan Mollick provides an excellent overview of their capabilities.

• The AI Spending Spree, in Charts

  The article provides several interesting charts on how tech giants and VCs spend on AI. As the first chart depicts, Amazon, Microsoft, Google (Alphabet), and Meta spent a combined $52.9B on AI in the past three months.

• Generative AI is looking like a big dud

  For a contrarian view, read this Boston Globe op-ed piece that argues (incorrectly, in my view) that generative AI has not, and may not, “have much impact on the world.”

• How digital twins of Earth are spinning up safer decisions for the planet

  This article provides a status report on ongoing efforts by NASA and others to create a digital twin of Earth. The article lists several valuable applications of such a twin but notes that “it's a many-years-long project.”

Coolest thing I saw…

• Flexible, soft robots

  A breakthrough in stretchable electronics has enabled the construction of new forms of flexible soft robots. While still under development, check out some that exist already…

A company that caught my eye…

• Sakana

  This Tokyo, Japan-based firm just closed a $100M Series A round that included New Enterprise Associates and Khosla Ventures. It caught my eye because it is being positioned as a “Sovereign AI” initiative, wherein countries seek to “capture and codify their data, culture and language through their own unique large language models.” This is not the first such effort, but it may be the best funded.

Best business article(s) I read this week…

• The Crowdless Future? Generative AI and Creative Problem-Solving

  This Harvard Business School Working Paper presents findings from a study that compares business ideas generated by humans working alone and humans working with AI. Their findings show that human-only solutions demonstrated greater novelty, but human-AI-generated ideas were superior regarding strategic viability, environmental and financial value, and overall quality. A key insight is that human-guided AI solutions, especially those using differentiated search techniques, outperform independent AI searches in generating more innovative outputs.

• Rewiring the way McKinsey works with Lilli, our generative AI platform

  This is a case study published by McKinsey & Company detailing how it uses generative AI as a knowledge management platform. It’s a quick read with lots of interactive graphics.

Best technical article(s) I read this week…

• OLMoE: An open, small, and state-of-the-art mixture-of-experts model

  The Allen Institute for AI (AI2), in conjunction with Contextual AI, released OLMoE, an open-source Mixture of Experts (MoE) model. As the authors write, “mixture-of-experts architectures have become a core technology used by closed AI labs to more efficiently serve and train leading language models.” The source code, the training data, the training performance data, and the research paper are all available from the link above.

• Configurable Foundation Models: Building LLMs from a Modular Perspective

  This article describes a method for the modular construction of large language models (LLMs) that breaks them into functional components called "bricks." These bricks can be emergent (formed during pre-training) or customized (added post-training), enabling efficient LLM updates, scaling, and task specialization. This approach optimizes computational efficiency, reusability, and adaptability.

Other items I found valuable/interesting…

• How digital twins could save time, money, and lives in developing prescription drugs

  The article discusses how digital twins are revolutionizing drug development by simulating human organs, cellular structures, and patient health conditions. These simulations can predict outcomes in clinical trials, streamline drug discovery, and improve the personalization of treatments.

• Augmented tomorrow: Augmented Reality (AR) experiences beyond smartphones

  This Ericsson ConsumerLab report presents research findings from 10 global markets on augmented reality and mixed reality (MR) trends. Two key findings:

  • The number of consumers combining smartphones and AR devices will double in the next five years.

  • Consumers want on-the-go AR devices and are willing to pay 20 percent more for portability.

• Automation’s Biggest Challenges: Full Human-Robot Collaboration

  This Association for Advancing Automation article summarizes how collaborative robots are used today and identifies what other advancements are required to achieve “full collaboration.”

Coolest thing I saw…

• The year is 2149 and…

  This short fiction piece envisions what life will be like 125 years from now. It may not be “cool” in the strictest sense, but it was thought-provoking.

A company that caught my eye…

• Safe Superintelligence Inc.

  When it was announced, I wrote about Illya Sutskever’s (OpenAI co-founder and its former Chief Scientist) new startup. It caught my eye again this week when it announced it had closed a $1B SEED round. Not a seed round that produced a $1B post-money valuation. It took in $1B in investment from VCs such as A16Z and Sequoia.

In a previous article, I introduced a capability maturity model for organizational adoption and use of AI and other collaborative technologies (aka “collaborative intelligence” or CI).

