Why You Need a Capability Maturity Model for AI

Rollouts of artificial intelligence (AI) and automation technologies are happening faster as innovations create new opportunities and the hype cycles motivate leaders to mash the AI accelerator. As a new system of work emerges, it’s becoming very clear that the true value of AI is in augmenting human effort, not displacing it.

This new system of work, built on AI and automation over just the past few years, elevates the role of human workers by automating tedious tasks, accelerating content creation, enabling data-driven insights, and more—all at an unprecedented scale. Humans work faster, more creatively, and strategically while up-leveling their efforts with AI assistants that help them code, design, and write much more effectively. This symbiotic human-AI-automation relationship of collaborative intelligence generates faster innovation cycles where AI and automation take on the mundane work while people focus on strategic refinement and higher-level endeavors.

To fully harness these benefits, however, enterprises must adopt AI-infused operating models, which require changes in governance, structure, and processes to ensure AI and automation integrations are holistic and aligned with strategic business objectives.

As AI’s value continues to inflate—and for good reason—it’s imperative that organizations take a moment to understand the opportunities and risks, match technologies with goals, and plot a prudent course to success. In other words, sustainable success requires a more structured approach amid a seemingly wild west of AI adoption.

Read on to discover how a new white paper, "The Capability Maturity Model for Collaborative Intelligence," provides an essential framework and clear roadmap for enterprises adopting an AI-infused operating model.

AI success depends on experience-driven structure

Technology and hype go hand in hand, but the speed at which AI has become mainstream is unprecedented. However, even the fairly recent cloud boom of the early 2000s spanned decades, from the 1999 launch of Salesforce to the 2010 launch of Microsoft Azure to the continuing popularity of cloud migration options.

AI is making the migration to the cloud look downright glacial by comparison. ChatGPT, in barely two years, now has approximately 800 million users. Google Gemini hit 350 million users in 18 months. That’s massive, and the related AI frenzy is fueling a rush of both C-suite mandates and new solutions. The result: Gartner says 90% of CFOs expect to spend more on AI in 2025, with zero projecting cuts to AI budgets.

Unfortunately, 65% of executives say they lack the technological expertise necessary for AI-driven transformations, according to Accenture research. Constantly improving and fast-expanding AI innovations, coupled with the relative newness of AI as an enterprise solution, are the culprits. Leaders today have little AI experience to guide their way. It’s not a technology challenge—AI is now available in nearly every application—it’s a maturity challenge.

The impacts of organizations jumping on the AI train without a roadmap are unnecessary risk, wasted resources, misguided investments, lost market traction, and more. A better alternative is to use an experience-driven maturity model, built to transition organizations from using AI as a basic support tool with heavy human oversight through to creating fully autonomous operations.

This Collaborative Intelligence Capability Maturity Model (CI-CMM) is a practical framework for responsibly, sustainably, and successfully scaling AI adoption. As mentioned, AI success comes from collaborative intelligence, a synergistic partnership between humans and AI that enhances decision-making and innovation. To learn more, check out our blog post, Collaborative Intelligence Explained: How Humans and AI Work Smarter Together.

Why AI adoptions fail

Too many promising executives found their way to career stagnation through misguided enterprise transformations. Bain & Company research shows that only 12% of enterprises reach business transformation goals. McKinsey research is more generous, finding that 30% of transformations succeed. Either way, the odds are not great.

Bain points to lack of experience and poor preparation as the key contributors to failure, while McKinsey mentions unrealistic goals, uninspiring change management, and insufficient investment.

The speed, pressure, and inexperience around AI magnify the risks and introduce new challenges for executives. Specifically for AI and technology-focused transformations, common roadblocks preventing organizations from realizing widespread and sustainable impact are:

Lack of shared goals
Siloed pilots with no path to scale
Insufficient support as teams learn, try, fail, and retry
No trust in AI decision-making outputs
No structure for cross-functional collaboration

The CI-CMM empowers AI leaders to overcome these challenges with methodical guidance to progress through the stages of AI maturity and scale responsibly.

AI success requires tools to drive value and a map to get there

The CI-CMM gives organizations a clear path to increasing organizational readiness, operational capabilities, and technological sophistication that simultaneously increases AI autonomy while decreasing human intervention. This enables AI to do more work with less human oversight while humans can spend more time on strategic, intellectual, creative, and other high-value, human-centric work.

The key here is to balance technological capabilities with organizational and operational capabilities. The organization’s maturity must increase in step with technical maturity. Without a structured model, it’s easy for leaders to deploy more tools the enterprise just isn’t ready for.

The CI-CMM gives leaders a structured roadmap to:

Align teams
Build trust
Increase human-AI collaboration
Scale across processes
Govern responsibly

As technological maturity increases, the CI-CMM ensures workers become more AI-adept even as they experiment within guardrails and governance, reducing risk and increasing trust. Incrementally, workers will confidently infuse AI into processes across enterprise domains.

Introducing the CI-CMM framework

The CI-CMM enables workers to continue gaining trust in AI capabilities and reducing oversight of AI outcomes, creating a flywheel of autonomy as workers increase collaboration with AI.

The five stages of CI-CMM, listed below, can be used to assess current AI maturity and as a guide to increasing maturity in pursuit of collaborative intelligence for an autonomous enterprise.

Stage 1: Initial human-led AI assistance leveraging basic AI tools to support decision-making, but with significant oversight. Use cases include customer support chatbots, help desk triage automation, and employee benefits self-service.
Stage 2: Emerging collaboration from AI-augmented decisions to enhance, improve, and accelerate human decision-making through valuable insights and augmented decision-making processes. Use cases include writing assistants, customer sentiment analysis, and inventory management.
Stage 3: Balanced collaboration between humans and AI as people and technology collaboratively complete end-to-end processes and work towards achieving larger goals. Use cases include actuarial analysis and pricing, document reviews and approvals, and competitive market research and analysis.
Stage 4: Advanced collaboration, AI-led with human oversight to provide information and execute processes with human-in-the-loop (HITL) supervision for high autonomy while humans still make critical decisions. Use cases include demand forecasting, inventory and logistics optimization, drug discovery, and financial risk assessment.
Stage 5: Autonomous enterprise with fully autonomous operations, the most mature stage, where operations function independently and self-learning AI platforms manage and govern processes with minimal human intervention. Use cases include product design, marketing campaign creation and execution, merger and acquisition evaluation, and supply chain disruption mitigation.

With each stage, trust grows, governance matures, and AI-infused systems become more intelligent and self-sufficient.

Why the CI-CMM framework matters and how you can get started with it today

AI is a relative newcomer to the enterprise toolkit. In most organizations, experience simply doesn’t exist at the levels required for effective advancement of AI utilization, let alone enterprise-scale transformation. Wise leaders will rely on an experience-driven blueprint for scalable, responsible transformation.

The CI-CMM is so much more than a superficial evaluation of AI capabilities; it empowers leaders to create a foundation for effective and sustainable AI deployments and success through AI that augments, not displaces, human effort. It further enables leaders to nurture and deploy the skills, governance, and platforms necessary for success across organizational readiness, operational capabilities, and technological sophistication.

AI as a concept and a technology is progressing rapidly. However, the pressure to adopt AI must not compromise the need to deploy AI successfully within and across the enterprise. The CI-CMM gives organizations at any level of maturity a shared language, common goals, and a clear path forward toward AI-powered success.

Download the CI-CMM white paper to see where your organization stands.