Past the Hype: Five Ways High-Performing Organizations Approach AI Differently
By Paul Patterson, CTO at Naviri Group
Companies are moving past the AI hype cycle and turning their focus to how AI can bring real value to their organizations.
Recent surveys from McKinsey and Forrester suggest we're moving past the AI hype cycle. In 2026, companies are starting to take a more pragmatic approach toward AI use, shifting from “Should we use AI?” to “How can AI bring measurable value to our organization?”
The McKinsey survey found that most organizations are still experimenting with AI, with almost 70% of respondents reporting that their organizations have yet to meaningfully scale AI. Despite this, 64% say AI is already enabling innovation.
But even though early efforts show promise, promise isn't the same as profit. Only 39% of organizations in the McKinsey survey report any EBIT impact from AI. Forrester's data is even starker, with just 15% of decision-makers reporting EBITDA lift in the past year.
The disconnect between AI's promise and its bottom-line impact is forcing companies to get serious about proving real impact. Forrester predicts that leadership teams, seeking more predictable ROI, will defer 25% of planned AI spend to 2027.
So how do you move past AI experimentation and start seeing real company-wide impact? To answer this question, let’s start with an overview of what AI use looks like today for most organizations. Then we’ll cover what separates the organizations seeing real returns from everyone else.
Most Organizations Are Still Finding Their Way
As mentioned above, McKinsey reports that most companies have yet to scale AI across the organization. Only 20% report using AI across five or more functions, and the majority of the companies who have scaled AI to this extent are larger organizations.
In addition, certain functions and industries are moving faster than others. IT departments lead the charge, driven largely by infrastructure needs. AI agent adoption is progressing most quickly in functions like knowledge management, marketing, and sales.
And, while bottom-line impact at the company level is still rare, software engineering, manufacturing, and IT are most likely to report measurable cost reductions. Among industries, we’re seeing that technology, media, healthcare, and telecommunications are adopting and scaling AI faster than other industries.
Another key finding is that most companies are using AI primarily to increase efficiency. Four out of five organizations cite cost reduction or productivity gains as their primary AI objective. This work tends to focus on augmenting existing processes: automating customer support, assisting with marketing content, or streamlining IT help desks.
High-Performing Organizations Do AI Differently
Now that we understand how most organizations are implementing AI, let’s look at the high performers. High performers are defined by McKinsey as those who “attribute EBIT impact of 5 percent or more to AI use and say their organization has seen ‘significant’ value from AI use.” They make up only 6% of respondents.
So what are these high performers doing differently? They’re after transformation. Like most organizations, they’re using AI for efficiency gains. But they’re also prioritizing growth and innovation, scaling AI across their organization faster, integrating it into their workflows, and demanding more than just increased efficiency and cost reductions.
Here are five things high performers are doing differently:
They're asking the right questions. Most companies are still asking, "Where can AI make us more efficient?” High performers ask: "How can AI help us do things we couldn't do before?" The data backs this up: High performers are 3.6x more likely to report using AI for “transformative change.” They’re also far more likely to set goals for growth and innovation in addition to efficiency.
They're redesigning work, not automating it. High performers are almost three times more likely to fundamentally redesign their workflows rather than bolt AI onto existing processes. This is one of the strongest predictors of success. It means they're rethinking how work gets done, not just speeding up old processes.
They have leadership buy-in that goes beyond budgets. High performers are three times more likely to “strongly agree” that their senior leadership actively demonstrates commitment to AI initiatives. These leaders are engaged, using AI themselves, and driving adoption from the top. They also have taken the time to clearly define and communicate how and when humans still need to be in the loop.
They're scaling agents, not just piloting them. In most functions, high performers are at least three times more likely to have moved AI agents from experimentation to production. They’re also scaling AI across more functions, from sales and marketing to product and service development to finance and corporate strategy.
They’re putting their money where their mouth is. High performers are 4.9x as likely as other organizations to report spending 20% of their digital budgets on AI. That’s 35% of high performers vs. only 7% of other organizations.
The Path Forward
The lesson from the data is clear: The organizations seeing real returns aren't waiting for perfect. They're asking the right questions, revamping workflows, promoting AI use from leadership on down, pushing past the experimentation phase, and significantly investing in AI.
AI deserves the same rigor you'd apply to any major investment. Ask where it creates a competitive advantage, not just where it can be deployed. Focus on measurable business outcomes, not pilot projects that never scale.
If you’re not seeing the impact from AI that you hoped for, 2026 is the year to redefine what success looks like and build a realistic path to get there. Want to discuss how to move your organization from experimentation to impact? Schedule a consultation with us.