This article focuses on Maturity Level 3, named “Integrated.” It will:

1. provide more detail about the observable characteristics at this level,

2. present strategies and actions that will enable organizations to move to Maturity Level 4.

Subsequent articles will focus on Maturity Levels 4-6. When this series of articles concludes, I will provide an assessment tool that helps organizations determine their current maturity level.

Characteristics of Organizations with "Isolated" Collaborative Intelligence

Organizational Awareness/Knowledge of CI Technologies

At Maturity Level 3, senior leadership and key decision-makers have gained a deeper awareness of CI's potential and strategic importance beyond isolated use cases. They recognize the value of integrating CI into organizational strategy and operations.

This awareness is not limited to a few individuals; it is shared across departments, fostering a culture where CI is viewed as a critical driver of business transformation. The organization has moved beyond generative AI and now considers a spectrum of CI technologies, including digital twins, collaborative robots (cobots), and advanced analytics platforms. There is a concerted effort to explore how these technologies can work together to optimize processes, enhance decision-making, and create new value propositions.

CI-Related Skills/Experience and CI-Specific Skills Management Processes

The organization has developed a more robust and systematic approach to building and managing CI-related skills and experience. Unlike earlier stages, where skills were unevenly distributed or developed through individual initiative, the organization now has formal training programs in place that are aligned with its strategic goals for CI adoption.

These programs ensure that employees throughout the organization gain the necessary skills to work with and implement CI technologies. The organization also begins to institutionalize a skills management process, identifying key competencies required for different roles and systematically addressing skill gaps. This process includes regular assessments of employee skills, targeted training sessions, and a clear pathway for career development in CI-related fields.

Influence of CI on Business Strategy

The organization fully integrates Collaborative Intelligence into its corporate and business unit strategy development processes, viewing CI as critical for creating and sustaining competitive advantage.

CI technologies are systematically evaluated for their potential to create value via operational efficiencies, new value propositions, or transformed business models. The organization leverages CI to identify and capitalize on emerging market opportunities, ensuring it stays ahead of competitors in rapidly evolving industries.

CI Technologies Deployed

The organization transitions from isolated CI deployments to a more integrated and scalable approach. CI technologies are no longer confined to niche applications or specific departments but are embedded into key business processes across the organization.

The organization strategically deploys CI technologies in areas where they can drive significant performance improvements, such as strategy development, production, customer service, supply chain management, product development, and decision support. These deployments are designed to be scalable and extensible, allowing the organization to build on initial successes and expand CI’s role in the business. The integration focuses on creating a unified technology architecture that supports seamless interoperability between different CI systems.

CI-Enabled Business Processes, Job Designs, and Team Structures

At Maturity Level 3, the organization redesigns its business processes, job roles, and team structures to fully integrate CI technologies. Unlike previous levels, where CI was often bolted onto existing workflows, the organization now reimagines and reconstructs its operations to maximize CI's capabilities.

Job designs are transformed to reflect the new reality of human-machine collaboration. Roles that previously involved repetitive or manual tasks are redefined to focus on higher-order activities that require human judgment, creativity, and problem-solving skills. Employees work alongside CI technologies, using them as tools to enhance their productivity and decision-making capabilities. Cross-functional teams that combine human expertise and CI are becoming more common, enabling the organization to tackle complex challenges with a blend of insights, skills, and technologies. These teams are designed to be agile and capable of quickly responding to changes in the business environment or technological advancements.

CI-Enabled or Embedded Products/Services

The organization systematically integrates CI into its products and services, moving beyond experimental or isolated efforts to full-scale deployment. CI is now seen as a core element of the organization’s value proposition, as it is used to enhance customer experiences, improve product performance, and create new revenue streams.

CI-Aware Management Process/Metrics

The organization has begun to rethink its management processes and metrics to account for the growing role of CI across the organization. As CI becomes a vital part of the workforce, traditional performance management approaches and the metrics used to support decision-making are redesigned to reflect the new dynamics of human-machine collaboration.

Performance management systems are adapted to recognize the impact of CI on individual and team outputs. Instead of solely assessing individual contributions, the organization evaluates how effectively employees leverage CI tools to enhance their work, collaborate with intelligent systems, and drive innovation. This shift acknowledges that human and machine collaboration is now a critical component of overall performance.

