AI Workforce Strategy Has Entered Its Next Phase

At its recent AI Workforce Forum, Acquis brought together executives from HR, R&D, portfolio strategy, legal, commercial, and technology functions across life sciences, healthcare, technology, and consumer products. The conversation reflected a shift taking place across industries. Organizations have moved quickly to introduce AI, but many are still working through the business decisions that will determine whether those investments pay off.

Acquis structured the afternoon around the four stages of its AI Workforce Navigator: reshaping the work and the organization, securing the people and capabilities, driving adoption and performance, and governing the change and proving its value. At each stage, leaders assessed where their organizations stand today, considered the tradeoffs they face, and took a position on the decisions ahead.

Several themes emerged from those discussions. Together, they point to a new set of priorities for leaders responsible for translating AI investment into business performance.

AI investment is outpacing the ability to prove its value

Organizations are training employees and rolling out tools faster than they are developing the measures to evaluate their impact. Most attendees shared they currently track time and cost savings because those figures are relatively easy to quantify and tend to be the first measures executives ask about. But when the discussion turned to what those numbers had revealed about business performance, few leaders could point to a clear answer.

Part of the challenge is that credible evidence takes time to establish. One pharmaceutical R&D leader described using AI to shorten clinical trial enrollment. An initial successful study is encouraging, but demonstrating that AI consistently reduces enrollment time requires repeated results across comparable studies and disease areas. That evidence is still developing.

For leaders, this creates an important distinction between progress in adoption and progress toward outcomes. Training completion and tool usage can show that an organization is moving, but they cannot establish the value of that movement. Organizations need a separate set of outcome measures, defined before investments scale, to understand whether AI is delivering on its promise.

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The value of AI may be growth, not headcount reduction

When asked what their organizations should do with the capacity AI creates, most leaders favored reinvesting it in the business over capturing it as cost savings.

Leaders agreed a stronger business can come from new revenue and better service as well as lower operating costs. Several described using AI to support growth without expanding headcount. One executive at a growing company explained how she framed the approach to her employees: "not looking to cut, looking to not add."

However, there are situations where direct savings remain the priority. A contact center automation initiative, for example, may have a specific cost-reduction target attached to its business case. Leaders also emphasized that AI brings its own costs, which can increase as usage expands. Any savings calculation needs to account for that investment.

The same thinking extends to talent strategy. The majority of leaders favored upskilling existing employees over hiring externally. One software executive explained that he could teach an experienced industry expert to build an AI agent much faster than he could teach an AI specialist the nuances of his industry.

AI strategy needs leadership direction and room for experimentation

The group agreed that successful AI adoption requires a combination of top-down direction and bottom-up experimentation. Leadership needs to identify the workflows where AI can deliver the greatest business value and focus investment accordingly. These strategic bets create clearer accountability and give organizations a way to concentrate resources where they matter most.

At the same time, broad access to AI tools helps employees become comfortable with the technology and surfaces opportunities that executives may not have identified themselves. Ready-made use cases can accelerate that process. As one leader shared, one well-developed application for a high-volume role, such as store managers, can create time savings across hundreds of employees.

The appropriate balance of autonomy and oversight produced meaningful differences of opinion. While most leaders representing healthcare and life sciences favored strict human oversight of AI agents, others cautioned that excessive governance can slow progress. When policies make experimentation difficult, employees may turn to personal AI accounts or abandon the tools altogether. Governance needs to reflect the organization's risk exposure while leaving room for responsible experimentation. That flexibility also depends on employees having the judgment to question AI outputs and recognize where additional scrutiny is necessary. One leader compared AI to a capable intern: its work can be valuable, but someone still needs to review it.

AI should be measured by the performance it enables

For most organizations represented at the forum, AI has yet to become a meaningful part of how employees are evaluated or rewarded. Leaders agreed that this needs to change, with an important caveat: performance should reflect the outcomes AI helps people achieve, not how frequently they use it.

One executive explained that he deliberately keeps AI usage out of performance reviews because AI "is an enabler" and "not a goal unto itself." Frequent use of a tool does not necessarily indicate strong performance. What matters is whether employees are using AI to improve the work they deliver.

The same principle applies at the organizational level. AI performance measures should connect to business outcomes leaders already track, including cycle time, cost, accuracy, and the pace of bringing new ideas to market. Usage dashboards can help organizations understand adoption, but they offer limited insight into whether AI is improving business performance.

Five questions leaders need to answer

The discussions revealed a gap between introducing AI into the workforce and rethinking how the workforce operates. When AI changes how work gets done, leaders also need to reconsider where human expertise adds the most value, how employees spend the capacity AI creates, and what the business should expect in return. These decisions cannot happen in isolation.

For organizations working through these decisions, five questions deserve attention:

What does the work become, and who does it?

Decide, work area by work area, what AI performs, what it supports, and what stays with people. Then redesign the roles around that work, decide who fills them, starting from inside the organization where possible, and prepare managers to lead teams where people and AI share the work.

What does success look like before we scale?

Define a focused set of outcome measures for each AI investment. These might include time to market, operating cost, accuracy, or new ideas brought to market. Establish a baseline and agree on what level of improvement would justify further investment.

What happens to the time AI saves?

Decide whether the capacity AI creates will translate into cost savings or transformation capital to be reinvested in the business. If reinvestment is the priority, identify where that capacity will have the greatest impact. Consider which capabilities to develop internally, with particular attention to the judgment employees need to evaluate AI-generated work.

How much governance does your organization need?

Set oversight requirements that reflect the risks associated with your industry and intended use cases. Maintain the controls that protect the business while giving employees room to experiment responsibly. Revisit those requirements as technology and regulation evolve.

How will AI factor into employee performance?

Identify the business outcomes AI should help employees achieve in each role. Incorporate those expectations into performance goals and reviews, keeping the focus on the quality and impact of the work.

Turning AI experimentation into workforce strategy

Workforce performance changes when leaders make a set of decisions: which work AI takes on and which stays with people, what the roles around that work become and who fills them, where the freed capacity goes, and what people are measured and rewarded for. Adoption is not enough; leaders must produce evidence of value to determine whether the decisions are working. But those decisions will not hold for long. AI will keep changing the work faster than any one-time redesign can anticipate.

Acquis saw firsthand that leaders are no longer asking what AI can do. Instead, they are deciding what the workforce should become because of AI. Organizations must treat workforce strategy as a standing discipline, with a named owner, a regular review, and clear triggers for revisiting roles, oversight, and capacity as the technology moves.