Artificial intelligence has moved decisively into the center of business planning. In a recent AI Leaders Council webinar, Executive Director Neil Brown, and Board Advisor OJ Laos reviewed findings from the 2026 Corporate AI Outlook Study and reflected on what those results signal for executives navigating the year ahead. Their discussion offered a grounded look at how organizations are using AI today, where progress has stalled, and what leaders should prioritize next.
Most organizations sit in the middle of AI adoption
Survey data from more than 250 respondents across North America shows that broad, enterprise-wide AI deployment remains uncommon. Instead, most organizations describe themselves as operating in the middle stages of adoption. As Neil Brown explained, “Selective deployment across one or more areas is the most common state, followed closely by pilot or experimentation only activity.”
The AI Leader Council Adoption Stages™ framework groups adoption into four stages: absent, exploratory, operational, and embedded. Half of respondents fall into the operational category, meaning AI is in production in at least one or more business functions. Only a small percentage describe AI as fully embedded across their organization.
OJ Laos noted that these figures likely understate real usage. “I question some of the exploratory 22 percent, if they’re maybe using it in ways that they don’t know,” he said, pointing to the widespread availability of tools like Microsoft Copilot. Once access exists, adoption tends to follow quickly.
General-purpose tools lead, while targeted projects move more slowly
Much of today’s AI activity comes from general-purpose tools that employees can use throughout the workday. According to OJ, “That is the most obvious place where we’re seeing a lot of very quick adoption.” He shared that within his own organization, once licenses were available, “97 percent of users were leveraging the tools.”
More specialized AI initiatives, such as automating accounts payable or handling customer complaints, tend to progress at a slower pace. These efforts often deliver higher value, but they require tighter design, testing, and governance. As OJ put it, “The kind of juicier or higher-hanging value is going to come from likely more complex projects that are going to take more experimentation to understand.”
AI use clusters in IT, operations, and customer-facing roles
The study shows AI usage concentrated most heavily in IT infrastructure, data security, operations, and customer-facing functions such as sales and marketing. This pattern did not surprise OJ “Most IT professionals probably think of AI as their biggest risk lever, biggest attack vector they’ve seen in some time,” he said, which naturally places them at the forefront of adoption.
Functions that demand absolute precision, including accounting and legal, tend to move more cautiously. OJ explained that roles with highly standardized processes are often easier to automate first, while specialized or regulated work requires greater care.
Development approaches favor cloud platforms and internal solutions
When it comes to how organizations build or implement AI, most rely on a mix of cloud-based services, vendors, and internal teams. Entirely in-house development remains rare. Brown referenced advice from AI Leaders Council Board Advisor Martina Matthews, who cautions leaders to start with the problem, not the tool.
OJ agreed, adding that internal development does not always mean traditional coding. “You get more traction of tools that are built by someone in the trenches than you’ll ever get from top down,” he said. With modern generative AI tools, employees can create practical solutions using plain language. “If you can make your own Excel sheet, you can definitely make your own AI tool to help solve these problems.”
Culture and skills remain persistent barriers
Among the top challenges to AI adoption are skills gaps, cultural resistance, and governance concerns. Referencing comments from Board Advisor Nirmal Jingar, Brown emphasized that without a learning culture, employees may use AI without appropriate oversight. OJ reinforced that these barriers are addressable. “This is something that with the right people in place and processes in place, make your current team AI enabled,” he said. While cost constraints are real, he noted that many organizations already have access to low-cost AI capabilities through existing software licenses.
Measuring impact remains difficult, especially for everyday use
Most respondents report moderate or unclear business impact from AI so far, with only a small percentage citing transformative results. OJ offered an explanation grounded in experience. “The impact is huge, but it’s not always visible,” he said, describing behind-the-scenes automation that quietly replaces hours of manual work.
Targeted projects with clear before-and-after metrics are easier to evaluate. Broad, everyday use of AI tools is harder to quantify, even when it meaningfully shapes how employees work. “You might track usage at an incredible level,” OJ said, “and you try to track down that ROI, and you realize it’s because it just becomes a kind of sub-process of what they’re trying to do elsewhere.”
Expansion is expected, but leaders are urged to start small
Looking ahead, most organizations expect AI adoption to continue expanding through additional pilots or broader deployment. A meaningful share anticipates enterprise-wide expansion or AI becoming a strategic core capability.
Brown cited Board Advisor Glenn Hopper’s advice to focus on quick wins. OJ agreed. “If you want these programs in the real world to succeed, well, then we need to show that impact,” he said. Discrete use cases with measurable outcomes help build confidence and momentum for larger initiatives.
Budgets are rising, even as clarity lags
AI budgets are expected to grow in 2026, with most organizations planning modest to moderate increases. The study’s AI Budget Index™ of 165% reflects a strong upward trend, despite many respondents reporting only limited benefits so far.
“Everyone knows they have to spend it,” OJ observed. “They might not know how.” He suggested that better frameworks for measuring impact could eventually align spending more closely with results.
Process automation and talent investment take priority
Process automation ranks highest among AI investment priorities, even ahead of generative AI. OJ noted that automation often provides clearer returns. “If we’re automating processes, it’s much easier to track,” he said, compared with more diffuse productivity gains.
Talent acquisition, training, and upskilling also rank at the top of planned investments. OJ emphasized that technology alone is not enough. “You’re never going to solve a problem with technology either until you fix the people and the processes that you have in place around it,” he said.
Risk concerns persist, with nuance beneath the surface
Data privacy and security top the list of anticipated risks, followed by talent shortages and change management challenges. OJ argued that some fears are overstated. “This is the most overblown concern with AI and really a non-factor when I look at most of my decisions today,” he said, referring to worries about prompts leaking proprietary data.
He cautioned, however, that secondary risks deserve more attention, particularly insecure tools built quickly without proper safeguards. “I actually think the biggest security risk” comes from what people create with AI rather than the models themselves.
AI roles may fade as AI becomes assumed
The study highlights a wide range of AI-related titles across organizations, from CIOs and CTOs to dedicated AI leaders. OJ suggested this may be temporary. “I think we’re going to see a wave of AI in the title and then subsequently fade out,” he said. As AI becomes a baseline skill, it may no longer warrant a separate designation.
Preparing for what comes next
Taken together, the 2026 Corporate AI Outlook Study paints a picture of steady, uneven progress. AI is already reshaping daily work, even when its impact is hard to isolate. Organizations that pair practical use cases with investment in people and governance appear best positioned to move from experimentation to durable value.
To hear the full discussion and explore the complete findings, watch the full webinar and download the 2026 Corporate AI Outlook Study.