CI Governance Processes and Policies

The organization’s approach to CI governance has evolved, moving beyond compliance to active support of innovation and strategic agility. Governance processes and policies are designed to ensure the responsible and ethical use of CI technologies and enable the organization to take calculated risks and make rapid decisions in a fast-moving technology landscape.

The organization enhances its risk management framework to better handle the unique challenges CI poses. This includes more sophisticated methods for assessing and managing risks related to data security, algorithmic bias, and the ethical implications of AI-driven decisions. The goal is to create a governance structure that protects the organization from potential downsides while empowering it to explore and exploit the full potential of CI technologies.

Additionally, CI governance policies are now structured to support swift decision-making processes, ensuring the organization can quickly adapt to new technological developments and market conditions. This might include streamlined approval processes for CI-related projects, flexible guidelines that can be adjusted as the technology evolves, and mechanisms for continuously monitoring and mitigating risks as they emerge.

Organizational Culture

The organization’s culture fully embraces Collaborative Intelligence as a core element of its operations and strategy. Unlike earlier stages, where cultural shifts were non-existent or occurred only within isolated teams, most of the organization now embraces CI and the values of collaboration, agility, experimentation, and continuous learning.

Leaders actively promote the benefits of CI, highlighting how these technologies can enhance, rather than replace, human capabilities. They emphasize the importance of collaboration between humans and intelligent machines, framing CI as a partner in driving innovation, efficiency, and competitive advantage. This messaging helps to alleviate lingering fears about job displacement and builds a sense of shared purpose around the organization’s CI initiatives.

Data Models, Architectures, Workflows, and Management Processes/Policies

At Maturity Level 3, the organization has made significant progress in evolving and standardizing its data models, architectures, workflows, and management processes/policies to fully support the development and use of CI technologies.

This standardization facilitates better data integration across different systems and departments, breaking down silos and enabling a more comprehensive view of the organization’s data assets. Architectures are developed with scalability and flexibility, allowing the organization to rapidly expand its CI capabilities as needed.

Data workflows are optimized to ensure data flows seamlessly from collection to analysis, supporting CI’s real-time and predictive capabilities. These workflows are designed to be adaptable, allowing the organization to easily incorporate new data sources and types as CI technologies evolve.

The organization also strengthens its data management processes and policies to support the growing demands of CI. Data governance frameworks are enhanced to ensure data quality, consistency, and security across the organization. Policies are established to manage data lifecycles effectively, ensuring that data remains a valuable asset throughout its lifecycle.

Computing and Cybersecurity Infrastructure

The organization has upgraded its computing and cybersecurity infrastructure to support the expanded use of CI. The organization implements more advanced security protocols and tools to safeguard sensitive data and ensure the integrity of CI systems. This includes AI-enhanced real-time threat detection and response systems that quickly identify and mitigate potential security breaches.

The organization also strongly emphasizes securing each CI application, ensuring that models and algorithms are protected from tampering or misuse. This includes adopting practices such as secure model training, regular audits of AI systems, and encryption to protect data at rest and in transit.

Moreover, cybersecurity policies are updated to reflect the specific risks associated with CI. These policies ensure that all employees, especially those working directly with CI technologies, are trained in best practices for maintaining security and compliance. This proactive approach to cybersecurity helps the organization mitigate risks while enabling the continued expansion and integration of CI technologies.

Key Strategies/Actions to Reach Maturity Level 4

At Maturity Level 3, organizations have successfully integrated Collaborative Intelligence into core business processes, product offerings, and strategic decision-making frameworks. However, the journey continues. To achieve the next level of maturity, the organization must transition from integration to optimization. This involves refining and enhancing strategies, workflows, organizational structures, technologies, and (especially) management systems, with a focus on maximizing the value that CI can deliver across the organization.

Specifically, companies that want to move to Level 4 (and beyond) should take the following actions:

1. Leverage CI to create a continuously adaptive, data-driven strategy development process that enhances foresight and decision-making.

2. Move beyond core processes to prioritize and automate high-impact workflows while eliminating low-value tasks, ensuring CI resources are applied where they offer the greatest return.

3. Leverage CI to redesign job roles and organizational structures, enhancing human-machine collaboration, agility, and workforce adaptability.

