Mastering CI Maturity: Understanding Level 3 and How to Move to Level 4
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:
provide more detail about the observable characteristics at this level,
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:
Leverage CI to create a continuously adaptive, data-driven strategy development process that enhances foresight and decision-making.
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.
Leverage CI to redesign job roles and organizational structures, enhancing human-machine collaboration, agility, and workforce adaptability.
Use CI to decentralize data management, enhance processing efficiency, and automate system maintenance.
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.