AI adoption stays uneven. Whereas many organizations are experimenting with AI — together with knowledge science and machine studying (DSML), generative AI and agentic AI — enterprise-wide deployment stays beneath 50%, in response to current analysis by Dresner Advisory Providers.
Reflecting that uneven maturity, solely a couple of quarter of the five hundred respondents to the “Particular Report: Agentic and Generative AI” mentioned AI was a major driver of enterprise technique on the finish of 2025. Nonetheless, that determine greater than doubled in contrast with the primary half of 2025 — a reminder of how rapidly expectations are shifting.
A bigger share — 55% — report that AI influences strategic planning however is just not but central to it.
Solely 16% of organizations say they continue to be primarily targeted on studying what AI can do, suggesting most have moved past experimentation — even when they haven’t but scaled AI throughout the enterprise.
As for why they’re investing in AI, organizations cite tackling long-standing enterprise challenges (49%), the danger of trade disruption (26%), and sustaining aggressive parity (8%) as their major motives.
“Regardless of the hype, a majority of organizations are nonetheless early of their AI journeys, experimenting selectively quite than deploying AI at scale all through their core enterprise processes,” mentioned Brian Lett, vp at Dresner Advisory Providers.
“Nevertheless, for these which are prepared, AI has turn into an integral a part of technique that’s worthy of funding,” Lett added. “For these organizations, AI is now not a skunkworks initiative or speculative expertise. Their AI adoption happens in operations and processes, and hyperlinks on to concrete enterprise outcomes.”
The strategic divide
Taken collectively, the info recommend a market in transition. The rising divide is now not between organizations experimenting with AI and people that aren’t. It is between these which are strategically embedding AI into ruled, production-grade processes and people utilizing AI tactically to enhance work.
In my conversations with distributors over the previous yr, knowledge maturity persistently got here up as the first bottleneck to scaling AI. Roughly half say they’re constructing instruments geared toward accelerating what has been a sluggish, multi-year course of of knowledge preparation and governance.
Dresner’s findings reinforce that constraint: With out production-grade knowledge and governance, AI initiatives stall on the pilot stage quite than shifting into full manufacturing.
What organizations put into manufacturing exhibits how far their AI efforts have progressed past experimentation. It additionally highlights the distinct roles completely different types of AI play now.
The place AI is being utilized
Information science and machine studying stay probably the most mature types of enterprise AI, targeted on optimizing selections and producing operational perception. Frequent functions embrace churn modeling, forecasting, A/B testing, personalization, anomaly detection and useful resource allocation.
Generative AI has gained traction primarily by way of use circumstances targeted on workforce productiveness. Its worth lies primarily in empowering staff to enhance their day by day work. Whereas helpful, these beneficial properties alone do not essentially translate into enterprise transformation. Enhancing particular person output is just not the identical as redesigning how work will get executed.
Agentic AI combines analytical fashions, generative capabilities and workflow automation to execute multi-step duties throughout methods. Quite than stopping at perception or content material technology, these methods act. They set off workflows, replace data and resolve points guided by outlined insurance policies. Not like DSML fashions that optimize selections or generate predictions, agentic methods carry these selections ahead. The place DSML informs generative AI assists, agentic methods function.
Within the second half of final yr, a majority of respondents reported being excited or cautiously optimistic about generative and agentic AI. n=500 Supply: Dresner Advisory Providers
Generative and agentic AI adoption
On the finish of 2025, barely greater than half of organizations reported actively experimenting with generative and agentic AI. Nevertheless, manufacturing deployment stays extra restricted: — 34% for generative AI and 15% for agentic AI — although each charges have greater than doubled since 2024. Funds alignment can be accelerating, with 72% allocating cash to generative AI initiatives and 66% to agentic AI.
The hole between finances allocation and manufacturing deployment means that a few of this spending is just not but translating straight into scaled functions. And whereas generative AI attracts vital funding, enterprise leaders say a portion of that funding is directed towards foundational knowledge work required to help superior circumstances. In different phrases, AI budgets are quietly underwriting knowledge modernization.
As one college expertise chief famous to me, groups might start with less complicated use circumstances, however growing an AI software that delivers a single view of the scholar or identifies at-risk college students relies on unified, well-governed knowledge environments.
Information maturity as a constraint
Dresner analysis on agentic AI exhibits a constant sample: Organizations which have moved agentic methods into manufacturing usually report earlier success with BI, and knowledge modeling and machine studying. They’re additionally extra more likely to have a clearly outlined knowledge chief.
In different phrases, AI adoption correlates with established knowledge self-discipline. Organizations which have already invested in modernizing analytical knowledge infrastructure, enhancing knowledge high quality, strengthening governance and lowering knowledge silos are higher positioned to operationalize AI at scale. Agentic functionality tends to comply with knowledge maturity — not the opposite method round.
Organizations that progress past experimentation are likely to comply with a structured path.
Steps to AI maturity: Experimentation to execution
For CIOs and knowledge leaders, the precedence is obvious: transfer AI from experimentation to embedded execution. That shift requires self-discipline in use-case choice, governance and a dedication to knowledge.
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Map DSML, generative and agentic AI to particular enterprise issues. Outline measurable outcomes and aligned funding accordingly.
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Prioritize use circumstances that may ship measurable outcomes utilizing present methods and knowledge. Keep away from delaying worth whereas ready for excellent architectures.
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Embed generative AI into information work and operational workflows, and measure productiveness beneficial properties on the staff and performance stage.
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Set up clear insurance policies on authorized instruments, acceptable use, knowledge dealing with and danger administration.
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Audit AI capabilities already embedded in core enterprise functions (ERP, CRM, human capital administration) and activate options earlier than investing in new instruments.
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Determine AI use circumstances that materially enhance buyer experiences or create new income streams.
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Begin with precedence enterprise use circumstances, then outline the minimal viable knowledge capabilities required to scale them.
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Outline a phased roadmap for delivering production-grade, ruled knowledge.
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Current executives with a transparent funding alternative: speed up full knowledge industrialization or pursue a staged functionality mannequin that incrementally advances knowledge maturity — each require sustained funding and enterprise possession.
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In data-mature organizations, develop DSML to optimize end-to-end processes and scale back structural prices.