4. Use CI to decentralize data management, enhance processing efficiency, and automate system maintenance.

5. Use CI to enhance financial modeling, portfolio management, and decision-support systems, driving more accurate, data-driven, and efficient managerial decision-making.

Continuously Adaptive Strategy Development

The organization should use CI to enhance foresight and scenario planning to optimize its strategy development process. Applying advanced data analytics and predictive modeling enables the organization to evaluate a broader range of future possibilities and trends, allowing it to stay ahead of industry shifts.

The organization should continuously refine its strategic goals using real-time data. Monitoring these data streams enables the organization to adjust its strategic objectives dynamically, keeping them aligned with evolving market conditions and opportunities.

CI should also be used to identify emerging risks and opportunities. Processing vast datasets with AI enables the organization to uncover patterns and trends that human analysis might miss, ensuring it acts quickly to capitalize on opportunities and mitigate risks.

Finally, the organization should automate routine strategic assessments with AI to track key performance indicators (KPIs) and provide automated insights. Automating these processes enables leadership to focus on high-level strategy while CI systems handle operational monitoring and adjustments.

Optimize Workflows

The organization should identify other high-value processes (beyond its core processes) for CI optimization by analyzing operational data. Analyzing this data enables the organization to pinpoint where CI can deliver the most significant return on investment (ROI) and improve efficiency.

The organization should automate routine and repetitive tasks using CI technologies like robotic process automation (RPA). Automating these tasks enables the organization to eliminate low-value work, freeing human employees to focus on higher-order tasks requiring creativity and strategic thinking.

The organization should also streamline decision-making workflows by embedding AI tools that provide real-time insights, recommendations, and automated reporting. These tools reduce manual effort and process bottlenecks, enabling faster and more accurate decision-making.

The organization should establish a prioritization framework to assess which workflows are worth CI investment. Implementing this framework enables the organization to determine which workflows can be simplified or maintained without substantial technology enhancements, ensuring that resources are allocated efficiently.

Finally, the organization should implement continuous monitoring and feedback systems using CI to identify workflow inefficiencies in real-time. These systems enable the organization to propose adjustments before inefficiencies escalate, maintaining optimal performance across all processes.

Redesign Job Roles and Organizational Structures

The organization should redesign roles to focus on human-machine collaboration. Shifting employee responsibilities toward creative, strategic, and problem-solving tasks enables the organization to automate repetitive, lower-value activities through CI, enhancing overall productivity.

The organization should also create agile, cross-functional teams that integrate CI technologies to improve collaboration across departments. Building these teams enables the organization to leverage both human and machine expertise, ensuring that resources are applied where they deliver the most value.

The organization should implement dynamic job roles that can evolve in response to CI-driven changes. Establishing these roles enables employees to continuously adapt their skills and responsibilities based on the organization’s shifting needs and technological advancements, fostering a more resilient and flexible workforce.

The organization should use CI to analyze workforce performance and structure to optimize its structure further. Analyzing these elements with CI enables the identification of areas where roles can be optimized, merged, or redefined, enhancing productivity and reducing redundancies.

Finally, the organization should develop highly personalized CI-driven training and development programs focused on upskilling employees to work effectively alongside intelligent machines. Implementing these programs enables the workforce to stay equipped to handle evolving CI-enabled tasks, ensuring the organization remains competitive in an increasingly automated environment.

Enhance Technological Efficiencies

The organization should adopt a federated data architecture. Implementing this architecture enables business units to manage their data autonomously while maintaining interoperability, reducing bottlenecks, and increasing agility in data management.

The organization should also optimize CI models and infrastructure to run more efficiently by leveraging edge computing and decentralized processing. Utilizing these technologies enables the organization to ensure real-time, low-latency performance where necessary, enhancing responsiveness and operational efficiency.

The organization should use AI-driven orchestration to dynamically allocate computing resources and optimize workloads across cloud, edge, and on-premise environments. This enables the organization to maximize infrastructure efficiency, ensuring that resources are utilized effectively and costs are minimized.

The organization should automate data quality management through CI tools. Automating these processes enables continuous monitoring, cleansing, and validation of data across systems, ensuring that the data feeding into CI models is accurate and reliable.

