How to Increase AI Adoption Across Your Workforce: 7 Evidence-Based Steps

Last updated September 29, 2026.

In organizations that give employees access to AI tools, fewer than half of individual contributors use AI a few times a week or more, compared with about 2 in 3 senior leaders, according to Gallup's February 2026 survey of 23,717 US employees.

To increase AI adoption across your workforce, identify the barriers holding back each employee group, embed approved AI tools in everyday workflows, equip managers to model and support use, set clear guardrails, and test targeted interventions against a control group before scaling them. The programs that work best are built around role-specific use cases instead of one training course for everyone.

At The Decision Lab, this is the approach behind our AI adoption work. A Fortune 500 HR technology company we worked with had deployed AI tools across its 20,000-person workforce with training and internal communications behind them, and even though the tools worked and employees knew about them, usage stayed low. The obstacles were behavioral: employees weighed the effort of changing a workflow against a payoff they could not yet see, and many were unsure whether they could trust AI output or where it fit in their job. The seven steps below come from that engagement and from the most recent research on employee AI adoption, and we make them measurable with our SPROUT framework, validated with data from more than 20,000 employees.

Gallup's research on what separates AI adopters from holdouts asked employees who had access to AI but rarely or never used it what was stopping them. Nearly half of non-users said they would rather keep working the way they already do. About 2 in 5 raised concerns about data privacy, security or compliance, and a similar share said they were ethically opposed to AI or did not believe it could help with their kind of work. These barriers to AI adoption have little to do with skill: only about 1 in 6 non-users said they did not feel prepared to use AI effectively, even though preparation is the gap most AI adoption training is designed to close.

We found the same pattern in our Fortune 500 engagement. By combining a 48-source review of the adoption literature with interviews and surveys across the workforce, we identified five behavioral barriers: time constraints, poor workflow integration, unclear usage guidelines, low trust in AI accuracy and perceived complexity. We cover each one in our guide to behavioral barriers to enterprise AI adoption.

The same research showed two groups of employees failing in opposite ways. Enthusiasts were already experimenting with AI and kept running into friction, while skeptics distrusted its value and stalled at first contact. A single company-wide program aimed at both groups is likely to miss each of them.

The steps below follow the order we usually work in, and organizations that have already done some of them can start further down the list. Each step ends with a short implementation format you can copy, and the examples in those formats are hypothetical and do not describe client work.

1. Diagnose barriers to AI adoption for each group

Usage dashboards show who logs in, but they cannot show why everyone else does not. We start every engagement by measuring the conditions behind use with our SPROUT framework, and it scores five dimensions:

1. Social Readiness: whether team dynamics and peer norms make AI use acceptable.
2. Psychological Readiness: how open individuals feel toward AI, including whether they fear being replaced.
3. Role Fit: whether AI feels relevant and useful for day-to-day work.
4. Organizational Enablement: whether policies, leadership, incentives and culture support AI use.
5. Understanding and Technical Capability: whether employees have the confidence and skills to use AI well.

When scores differ sharply between teams, the cause is usually local, such as one manager or one workflow, and a company-wide program will not reach it. Our AI adoption readiness assessment guide explains how to read the results.

What to do: Survey each team on the five dimensions and break the results down by role and manager before choosing any intervention.

Example: A finance team scores well on technical capability but low on role fit, so the next step is finding a use case in their monthly close before offering any more tool tutorials.

What to measure: The share of employees in each team who use AI at least a few times a week, recorded alongside the dimension scores as a baseline.

2. Segment employees by why they are not using AI

Gallup's data shows why segmentation matters. Among employees with access to AI, non-users were far more likely than infrequent users to say they were ethically opposed to it, at about 2 in 5 compared with 1 in 4. They were also nearly twice as likely to say AI could not help with their work. People who have never started need to see a credible, acceptable use in their own role, while people who tried AI and drifted away usually need friction removed from tasks they already want to use it for.

In the Fortune 500 workforce, we designed 20 candidate programs, each built to clear a specific barrier for a specific segment of employees, instead of one program for everyone.

What to do: Split employees into at least two groups, people who have never used AI at work and people who tried it but use it rarely, and plan a separate first intervention for each.

Example: Non-users in a claims team watch AI work through one of their own cases with the output checked on screen, while occasional users get the tool added to the claims system so they no longer need a separate login.

What to measure: Movement between groups over time, counting non-users who try AI at least once and occasional users who become frequent users.

