For the past few years, much of the conversation around AI has focused on adoption. Companies have experimented with new tools, launched pilots and looked for ways to introduce AI across their organizations. For technology leaders, there has been understandable pressure to move quickly as capabilities advance and competitors invest.
In 2026, that conversation is changing. The question is becoming less about whether companies will use AI and more about how they turn that investment into meaningful business value.
Moving too slowly carries a real risk of falling behind. But moving quickly without the right strategy, infrastructure or talent creates a different challenge: organizations can invest significant time and resources into AI without a clear path to scale or measurable results.
The next phase of AI will require companies to find the balance.
AI adoption is accelerating. Scaling it is harder.
Investment in AI continues to grow. Gartner reported in September that 85% of functional leaders plan to increase AI spending in 2026, yet only 22% of organizations have successfully scaled AI across multiple business units or adopted an AI-first approach.
That gap says a lot about where the market is today. Getting started with AI has become relatively easy. Organizations can introduce generative AI tools, test new applications and launch pilots faster than ever before. Scaling those efforts across an organization is different.
It requires companies to determine which use cases are worth pursuing, how AI fits into existing systems and workflows, what data it can access, how results will be measured and what risks need to be addressed along the way.
In other words, adoption alone is no longer the finish line.
Productivity gains are real, but enterprise value takes more work
There is already significant evidence that AI can make individuals more productive. McKinsey’s August 2026 State of AI research found that nearly nine in ten organizations now regularly use AI in at least one business function, and 80% of respondents said AI has improved their own productivity.
But the picture changes when companies look at organization-wide financial results. Only 37% of respondents said AI was contributing positively to enterprise EBIT, and just 6% of organizations qualified as AI high performers based on McKinsey’s measures of financial impact and value.
That creates an important distinction for technology leaders. Employees using AI is not the same as an organization successfully integrating AI into how the business operates.
The harder work begins when companies move beyond individual productivity and start redesigning processes, connecting AI to enterprise systems and applying it to clearly defined business problems. That is also where AI strategy starts to touch nearly every part of the technology organization.
The AI conversation is becoming a data conversation
One of the clearest examples is data. AI depends on information that is accurate, accessible and organized well enough to use, yet many organizations are discovering that their existing data environments were not designed for the scale and complexity of today’s AI ambitions.
Dun & Bradstreet surveyed 10,000 businesses in 2026 and found that more than three-quarters reported at least some measurable return from AI. At the same time, only 6% said their enterprise data was fully ready to support AI at scale.
That is a substantial disconnect. For an AI initiative to move beyond a pilot, organizations may first need to address fragmented data, inconsistent definitions, access issues, governance and overall data quality.
Those challenges cannot necessarily be solved by adding another AI tool. They require data engineers, architects, analytics professionals and technology leaders who understand how information moves through the organization and what needs to change to support AI at scale.
Cloud, infrastructure and security become part of the strategy
Data is only one piece of the foundation. As companies move from experimentation to production, their cloud and infrastructure environments also have to support larger and more complex workloads. That can raise new questions around architecture, performance, integrations, cost and scalability.
Cybersecurity becomes increasingly important as well. AI systems can introduce new ways for employees and applications to interact with sensitive business information, making access, permissions, privacy and governance even more critical.
These considerations are much easier to address at the beginning of an initiative than after it has already been deployed.
Gartner’s 2026 research reinforces the importance of that groundwork. Organizations reporting successful AI initiatives invest as much as four times more in foundational areas such as data quality, governance, AI-ready talent and change management than organizations experiencing poor results.
The takeaway is not that businesses should wait until every system is perfect before using AI. It is that the ability to move quickly and successfully often depends on how strong the foundation already is.
The talent behind AI extends far beyond AI specialists
The same principle applies to people. When companies think about building AI capabilities, the first roles that come to mind may be machine learning engineers, AI developers or data scientists. Those professionals are important, but enterprise AI requires a much broader mix of technology expertise.
Data engineers and architects build the environments that make reliable information available. Cloud and infrastructure professionals create systems that can support new workloads. Cybersecurity teams help manage access, governance and risk, while software engineers connect AI capabilities to existing applications and workflows.
Product managers, project leaders and business analysts also play an important role by helping organizations identify which problems are worth solving and how success should be measured. Technology executives then have to connect all of those pieces to the larger business strategy.
This workforce piece is becoming increasingly important. Deloitte’s 2026 State of AI in the Enterprise research found that insufficient workforce skills are now the biggest barrier organizations report when integrating AI into existing workflows.
That does not mean every company needs to build a large, dedicated AI team. It does mean organizations need to understand which capabilities they already have, where their gaps are and whether those gaps should be addressed through hiring, reskilling, project-based expertise or a combination of approaches.
More technology does not automatically create better outcomes
As AI tools become easier to access, technology leaders may also need to become more selective. Not every pilot needs to scale, and not every new AI capability needs to be implemented simply because it is available. Most importantly, not every business problem needs an AI solution.
The organizations getting the most value from AI appear to be taking a more disciplined approach. McKinsey found that AI high performers are more likely to redesign workflows rather than simply layer AI onto existing processes. They are also more likely to have clear processes for measuring the impact of their investments.
Gartner’s latest research points in a similar direction, with organizations seeing stronger results more consistently measuring ROI and redirecting resources away from initiatives that are not delivering.
That is an important evolution in the AI market. The goal is not to slow innovation down. It is to make sure innovation is connected to something the business is actually trying to accomplish.
A successful AI initiative should ultimately improve an outcome, whether that means increasing productivity, improving decision-making, reducing costs, creating revenue, strengthening customer experience or enabling people to work differently. Without that connection, experimentation can quickly become another technology expense.
Building for what comes next
AI will continue to move quickly, and technology leaders will not have the luxury of waiting for the market to settle before making decisions. But the pressure to keep pace does not have to mean pursuing every opportunity at once.
In many cases, the organizations best positioned for the next phase of AI will be those willing to strengthen the capabilities surrounding it. That could mean improving data architecture before scaling a new application, modernizing cloud infrastructure, addressing a cybersecurity gap or bringing in specialized expertise for a specific implementation. It could also mean looking closely at a pilot that is not producing enough value and deciding not to scale it.
These are technology decisions, but increasingly, they are workforce decisions too.
At Brilliant, our Technology Staffing team works with organizations across software engineering, data and analytics, cloud, infrastructure and cybersecurity, product and project management and technology leadership. As AI becomes more deeply connected to the broader technology environment, the skills required to support it are becoming increasingly interconnected as well.
The next phase of AI will not be defined simply by which organizations adopt the most technology. It will be defined by which organizations can identify the right opportunities, build the foundation to support them and bring together the people needed to turn AI from experimentation into measurable business value.
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Sources: Gartner, 2026; McKinsey & Company, 2026; Deloitte, 2026; Dun & Bradstreet, 2026.