Finally, the organization should implement predictive maintenance for technology infrastructure using AI. Monitoring system health with AI enables the organization to predict failures, reduce downtime, and ensure seamless CI operations, maintaining high system reliability and performance levels.

Enhance Management Systems and Decision-Support

The organization should develop CI-enhanced valuation models that leverage real-time data and predictive analytics. These models enable the organization to dynamically assess the financial value of projects, investments, and assets, leading to more accurate and timely financial decisions.

The organization should also implement AI-driven enterprise portfolio management to evaluate project alignment, risk, and performance continuously. This approach enables data-driven resource allocation and prioritization decisions, ensuring investments align with strategic goals and deliver maximum value.

The organization should use CI-powered decision-support systems to provide context-specific recommendations for day-to-day operations. Incorporating predictive KPIs into these systems enables the organization to monitor and adjust performance in real-time, allowing for more responsive and informed decision-making.

Finally, the organization should automate routine managerial decisions with CI tools to optimize decision-making processes. Automating these tasks enables managers to focus on higher-level strategic issues and complex challenges, enhancing overall managerial effectiveness.

By focusing on these five key strategies and related actions, organizations can advance from Maturity Level 3 to 4, where CI is optimized across the organization, driving sustainable improvements in organizational performance.

Collaborative Intelligence is a Transformativ, LLC publication. If you’d like to learn more about how to become an AI-powered enterprise, please contact us here.

Best business article(s) I read this week…

• How Human-Informed AI Leads to More Accurate Digital Twins

  This article describes how human expertise and AI can enhance the accuracy and effectiveness of digital twins. This approach reduces costs, accelerates innovation, and improves safety, making it a valuable strategy for mission-critical applications. The article provides examples and insights, highlighting a hybrid methodology beneficial for industries where data is limited but precision is essential.

• Do Not Use LLM or Generative AI For These Use Cases

  I have written at length about the need for executives to think more broadly about AI because Generative AI is often inappropriate for many applications. This article summarizes twelve use cases and six AI techniques and concludes with a matrix showing where different AI techniques are most appropriate.

Best technical article(s) I read this week…

• Leveraging Large Language Models for Enhanced Process

  Model Comprehension

  The authors describe how they trained an LLM to understand and generate responses to specific questions related to business process models. ​ I found this interesting because it is another valuable generative AI business application. Businesses can use the approach described (and the open-source software provided) to improve their understanding of their business processes and identify prospective optimizations.

Other items I found valuable/interesting…

• Digital Doubles Vs. Digital Twins: Unraveling The Distinctions

  This podcast from Forrester describes the differences between digital doubles and digital twins. Then, it discusses the use cases for each technology and some challenges in deploying and using each effectively.

• Engineers design tiny batteries for powering cell-sized robots

  I included this because I think it might be a very weak signal of an emerging trend: microscopic robot swarms that can sense and respond to their environments. This technology has a wide range of applications, including healthcare, agriculture, and the military.

• 20 Generative AI Tools For Creating Synthetic Data

  I generate a lot of synthetic data for testing on confidential or HIPAA-restricted client data. While I was aware of several tools on the list, some were new to me.

Coolest thing I saw…

• DeepMind’s AlphaProof and AlphaGeometry 2

  Google DeepMind released a video explaining how its new AlphaProof and AlphaGeometry models can solve Math Olympiad problems. Although the technical papers were released earlier this year, this video is much easier for a layperson to understand. I’m sort of a math geek, so I thought this was cool.

A company that caught my eye…

• Magic

  The company, which automates software development via generative AI, announced it had closed a $320M investment from Eric Schmidt, Sequoia, and Atlassian (among others). It also announced a partnership with Google Cloud to build two supercomputers that can scale to “tens of thousands of [NVIDIA] Blackwell GPUs.”

Best business article(s) I read this week…

• How to fine-tune AI for prosperity

  This article takes a macro-economic view of AI and its prospective impact on US productivity, exploring the questions “How will AI boost productivity?” and “How long will it take to discern its impact on the broader economy?”
  
  The article’s answer to the first question is improving worker productivity rather than replacing workers and using AI to boost scientific discovery and innovation. Its answer to the second is “it depends,” with the uncertainty driven by the difficulty in estimating diffusion rates and “Solow’s Paradox.” Solow’s Paradox is named for Nobel Laureate Robert Solow, who, after studying the productivity impact of then-new personal computers, quipped, “You can see the computer age everywhere but in the productivity statistics.”
  
