Workforce Readiness Is the Missing Link Between AI Investment and AI Returns

As AI moves from experiment to expectation, boards of directors and executive teams are identifying goals for AI to improve productivity, accelerate decisions, lower cost, and create better customer experiences. Yet for many organizations, the obstacle to realizing those benefits is whether their workforce is ready to use it with confidence, judgment, and discipline.

This Is Business Transformation, Not an IT Rollout

While the CIO may own the technology environment, security posture, and data foundation and the CHRO may own workforce strategy, skills, culture, and the employee experience, neither can solve the adoption challenge alone. AI changes how work is done, how decisions are made, and how people understand their contribution to value creation. That requires business leaders to align on the purpose and goals of AI adoption before they ask employees to change their behavior.

In practice, that alignment means two different conversations. The CIO owns the conditions for safe use: data readiness, tooling, access, monitoring, and the guardrails that let employees act without creating exposure. The CHRO owns the conditions for confident use: role impact analysis, manager capability, learning design, incentives, and honest communication about what AI does and does not change about people’s jobs. Adoption stalls when either leader treats the other’s work as a dependency rather than a shared objective benefiting from coordination and collaboration.

Readiness Fails Quietly Before ROI Fails Loudly

Companies that treat AI upskilling as a training rollout miss the larger need: that a change management effort is needed to drive meaningful adoption. SHRM’s 2025 Research Report^[1] finds that HR professionals have limited confidence in AI implementation success and apply change management practices unevenly. Across our own transformation engagements over the past 12 months, many clients believe their workforce is fully ready for AI, even as more companies embed AI into core business processes. AI tools are moving faster than workforce readiness.

The gap shows up in predictable ways:

• Employees worry that AI will replace their roles.
• Managers do not know what good AI use looks like.
• Teams receive generic training but no guidance on how AI changes their daily work.
• Policies exist, but employees do not know what is allowed, what requires review, and where human judgment must remain in the process.

Adoption then becomes uneven and unmeasurable, splitting the workforce into three groups: a small set of enthusiasts experimenting ahead of the guardrails, a cautious majority waiting for permission, and a quiet minority working around policy in ways that create real enterprise risk.

What is needed is a practical operating approach that connects leader goals for the adoption needed to deliver promised benefits, with structured change management, and training mapped to the business outcomes leaders want AI to achieve. Before launching another tool or pilot, or another contest or development goal, enterprise leaders should be able to answer four questions in plain language:

1. What business goal are we trying to improve?
2. Where should AI be used, and where not?
3. What will employees need to do differently?
4. How will we know whether adoption is creating value?

The cost of waiting rarely shows up on a dashboard. Licenses go unused, pilots stall before they impact any KPI, sensitive data moves through unsanctioned tools, and competitors who solved adoption first compound their advantage while your investment sits idle. MIT’s NANDA initiative revealed that in 2025 95% of 300 generative AI pilots assessed were failing to deliver value and they attributed that to the “learning gap” for both tools and organizations.^[2]

What We Have Seen Work in Practice

The first obstacle is typically ambition without structure. Our client, a national property restoration services company, was all in on adopting AI, but the organization needed a structured way to manage, prioritize, and scale AI responsibly. The work required executive alignment on specific goals, responsible AI guidelines, plain-language communications, AI education, change management, and a cross-functional model for decision-making and adoption. Within 90-days, we helped our client establish an AI Oversight Committee with governance, owners, and priorities, including a stage gate process to expedite decisions and progress of AI pilots.

The second obstacle is speed. In another engagement, a franchise-based service organization needed to move an AI-powered call center pilot from vendor selection to live operations on an accelerated timeline. The adoption work mattered as much as the technical integration, because call workflows for frontline operators and franchise field teams would change dramatically. We developed role-based workflows and scripts, release notes, and office hours for hyper-care and feedback loops to make the pilot usable in their real operating environment. Speed came from disciplined execution, but confidence came from the support provided to those teams. The pilot went live in less than eight weeks, enabling 24/7 customer support for at-risk accounts at scale for the first time in the organization’s history.

The third obstacle is mistrust: If users cannot trust the message, they will not trust the change. Our client, a large research enterprise undergoing digital transformation, faced stakeholder resistance rooted in broken promises and communication challenges. Ankura helped develop stakeholder profiles, targeted messaging that openly addressed prior missteps, and an AI-powered communication capability that allowed change teams to scale audience-specific messaging. Leaders could then answer the different questions and concerns raised by each audience, and the head of clinical operations reported strong enthusiasm for agentic AI applications for the first time leading to renewed collaboration with IT and other functions.

A Practical Playbook for Moving From Fear to Fluency

For C-suite leaders, the practical playbook has seven steps:

1. Align the leadership team on the business purpose of AI and the measures that matter.
2. Assess workforce readiness, including literacy, perceived risk, role impact, and manager capability. 
3. Translate AI goals into specific, role-based behaviors, so employees know how AI should change their work. 
4. Develop plain-language guidance on acceptable use, human review, privacy, and decision rights, grounding that guidance in the real examples surfaced by the readiness assessment in step two.
5. Deliver training using real workflows rather than abstract tool demonstrations, with an I-Do / We-Do / You-Do hands-on training model.
6. Support adoption through influential champions, office hours, feedback channels (ideally anonymous), and leader reinforcement. 
7. Measure adoption by behavior and its related business impact, not by attendance alone.

Follow this playbook and workforce readiness becomes the bridge between AI investment and AI return. Aligned leaders provide direction. Change management builds awareness. Training builds confidence. Governance builds trust. Measurement proves whether AI is improving the work that matters. When those pieces are designed together, employees understand the why of AI, managers can reinforce the how, and leaders can see what is changing as the organization moves from experimentation to measurable adoption, building value and competitive advantage.

If your organization is trying to scale AI, we can help you move your workforce from experimentation to confident use. We would welcome a conversation about your experience and overall readiness.

For more information, please review part one of this series: https://ankura.com/insights/upfront-ai-governance-accelerates-value-do-not-bolt-it-on-like-a-brake

Sources

1. SHRM – From Adoption to Empowerment: Shaping the AI-Driven Workforce of Tomorrow, SHRM, 2025.
2. Fortune 2025; The GenAI Divide: State of AI in Business 2025, as accessed via Yahoo! Finance.

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