You Can’t Brute Force Your Way to Capabilities:
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Technologies can be bought and implemented; capabilities must be grown. Most firms think of Generative AI and AI, as technologies that’s why they will never lead, nor catch up. AI is a capability – which means it is a combination of technology, skilled individuals and organizational processes applied over time! For senior executives, the critical question should shift from “Should we adopt AI?” to “How do we mature our AI capabilities to drive genuine transformation?” This journey, from initial education and experimentation to deep application and systemic transformation, defines the new frontier of corporate agility and competitive advantage. Because it is a capability, not just a technology, those who “wait and see” may never catch up.
This is the second in a 3 part series on making GenAI/AI real for your organization. The first article emphasized the need for improving the four C’s of AI: Content, Capability, Curation and Community. This one begins with an assessment of where you are and what you should do next.
Organizations typically traverse four distinct stages of Generative AI / AI maturity, each presenting unique challenges and opportunities. Understanding where your firm stands, and what it takes to ascend to the next level, is paramount for unlocking AI’s full potential.
1. Cautious Explorers
At the base of the maturity curve are the Cautious Explorers. These are firms that have engaged with AI, often GenAI, but their efforts remain cautious, limited, and frequently siloed. They’ve experimented, perhaps even seen promising initial results, but quickly handed off AI initiatives to the IT department, treating it primarily as a technological tool rather than a strategic business imperative.
Characteristics:
Limited Scope: AI projects are often confined to sandbox environments, pilot programs, or specific, non-critical tasks.
Trust Deficit: Significant concerns around data security, compliance, integration, and the accuracy of AI outputs hinder broader adoption. Many executives maintain a “human-in-the-loop” approach, where AI suggestions are always reviewed by a person before action.
Siloed Thinking: AI initiatives are rarely coordinated across departments, leading to fragmented efforts and a lack of shared vision.
Skeptical Outlook: There’s often a failure to recognize the long-term, transformative potential of AI beyond immediate, tactical applications.
Firm Examples: Many mid-market companies and firms in highly regulated industries like law firms often find themselves in this category. While they acknowledge AI’s presence and may use off-the-shelf tools for specific tasks like document automation or basic research, broader enterprise-wide deployment is hampered by concerns over data privacy, ethical implications, and the perceived immaturity of solutions. They are often more focused on mitigating risk than aggressively pursuing innovation, leading to a cautious, slow pace of adoption. This can create a “trust gap” in agentic AI rollouts, where systems are deployed in controlled settings to evaluate performance and mitigate risk, rather than for broader, unsupervised implementation.
2. Islands of AI Innovation: Widespread Experimentation, Fragmented Impact
Islands of Innovation
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Moving up, Islands of Innovation are characterized by widespread, enthusiastic experimentation with GenAI. This is often driven by a “thousand flowers bloom” approach, where various teams and departments initiate their own AI projects. While this fosters creativity and grassroots innovation, it often lacks centralized coordination and strategic oversight.
Characteristics:
Bottom-Up Drive: Innovation originates from individual teams or functions, leading to a proliferation of disconnected micro-initiatives.
Scaling Struggles: A significant challenge lies in moving promising prototypes and pilots out of the experimental phase into scaled, revenue-generating applications.
“GenAI Paradox”: Many firms in this stage report deploying GenAI in some form but seeing no material impact on earnings or bottom-line value due to the diffuse benefits of horizontal use cases (like employee copilots) versus higher-impact, vertical (function-specific) use cases that get stuck in pilot.
Coordination Gaps: The absence of an overarching strategy means efforts are uncoordinated, leading to duplicated work, inconsistent standards, and an inability to extract systemic transformational value.
Firm Examples: Numerous companies, particularly large enterprises, might experience this stage, as highlighted by McKinsey reports. While major tech companies like Google or Meta have the resources for widespread experimentation, earlier stages of their AI journeys or certain divisions might exhibit “Islands of Innovation” if their efforts are not yet fully harmonized. The general trend of “companies with widespread but uncoordinated AI or GenAI experimentation” points to a common struggle across many sectors where initial excitement outpaces strategic integration, resulting in many promising initiatives that fail to scale or deliver significant business value due to fragmented approaches and a lack of mature packaged solutions.
3. The AI Orchestrators: Strategic Integration and Governance
The Orchestrators represent a significant leap forward. These firms recognize GenAI as a critical organizational capability and actively integrate it into their core business strategies. They have established robust governance structures—both top-down and bottom-up—to foster innovation while ensuring responsible, ethical, and scalable AI deployment.
Characteristics:
Centralized Strategy with Decentralized Execution: A clear enterprise-wide AI strategy guides initiatives, but teams are empowered to innovate within defined frameworks.