  For more on this topic, see this article by one of the economists quoted.

• Generative artificial intelligence in innovation management

  Since the article above states that its use in innovation will be the GenAI application most impactful on productivity, I included this article, which is a survey of how generative AI could impact all areas of innovation management.

Best technical article(s) I read this week…

• A novel decision support system based on computational intelligence and machine learning

  The authors combine a “particle swarm optimization” algorithm with a decision tree to significantly improve classification performance in support of a Zero-Defect Manufacturing (ZDM) process for injection molding. The combination proved superior to other approaches, such as Support Vector Machines, on all classification performance metrics. My interest is not in the specific application but in the novel combination’s ability to improve classification in general for such things as customer segmentation and sentiment analysis.

Other items I found valuable/interesting…

• Custom-Trained Household Robots

  Researchers at MIT’s Computer Science and AI Laboratory (CSAIL) have developed a procedure to train household robots quickly for specific environments. The researchers realized that training a robot to work in all environments was too difficult and unnecessary. All they had to do was quickly train each robot for its specific household environment. The prototype is trained by people using their phones to take pictures of the environment, uploading them to software that translates the pictures into something the robot understands, and then using simulations to train the robot on tasks specific to the environment depicted.

• An “AI Scientist” is inventing and running its own experiments

  At the University of British Columbia, an "AI Scientist" is autonomously designing and executing its own experiments, potentially revolutionizing scientific discovery. This AI doesn't just analyze data—it formulates hypotheses, writes code, and iterates experiments, blurring the lines between human and machine-driven research. For business leaders, this breakthrough could signal a future where AI not only supports but drives innovation, offering a glimpse into the evolving role of AI in research and development.

• AI-powered Web Application Firewalls (WAFs) improve application security

  The article details how using WAFs can dramatically improve web application security. They work at the application layer (Layer 7 for those who know) and use AI to scrutinize requests to identify attempts to exploit app logic and functionality using injection attacks, stolen credentials, unusual workflows, and other techniques.

• The Turing Test and our shifting conceptions of intelligence

  The article explores the evolution of the Turing Test, a thought experiment introduced by Alan Turing in 1950 to examine whether machines can exhibit human-like intelligence. Created to challenge the belief that only living organisms can possess intelligence, the Turing Test has become a popular yet often misunderstood yardstick for AI. The article contends that recent assertions of chatbots passing the Turing Test are misleading, as the test's efficacy in evaluating genuine intelligence is increasingly questioned. It proposes that intelligence is more intricate than Turing or early AI researchers had envisioned.

Coolest thing I saw…

• Despite the underlying theme of innovation in many of this week’s highlights, I didn’t see anything new this week that I thought was cool.

A company that caught my eye…

• SigularityNET

  The company is attempting to build a “multi-level cognitive computing network” powered by an array of supercomputers to accelerate the achievement of Artificial General Intelligence (AGI).

In a previous article, I introduced a capability maturity model for organizational adoption and use of AI and other collaborative technologies (aka “collaborative intelligence” or CI).

This article focuses on Maturity Level 2, named “Isolated.” It will:

1. provide more detail about the observable characteristics at this level,

2. present strategies and actions that will enable organizations to move to Maturity Level 3.

Subsequent articles will focus on Maturity Levels 3-6. When this series of articles concludes, I will provide an assessment tool that helps organizations determine their current maturity level.

Characteristics of Organizations with "Isolated" Collaborative Intelligence

Organizational Awareness/Knowledge of CI Technologies

The organization has begun to recognize the potential of Collaborative Intelligence (CI) technologies beyond chat models. While select mid-level managers and their teams are much more aware, senior leaders/managers may not yet understand CI's full range and capabilities. This limited awareness often results in the deployment of CI technologies in isolated pockets of the organization without a strategic vision guiding their broader application.

CI-Related Skills/Experience and CI-Specific Skills Management Processes

The organization has developed some CI-related skills and experience, especially among the teams deploying CI. However, the skills and experience remain unevenly distributed across the enterprise.

Much of the skills development is still achieved through individual initiative. Formal training programs and structured skills management processes are in their infancy, so deficiencies are not yet being addressed proactively and promptly, limiting the organization’s ability to fully exploit the opportunities CI might provide.

