Artificial intelligence is becoming a larger part of business strategy, and leaders are under increasing pressure to show results. Across industries, leaders are investing in AI to improve productivity, streamline operations, enhance customer experiences, and unlock new business opportunities. Yet as adoption accelerates, many organizations are discovering that AI ROI isn’t as straightforward as expected.
In fact, Insight Global’s 2026 AI Adoption Survey found that 89% of leaders have experienced an AI initiative that stalled at some point in the process. The challenge has moved from utilizing AI to translating AI activity into measurable business value.
There is no single definition of AI success. The outcomes a healthcare organization is pursuing may look very different from a technology company, manufacturer, or financial institution. As leaders work to scale AI across teams and workflows, they’re learning that AI ROI depends less on the technology itself and more on how well it aligns with business goals, people, and processes.
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How Companies Are Using AI
The conversation around AI has shifted significantly over the past few years. Organizations are no longer asking whether they should explore AI and are instead figuring out where it can create the most impact.
According to our survey, 83% of companies have already moved beyond the exploration phase and established some level of AI strategy or implementation. Meanwhile, Stanford’s 2026 AI Index found that organizational AI adoption has reached 88%, highlighting just how widespread enterprise AI has become.
What companies are doing with AI varies widely. But the goals tend to be remarkably consistent. Our research found that organizations most commonly measure AI success through:
- Time savings and productivity improvements
- Higher-quality outputs
- Better customer experiences
- Faster cycle times
- Reduced risk
- Revenue impact
- Cost reduction
Notice that those outcomes don’t point to a single use case. That’s because organizations are applying AI differently depending on their priorities.
Some teams are using AI to automate routine administrative work. Others are enhancing customer service, accelerating software development, improving knowledge management, supporting decision-making, or helping employees work more efficiently. More recently, organizations have also begun exploring agentic AI applications that can complete multi-step workflows with limited human intervention. However, many leaders are still evaluating where those use cases deliver the most meaningful business value.
Where the Major Challenges Are for Leaders With AI and ROI
If AI adoption is growing so quickly, why are so many leaders struggling to measure ROI?
The answer is that most organizations are navigating what many describe as the “messy middle” of AI adoption. They’ve moved beyond experimentation, but they’re still building the operational foundation needed to scale AI successfully.
Our survey revealed that organizations most often get stuck when they attempt to operationalize AI within the realities of the business. The biggest challenges leaders report include:
- Deploying AI into existing systems and workflows (29%)
- Managing compliance and accuracy concerns (29%)
- Measuring business impact and ROI (28%)
- Building or engineering AI solutions (28%)
- Driving organizational adoption (27%)
What’s notable is that few of these challenges are purely technical.
Research from IBM reinforces this trend, noting that organizations increasingly face challenges related to data readiness, governance, workflow integration, skills gaps, and proving business value.
Measuring ROI remains particularly difficult. While many leaders can point to productivity gains and positive employee experiences, connecting those improvements directly to financial outcomes is often more complicated. IBM reports that only about 29% of executives say they can confidently measure AI ROI today, despite many organizations seeing operational benefits from their AI investments.
That’s why comparing AI success across organizations can be misleading. A company focused on reducing customer service response times may measure success very differently than a company focused on accelerating product development or reducing operational risk. Both may be achieving strong ROI, but through entirely different outcomes.
What Successful Leaders Are Doing Right Now
The organizations seeing the most progress with AI aren’t necessarily deploying the most advanced technology. They’re creating clear connections between AI initiatives and business objectives.
One of the most effective strategies leaders are adopting is defining success before implementation begins.
Rather than launching AI because competitors are doing it, they’re identifying specific workflows, establishing baseline metrics, and deciding how success will be measured. Whether the goal is improving customer satisfaction, reducing cycle times, increasing productivity, or lowering operational risk, successful teams know what outcome they’re trying to achieve before they scale.
They’re also recognizing that AI is as much an organizational challenge as a technology one.
Microsoft’s 2026 Work Trend Index found that organizational factors such as culture, manager support, and talent practices account for more than twice the reported impact of AI compared to individual factors alone. In other words, the organizations generating meaningful value from AI are investing in more than software licenses. Leaders at these companies are:
- Training employees to work effectively with AI.
- Establishing AI governance practices early.
- Aligning business and technology teams around shared goals.
- Helping managers lead through change.
- Creating environments where employees understand how AI fits into day-to-day work.
The most successful leaders also take a practical approach to scaling. Rather than trying to transform every process at once, they focus on measurable use cases first, build momentum, learn from results, and expand from there.
AI success doesn’t happen because an organization adopts a tool. Ultimately, it happens when leaders create the conditions that allow that tool to deliver value.
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Turn AI Activity Into Measurable Business Value
As AI adoption continues to accelerate, the gap between implementation and impact will become one of the most important leadership challenges organizations face.
The companies making the most progress aren’t searching for a universal AI ROI benchmark. They’re defining what success means for their business, aligning AI initiatives to meaningful outcomes, and building the people, processes, and governance needed to support long-term adoption. That’s why AI ROI looks different at every company.
At Insight Global, we help organizations connect AI strategy with execution through talent, consulting, and AI expertise. Whether you’re evaluating use cases, building an adoption strategy, developing workforce readiness, or scaling AI across the enterprise, having the right combination of people and expertise is critical to turning AI investments into measurable business value.
If your organization is ready to move beyond AI experimentation, connect with our team and start building an AI strategy designed for results.
by Celine Pham 
by Emilie Skaug 
