AI delivers business value when leaders align priorities, modernize the workforce, and provide sustained expert support.
Employees are ready to adopt AI. Their organizations are not.
McKinsey’s July 2026 research found that 70% of respondents felt personally ready to use AI, but only 27% of leaders believed their organizations were ready for the people, workflow, and cultural shifts ahead. (McKinsey, 2026)
Cost and technology access matter, but they are rarely the only barriers. For many organizations, the harder problems are leadership alignment, workforce readiness, and sustained expertise. The result is predictable: pilots that never scale, decisions that repeatedly reopen, and ROI that remains elusive.
Building the AI Operating Model
Organizations need a repeatable way to make AI investment decisions, modernize how work gets done, and support employees as technology evolves. That requires an AI Operating Model composed of three connected capabilities: leadership alignment, workforce modernization, and an accessible AI Center of Excellence (AI CoE).
Step 1. Demystify AI and Align the Leadership
Gartner found that 66% of CEOs said their business models were not fit for AI purposes, while only 44% considered their CIOs AI-savvy. The gap is larger than technical knowledge. When executives cannot agree on where AI can create value teams pursue learning activities instead of results. The outcome is not cautious adoption. It is expensive tinkering. (Gartner, 2025)
Executive Action: Define the AI investment thesis.
Before funding pilots, the leadership team should agree on:
- A shared language for AI capabilities, models, and solutions to demystify and improve discussion value.
- The business outcomes AI is expected to improve, such as revenue, cost, risk, employee capacity, or customer experience.
- Clear boundaries for what the organization will buy and what it will build.
- The value streams with the greatest potential and the strongest connection to strategy.
- The measures that will determine whether to scale, redesign, or stop.
Leadership principle: Buy what is common. Build what differentiates.
Step 2. Supercharge the Workforce
Workforce modernization takes root when people realize their personal power to improve their own work quality, their team’s environment, and the organization’s overall results. Gartner reports that versatilists—employees who combine technical depth with cross-domain skills—represent 40% to 60% of high-performing AI teams. (Gartner, 2026)
Executive Action: Enable your team with training and co-development with experts.
Develop four capabilities through training plus practical work.
- The innovation mindset: The ability to question, challenge, and envision change. In practice that often means the ability to look at a workflow and ask why it exists at all.
- Process design skills: The ability to map the work, find the real bottleneck, and decide what to automate, what to augment, and where to place the points of human accountability.
- User-grade tool fluency: Not everyone needs to become an engineer. User-grade tool fluency is about building enough practical understanding to know what the tools can do, where they break, and how they change the work.
- Governance: Translate business decisions into clear accountability, safeguards, and technical guardrails.
Leadership principle: AI advantage compounds when the people closest to the work are empowered to redesign it.
Assign a business leader to own a redesigned workflow and its results. Then create a measurable initial success:
- Map the current end-to-end process.
- Identify repetitive work, bottlenecks, and judgment-intensive decisions.
- Redesign the roles of AI and people.
- Co-develop the workflow with experts and the employees who will use it.
- Measure adoption, cycle time, quality, capacity released, and business results.
Step 3. Sustain Momentum with Expert Support
AI adoption does not end when a solution is launched. Platforms, licensing models, security requirements, and technical capabilities continue to change. For example, in June and July 2026, Microsoft introduced major changes to how Copilot Studio agents are built, connected to business information, governed, and monitored. (Microsoft, 2026)
Each advance creates opportunity, but it also creates decisions about architecture, cost, security, licensing, and platform dependence. Without dedicated expertise, every major change can reopen decisions the organization thought it had already made.
Executive Action: Establish an accessible AI Center of Excellence.
The AI CoE should provide three essential functions:
- Teach: Training, office hours, troubleshooting, and adoption support.
- Design: Support for workflow redesign, solution architecture. Offer development and co-development options.
- Govern: Security, permissions, licensing, risk controls, management, and reusable standards.
Leadership principle: Whether internal, outsourced, or blended, treat the AI CoE as a shared service to maximize impact and value.
Achieve AI Self-Sufficiency
AI hesitation is rarely caused by a lack of interest. It is prolonged by unresolved leadership decisions, teams that cannot yet redesign work, and insufficient expert support.
A practical AI Operating Model addresses these issues and turns scattered pilots into better investment decisions and repeatable business value. The resulting success breads confidence and momentum as an agreed and internalized AI Operating Model that allows the organization to take control – to decide, learn, leverage, and evolve.
Related rGen Services
rGen’s AI Executive Workshop & Planning engagement helps leadership teams demystify AI, establish priorities, set build-versus-buy criteria, assign ownership, and translate interest into a focused, value-based roadmap.
rGen’s AI Workforce Modernization program helps teams build the innovation, process design, and technical skills required through training and co-development of a practical AI solution that fills a genuine business need.
rGen’s AI Center of Excellence on Demand provides office hours, solution reviews, governance guidance, reusable standards, development and co-development, and monitoring of material platform changes on demand without requiring organizations to build the entire function internally. It is a cost-effective alternative to building an in-house team.
If you want to ensure your AI Operating Model produces measurable impact, contact ray.rasmussen@rgenconsulting.
Sources
McKinsey & Company. “From adoption to impact: Three horizons of AI transformation.” July 8, 2026. View source.
Gartner. “Gartner Survey Reveals That CEOs Believe Their Executive Teams Lack AI Savviness.” May 6, 2025. View source.
Microsoft. “What’s new in Copilot Studio.” Updated August 20, 2026. View source.
Gartner. “AI Talent Gaps Stall Enterprise Value: 4 CIO Blind Spots.” June 1, 2026. View source.