Influence of CI on Business Strategy

Some business units or functional areas have incorporated CI into their tactical plans, but these efforts still need to be unified under a comprehensive corporate strategy. CI initiatives tend to be opportunistic and isolated rather than guided by the organization’s strategic goals, resulting in missed opportunities to align CI with long-term business objectives.

CI Technologies Deployed

The organization has a few CI applications in place, but they are isolated and unconnected (e.g., a chatbot for customer service or a machine learning model for predictive maintenance). While these applications create value, their development in isolation often limits their ability to scale effectively and efficiently or expand beyond their initial application domain.

CI-Enabled Business Processes, Job Designs, and Team Structures

While some CI technologies have been implemented to support specific tasks or processes, they are often "bolted on" to existing workflows, with minimal consideration given to redesigning processes to fully exploit CI’s capabilities. Thus, the organization continues to operate with legacy processes, treating CI as an add-on rather than a transformative tool.

Job designs and team/organizational structures remain the same, limiting the organization's ability to capture all the economic and intangible benefits of CI.

CI-Enabled or Embedded Products/Services

Product and service teams may be in the early stages of embedding CI into offerings, but such initiatives are often experimental and not yet fully commercialized. The development of CI-enhanced products/services is driven by individual teams or departments rather than as part of a coordinated company-wide effort.

CI-Aware Management Process/Metrics

Since CI-enabled business processes do not exist, and no job descriptions reflect the use of CI, the related management processes and metrics are not necessary. (This characteristic is unchanged from Level 1.)

CI Governance Processes and Policies

Guidelines for using generative AI have been established, but most guidelines are silent on other CI technologies. The established guidelines focus on compliance rather than on spurring innovation through such things as effective risk management and streamlined decision-making.

A more comprehensive set of CI governance processes and policies is in development but still needs to be implemented. Formal policies around Responsible AI (RAI) may exist in draft form but have not been operationalized.

Organizational Culture

The organizational culture is slowly beginning to shift to accommodate CI, but significant barriers remain. Employees and leaders may recognize the potential of CI. Still, there is often resistance to change, driven by fear of job displacement or a lack of understanding of how CI can enhance, rather than replace, human roles. This cultural inertia hampers the adoption of CI technologies and prevents the organization from realizing CI's full potential.

Data Models, Architectures, Workflows, and Management Processes/Policies

The organization has started to address data-related challenges but has yet to develop a comprehensive approach.

While there are exceptions, most data remains siloed. Some integration efforts are underway, but their scope is often limited to what is required to support the isolated uses of CI within the organization. Data architectures are being redesigned to support a more diverse mix of data, but they lack the robustness and scalability needed for widespread CI development and use. Data management processes/policies are inconsistent, and some processes require manual intervention.

Computing and Cybersecurity Infrastructure

The organization's computing and cybersecurity infrastructure is sufficient to support the isolated CI applications currently in use, but it still needs to be optimized for scalability or security. The infrastructure is not yet prepared to handle the demands of more extensive CI deployments, which could lead to performance bottlenecks or security vulnerabilities as CI adoption increases.

Key Strategies/Actions to Reach Maturity Level 3

Organizations at Maturity Level 2 have begun to deploy CI in isolation, but to advance to Maturity Level 3 and beyond, they must shift from these isolated efforts to a more strategic and integrated approach. Rather than unsystematically expanding their use of CI technologies, they should focus on creating a cohesive strategic vision and a technical infrastructure that will enable the seamless integration of CI across the enterprise.

By strategically aligning their CI initiatives with broader organizational goals and fostering a culture that supports innovation and collaboration with intelligent machines, organizations will create a foundation for sustained success in the AI Era.

Specifically, companies that want to move to Level 3 (and beyond) should take the following actions:

1. Develop a strategic vision for CI

2. Continue to develop/acquire CI-related skills and experience

3. Integrate CI into redesigned core business processes and jobs

4. Cultivate a CI-supportive organizational culture

5. Implement robust CI governance/management processes

Develop a Strategic Vision for CI

Senior leaders should work to develop a clear, strategic vision for CI that aligns with the organization’s long-term goals. They should review each item on the organization’s strategic agenda to identify how CI can help the organization achieve each goal more quickly, more effectively, or with fewer resource requirements. In addition, the organization’s business model should be reviewed to determine whether and how CI could be used to make the model more effective, more profitable, or more sustainable.

