What Would It Look Like If Your Workforce Actually Collaborated With AI?

100% of organizations using AI means very little if the quality of that use is not contributing to a competitive advantage. AI adoption, a common AI performance measure, does not reflect a workforce capability or true AI readiness.

There is a conversation happening in boardrooms and executive offsites right now that sounds like progress. Leaders are reporting AI rollout numbers, dashboard metrics, and licensing spend. Sure 95% of the workforce are using AI to schedule meetings more efficiently, summarize email threads, and generate first drafts of documents they could have written themselves. This also means that only 5% of the workforce are utilizing AI to bring outcomes that would be considered by definition as a competitive advantage. 

I have spent nearly two decades leading complex enterprise business transformations, not as an outside observer, but embedded in the infrastructure of how change actually reaches people. I have been in the rooms where strategy gets designed and the anticipated momentum in progress stalls at the typical 3-5 months post implementation and being assigned to define the root causes and realign teams and executive focus. In the scope of AI investments for transformations, the gap between executive intent and organizational execution widens due to the assumption of what measures determine application success. How executives set directives for collaborative use of AI for role augmentation, clarify business outcomes that serve as key drivers to build AI capabilities and measure the right areas to determine AI ROI will make the competitive difference. As your executive partner, here are three executive questions that I advise you to answer to expand what it could look like for enterprise-wide impact with AI being a collaborative workforce solution instead of just a technology tool. 

Executive Question 1: What are you actually encouraging your workforce to use AI for?

The direction you give your teams about AI will determine the ceiling of its value. And right now, most organizations are inadvertently setting that ceiling very low.

When the primary message to employees is "use AI to be more efficient," efficiency becomes the only outcome. Teams default to task-level improvements because that is what they were pointed toward. Improving small non-complex tasks to bring faster outputs, shorter timeline brings “efficiency” but not necessarily effectiveness or optimal capability use by the employee.

I am currently working with an enterprise that is scaling the integration of a major AI productivity platform across their workforce. The organizational message has been efficiency-forward. And the results reflect exactly that. Teams are using the tool to complete familiar tasks a little faster. They are not using it to think differently about those tasks, to reframe a customer problem, to identify a process risk earlier, or to stress-test a strategic decision before leaders communicate it in executive briefings. l

When you reframe AI as a collaborator rather than an assistant, you change how people engage with it. A collaborator gets brought into complex problems. An assistant gets handed simple ones.

Before your next leadership review or advising meetings with your executive team, ask “What specifically are we encouraging our people to do with AI, and does that behavior map to the outcomes we actually want to lead in?”. The quality of the answers provided in these reviews will help you strengthen the quality of how AI use is being leveraged. 

Executive Question 2: Are your teams capable of giving AI what it needs to return competitive results?

AI does not improve the quality of what it receives, it accelerates the quality of what it’s being prompted to produce. One of the most important and under-discussed realities of enterprise AI adoption is that the tool is only as effective as the capability of the person using it. Now let's not confuse AI capability and role capability so allow me to clarify. If a team member cannot clearly define a business problem, AI cannot define it for them even though they have viable experience in their role and ability to lead innovations from where they are. If a team does not understand the strategic outcome they are working toward, AI will produce confident, well-formatted work in the wrong direction.

AI believes the quality of the information it is given is sufficient. It does not push back on a poorly framed prompt. It does not flag a misaligned objective. It produces results based on what it receives, and those results will look polished and reasonable whether the underlying input was sound or not. That is a risk most organizations are not accounting for in how they measure AI performance.

Building human-AI capability means investing in the human side of that equation. It means developing your workforce's ability to think critically about what they are asking AI to do, to evaluate the output they receive rather than accept it, and to iterate on both the input and the result until it actually reflects the business outcome they are pursuing.

This is where I see organizational capabilities being a must in the executive agenda in the same strategic priority as the deployment of AI itself. When those two investments are not aligned, organizations end up with AI that is highly utilized while also generating very little of the competitive differentiation that justified the spend.

Executive Question 3. Who is measuring the right things, and does that group include the right people?

The current AI performance conversation inside most enterprises happen between the CIO and the CFO. Yes, these conversations are critical. Security, integration, cost management, and application performance are legitimate measures. But they do not tell you whether AI is actually making your organization more capable.

Cost tells you what you spent. Integration tells you what got connected. Security tells you what is protected. None of those measures tell you whether a team is solving harder problems than they were six months ago. None of them tell you whether your frontline leaders are making better decisions under pressure. None of them tell you whether the workforce that is closest to your customers, your operations, and your risk is actually more capable because of AI accessibility. 

The metrics that matter for competitive advantage are the ones that measure role augmentation. It is key to expand how employees view AI as a collaborator. It is also key for executives to account for the real fears and behavioral responses their workforce can have that limit how a role leverages AI. Is your workforce able to address more complex problems with the same headcount? Are teams identifying risks and opportunities earlier than before? Are the decisions that reach your executive table more informed, more tested, and more grounded in cross-functional context?

Getting to those answers requires expanding who is in the AI performance conversation. Chief Human Resources Officers, Chief Transformation Officers, Operations leaders, and the people closest to frontline execution all carry perspectives that the technology and finance lens cannot provide. Until those voices are at the table, AI performance will continue to be measured by what it costs and how it connects, usage at the surface level and not by what it helps the workforce to build as individual contributors.

The organizations that will define the next era of competitive performance are building the infrastructure that determines what AI can do inside their specific organization, with their specific people, toward their specific outcomes.

Organizational readiness and AI readiness require leadership alignment on what AI collaboration actually looks like in practice. It requires a workforce that is developed beyond how to use the technology to engage with AI as a thinking partner, not a shortcut for small task completions. And it requires a performance framework that measures what is actually growing, not just what is running.

Article written by:

Domonique Townsend is the CEO and Executive Partner of We Optimize Work (WOW), a consulting firm specializing in enterprise AI readiness transformation, organizational readiness, and human capability development. She is a Lean Six Sigma Master Black Belt, TEDx speaker, and the creator of the WARD™, an AI Readiness and ROI diagnostic designed to help executives measure the readiness of their workforce, cost of inaction, operating model, change capability, and organizational readiness culture to absorb AI and generate sustained value from it.  Learn more about We Optimize Work and how they can help you with AI readiness here: We Optimize Work