3. Embed AI in the workflows people already use

Workflow fit had the strongest link to usage in Gallup's survey. Among employees with access to AI, nearly 9 in 10 of those who strongly agreed that their organization's AI tools integrate well with their existing systems and processes used AI a few times a week or more, compared with just over half of those who did not strongly agree. Gallup reports these as associations from survey responses, so they show where to focus without proving what causes what.

What to do: Identify one recurring task per role where AI replaces or shortens an existing step, and build the approved tool and prompt into that step.

Example: Recruiters get an approved AI template for summarizing interview notes inside the applicant-tracking system they already use.

What to measure: Weekly active use for that task, repeat use after four weeks, time saved per task and employee confidence using AI for it.

4. Equip managers to model and support AI use

Manager support showed the second-largest gap in the same survey. About 4 in 5 employees who strongly agreed that their manager actively supports their team's AI use were frequent users, compared with fewer than half of those who did not strongly agree. Gallup's State of the Global Workplace 2026 report found that fewer than 1 in 3 US employees in organizations implementing AI strongly agree that their manager gives that support.

Manager support matters partly because visible AI use carries a social cost. In a PNAS study of the social evaluation penalty for using AI, Jessica Reif, Richard Larrick and Jack Soll ran four preregistered experiments with more than 4,400 participants and found that people who use AI at work expect to be judged as less competent and less motivated, and that others do judge them that way.

Crediting good AI-assisted work in performance reviews changes the incentives directly, since an employee who expects AI-assisted work to count for less has a sound reason to hide it or skip it.

What to do: Give each manager a short brief with two team-specific use cases, a standing meeting slot for sharing AI results, a protected hour of learning time each week and guidance on crediting AI-assisted work in reviews.

Example: A sales manager opens each weekly pipeline meeting by showing how she used AI to draft one account plan, and asks one team member to do the same the following week.

What to measure: The share of employees who strongly agree their manager actively supports their team's AI use, tracked team by team against each team's frequent-use rate.

5. Set clear guardrails and make experimenting safe

About 2 in 3 employees who strongly agreed that their organization has clear guidelines for using AI safely and securely were frequent users, compared with fewer than half of those who did not. The gap was larger for experimentation: nearly 3 in 4 employees who strongly agreed that their organization supports them experimenting with AI were frequent users, compared with fewer than half of the rest.

Unclear usage guidelines were also one of the five barriers we found at the Fortune 500 company. Employees who cannot tell what is allowed tend to either avoid AI or use it out of sight, a pattern we describe in our guide to why employees resist AI tools.

What to do: Publish a one-page, role-by-role list of approved tools and the kinds of data allowed in each, plus a small set of practice tasks where a mistake carries no penalty.

Example: A legal team gets an approved tool for summarizing published case law, with a clear rule that client documents stay out of it until a secure version is available.

What to measure: The share of employees who say the rules on AI use are clear, and whether questions to the help desk about what is allowed fall over time.

6. Start with the tasks where AI helps most

The gains from AI are uneven across people. Erik Brynjolfsson, Danielle Li and Lindsey Raymond studied 5,179 customer support agents who were given an AI assistant, in a study published in the Quarterly Journal of Economics in 2025. Issues resolved per hour rose 14% on average compared with agents who did not yet have the tool, and by about a third for novice and lower-skilled agents, with little change for the most experienced and skilled agents.

For adoption, this means choosing first use cases role by role. Newer employees are likely to see the clearest early gains, so they make good early adopters within a team. For experienced staff, a better starting point is probably the routine work they find tedious, since AI adds less to the tasks they already do best.

What to do: Rank candidate tasks in each role by how often they recur and how easily someone can check the AI output, then launch the top one or two.

Example: A customer support team starts newer agents on AI-suggested replies to common questions, and offers senior agents AI help with after-call summaries.

What to measure: An output measure the team already tracks, such as tickets resolved per hour, compared with a similar team that has not started.

7. Pilot against a control group, then scale what works

In the Fortune 500 engagement, we used structured, evidence-based criteria to choose eight of the 20 candidate programs. We piloted them for one month with more than 100 employees against a control group, and the programs ranged from hands-on tasks embedded in real workflows to a challenge series run in Slack. Before and after the pilot, we measured how easy employees found AI to use and how confident they felt exploring it, and we also tracked the time it took them to use AI. Confidence in exploring AI tools rose 41% in the pilot group and fell 26% in the control group over the same period.