The resulting vision should guide the deployment of CI technologies across the enterprise, ensuring that all CI initiatives are coherent and contribute to the achievement of a strategic objective.

Continue to Develop/Acquire CI-Related Skills and Experience

Reaching Maturity Level 3 requires organizations to gain access to sufficient numbers of people with the skills/experience to develop, deploy, and operate CI technologies across most business areas and to design and implement CI-enhanced business processes and jobs. This requires an effective talent acquisition strategy, an effective and comprehensive skills development program, and, perhaps, the development of relationships with third parties who can provide specific resources. Thus, organizations need to accelerate and expand the talent development/acquisition efforts they initiated to move from Maturity Level 1 to Maturity Level 2.

Integrate CI Technologies into Redesigned Business Processes and Jobs

Organizations should begin moving beyond isolated CI deployments by redesigning end-to-end business processes to integrate CI technologies wherever they are technically feasible and economically and strategically valuable. This involves rethinking workflows, job roles, and team structures to maximize the benefits of CI for the organization and its workforce.

Cultivate a CI-Supportive Organizational Culture

Organizations must work to shift their culture to one that fully embraces CI. This involves addressing fears and misconceptions about CI, demonstrating its value through early successes, and creating incentives for employees to engage with and support CI initiatives. Building a culture that supports innovation and collaboration with intelligent machines is essential for sustaining long-term CI adoption and growth.

Implement Robust CI Governance and Management Processes

To support the broader deployment of CI, organizations must establish formal governance and program management processes that align with Responsible AI principles and established strategic, financial, and risk management practices. This includes developing metrics to measure CI performance and ensuring that all CI initiatives adhere to best practices in efficiency, scalability, security, and ethics.

By focusing on these five key strategies and related actions, organizations can advance from Maturity Level 2 to 3, where CI becomes a more integrated and strategic component of their organization and its operations.

Collaborative Intelligence is a Transformativ, LLC publication. If you’d like to learn more about how to become an AI-powered enterprise, please contact us here.

Best business article(s) I read this week…

• Where Data-Driven Decision-Making Can Go Wrong

  The rapid adoption of AI and machine learning in companies has also increased organizational focus on data-driven decision-making. This article lists five ways in which data-driven decision-making can lead to suboptimal or poor decisions. They are:

  • Conflating correlation and causation

  • Misjudging the potential magnitude of effects

  • Creating a disconnect between what is measured and what matters

  • Misjudging generalizability

  • Overweighting a specific result

Best technical article(s) I read this week…

• Localized Neural Network Modeling of Time Series: A Case Study on US Monetary Policy

  I think I have previously written that I have a side business and a strong interest in applying AI/ML to quantitative finance. This may not appeal to many, but I liked it.

Other items I found valuable/interesting…

• This 300-Year-Old Winemaker Is Embracing AI

  This story melds two of my favorite topics: wine and AI. It describes how Château Ducru-Beaucaillou is using AI to improve quality and increase production. For example, the winery uses AI-powered sensors to monitor vine health and sap flow and to predict disease and frost risks.

• Researchers Ranked AI Models Based on Risk—And Found a Wild Range

  Researchers developed a benchmark that uses thousands of prompts to test chat model performance against government regulations and usage guidelines published in the US, China, and the EU. Each model did well on some aspects of risks but not others. None did well on all.

• AI Risk Repository

  In a development related to the previous article, researchers at MIT released a publicly available database of 700 risks related to AI, categorized by risk domain. The database is available as a Google Sheet, and there is an accompanying explainer video.

Coolest thing I saw…

• Robots making Levi’s ®

  The video below describes how Siemens, in collaboration with a startup (Sewbo) and the Advanced Robotics for Manufacturing Institute, has introduced a robot that can complete sewing tasks.

A company that caught my eye…

• Tavus.io

  The company has released a platform that enables software developers to create realistic digital twins for use on conversational tasks. The video at this link demonstrates the platform’s capabilities, and the company’s website link in the title provides an opportunity for a live, interactive demo. When I tried it, it wasn’t as responsive as it is in the video, but I guess that’s to be expected.