Robust Governance: Structured AI governance frameworks are in place, addressing data privacy, ethical AI, model explainability, and risk management. This includes C-suite sponsorship for the AI agenda.
Innovation Embedding: GenAI is not an add-on but is embedded into core operational processes, product development, and customer experiences.
Talent Development: Significant investment in upskilling the workforce and fostering a culture of continuous learning and adaptation to AI.
Firm Examples: Companies like Blue Cross Blue Shield of Michigan, who use GenAI/AI to deliver superior performance for every dollar of IT. See my Harvard Business Review article on BCBSM.) PwC (with its “Agent Powered Performance” and focus on orchestrating AI agents across platforms), Mercedes-Benz integrating GenAI for sales assistance, or UPS building a digital twin of its entire distribution network) exemplify “The Orchestrators.” These companies not only develop cutting-edge applications but also actively implement comprehensive AI governance frameworks internally and offer these as services to their clients. They focus on creating structured environments that balance rapid innovation with rigorous ethical and operational guardrails, ensuring AI initiatives are aligned with strategic business goals and deliver measurable value.
4. AI Intelligence Leveragers: Human-AI Synergy for Transformation
Intelligence Leveragers
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At the apex are the Intelligence Leveragers. These firms are fully committed to integrating both silicon-based (AI) and human intelligence into the very fabric of their organization. They push the boundaries of AI, not just for efficiency gains, but for fundamental business model transformation, creating environments where AI profoundly enhances human capabilities and is deeply embedded in every facet of their operations.
Characteristics:
Holistic Integration: AI is seamlessly woven into daily workflows, decision-making processes, and customer interactions, acting as an intelligent partner to employees.
Redefined Business Models: AI enables entirely new services, products, and operational paradigms, leading to significant competitive differentiation and market disruption.
Augmented Human Intelligence: AI amplifies human potential, freeing up employees from mundane tasks to focus on higher-value, creative, and strategic endeavors.
Massive Asset Creation – Large Language Models, data centers, new hardware, new software stacks. They create tremendous proprietary intelligence assets of high scale.
Firm Examples: All of the hyperscalers – Google, Microsoft, NVDIA, Amazon, Meta, Apple, Tesla – use AI to build AI. These firms see AI not just as a tool, but as a co-pilot and a strategic asset that fundamentally reshapes their operations, customer engagement, and competitive positioning, creating a symbiotic relationship between advanced AI systems and human ingenuity for unparalleled business outcomes.
The Path Forward for Senior Executives
The journey from a “Toe Dipper” to an “Intelligence Leverager” is not merely about adopting more technology; it’s a strategic imperative demanding a fundamental shift in mindset, culture, and operational frameworks. Senior executives must lead this transformation by:
- Be a Hands-on AI Leader: Nothing moves an organiation forward faster than the openness and curiosity of the top dog. She or he must lead by example, with hands on understanding and Generative AI and AI use. They must champion a holistic vision where AI is seen as a core enabler of future growth and competitive advantage.
- Invest in In-Person Training: oAs counter intuitive as this may be, digital intelligence is best imparted to an organization through a combination of virtual AND real world learning. The excitement, sharing and learning that occurs when people are together taking real problems in their real work, and creating real solutions – cannot be fully communicated through a virtual meeting. Of course, in between these in person learning events, much can be done and shared, but the right combination of marketplace and marketspace training is key.
- Prototype and Pilot First, Then Redesign: We’ve never seen an organization who can go straight from Toe-Dipper to Intelligence Leverager. In fast most organizations we ahve seen call a consultant in, before they have had hands on training, fail. The company does not know what to ask for, and the consultant does not know enough bout the client’s processes to build value.
- Prioritizing Enthusiastic Talent and Continuous Learning: Develop comprehensive up-skilling programs to equip employees with AI literacy and new skills, transforming them into AI-augmented professionals. Of course, support the enthusiasts – who are usually about 1 in 10 of your employees. Boosting these natural teachers is a massively efficient way to grow capability.
- Embracing Agility and Experimentation: While governance is key, maintain an agile approach that allows for rapid prototyping, learning from failures, and iterating on successful deployments. These experiments should become more comprehensive and impactful as you move up the maturity ladder.
The main thing for a leader to remember is: AI is such a different technology than we have ever had before you need to allow your organization time to understand what Generative AI and AI are capable of doing. Then, progressive building on real experience in your organization can set the stage for ongoing growth. Early on, you will measure prototypes and learning later you will add sustainable ROA, where these new tools and technology become part of your firm’s ongoing asset base.
My next article will lay out more on what types of projects you should pursue first, and how you can build a robust center of excellence in any firm.
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