Without the control group, the fall among colleagues who received nothing would have gone unseen, and the pilot would have looked less effective than it was. Each program was handed over with the guides and support structures the client's teams need to run it themselves, and the programs are now scaling to the full 20,000-person workforce. The full engagement is described in our case study on increasing AI adoption across a 20,000-person workforce, and our guide to AI adoption training that changes behavior covers the training side of the same work.

What to do: Run a small number of interventions for a fixed period with one group while a similar group carries on as usual, and measure both groups before and after.

Example: Two sales regions take part in a four-week AI challenge series while two comparable regions carry on as normal.

What to measure: The difference between the two groups' changes in usage and confidence, instead of the pilot group's before-and-after change on its own.

The plan below gets a first controlled pilot running within a month. Results come once the pilot has run for its planned period, and in our Fortune 500 work the pilot itself lasted a month.

1. Week 1: Diagnose barriers to AI adoption team by team and role by role, and record a usage baseline for each team.
2. Week 2: Select two low-risk, high-value use cases with approved tools and clear data rules, and match each one to the group whose barrier it addresses.
3. Week 3: Brief the managers involved and build the approved tools and templates into the existing workflow for each use case.
4. Week 4: Launch manager-led pilots with the chosen groups, and set aside comparable teams that will continue as usual as a control group.

When the pilot period ends, compare each pilot group's change against its control group and scale only the interventions that moved usage.

We track AI adoption metrics in four layers, and each one answers a different question:

1. Access: who has an approved tool, a license and the permissions to use it.
2. Usage: who uses AI at least a few times a week, the threshold Gallup uses for frequent use.
3. Workflow adoption: who uses AI in a core task instead of only experimenting with it.
4. Readiness: whether employees feel confident, see AI as relevant to their role, trust its output and understand the rules.

Most organizations can report the first layer and part of the second. By Gallup's frequent-use threshold, about 3 in 10 US employees used AI a few times a week or more as of May 2026, according to its AI workplace indicator. Segment all four layers by team, role, manager, seniority and use case, since a company-wide average can hide a team where adoption has stalled.

Measure the same layers before and after every change, and against a group that did not receive it. Usage across a company can rise or fall for reasons that have nothing to do with the program, such as a new tool release or a policy change, and a control group separates the program's effect from those shifts.

Teams that want a quick starting point can use our AI Adoption Diagnostic, 17 anonymous questions that report results once at least five people on a team have responded. For organizations that want help designing and testing interventions, our AI adoption consulting team runs the full process described in this guide, from the first diagnosis through controlled pilots.

What is the fastest way to increase AI adoption in a company?

The fastest gains usually come from workflow fit and manager support, the two factors most closely linked with frequent use in Gallup's 2026 research. Put AI inside the tools where work already happens and tie it to specific recurring tasks. Before investing in more training, ask managers to use AI openly and to set expectations for their own teams.

Why don't employees use AI tools even after training?

Most AI adoption training explains what AI tools can do, while the reasons employees give for not using them are mostly about habit, privacy, ethics and relevance to their role. In Gallup's February 2026 survey, only about 1 in 6 non-users said they felt unprepared. Adoption rises when organizations address the specific barrier holding back each group.

What role do managers play in AI adoption?

Managers decide whether AI use feels expected and safe on their team. Employees who strongly agree their manager actively supports AI use are far more likely to use it frequently. Managers can also offset the reputational penalty of visible AI use by using it openly themselves and crediting AI-assisted work in performance reviews.

How do you encourage employees to use AI?

Start with a real task from each person's own job where the result is easy to check, so they can judge AI on work they understand. Make the rules on approved tools and data explicit, and have managers show their own AI use, since employees who expect to be judged for using AI tend to hide it or avoid it.

What is an AI adoption framework?

An AI adoption framework is a structured way to diagnose why employees do or do not use AI and to plan interventions around the answer. Our SPROUT framework scores five dimensions, from social readiness to technical capability, and we validated it with data from more than 20,000 employees.

What is AI change management?

AI change management is the work of shifting how people do their jobs once AI tools are in place, beyond installing the tools and announcing them. A behavioral approach starts by diagnosing why each group is not using AI. It then redesigns workflows and incentives around that diagnosis and tests each change against a control group before scaling it.

Which AI adoption metrics should you track?

Track frequent use, meaning a few times a week or more, and core-task use as separate numbers, because a team can use AI every week for side tasks while its main work stays unchanged. Add readiness measures such as confidence and trust in AI output, and report every metric by team instead of as one company-wide average.