Mastering CI Maturity: Understanding Level 2 and How to Move to Level 3
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:
provide more detail about the observable characteristics at this level,
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:
Develop a strategic vision for CI
Continue to develop/acquire CI-related skills and experience
Integrate CI into redesigned core business processes and jobs
Cultivate a CI-supportive organizational culture
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.