AI Adoption and Impact Study
AI in the Enterprise
June 2026 Top 10 Insights

Top 10 insights from our survey of senior leaders on AI adoption in the enterprise.

FIRMOGRAPHICS

Survey respondents are senior-level enterprise leaders

Industry Distribution

Annual Revenue/Budget

Headcount Distribution

551 Completed Responses

Observations

• 64% Commercial/private sector
• 53% Above $500M revenue
• 40% 501-2,000 employees

Top 10 insights

AI Maturity and Strategy

1. More than four in 10 organizations have cross-departmental AI adoption with measurable impact
2. CIOs/CTOs mostly still own accountability for AI, but risk losing ownership if they don’t drive measurable impact
3. Enterprises with an AI strategy triple their chances of getting measurable value from AI

AI Vendor Sourcing and Infrastructure

1. Better data readiness leads to better results with AI
2. Most organizations chose buy over build for AI sourcing
3. Three-quarters of IT executives believe that AI will disrupt SaaS

AI Investment and Workforce Outlook

1. When AI is deployed without proven value, organizations plan headcount reductions
2. AI adoption spans all business functions but remains concentrated in IT
3. Almost all IT executives are expecting AI budget increases in the next 12 months
4. Cost cutting goals rarely lead to effective AI outcomes

AI IN THE ENTERPRISE – JUNE 2026 – INSIGHT #1

More than four in ten organizations have department-wide AI adoption with measurable impact

How would you best describe your organization's current AI maturity level?

Info-Tech Insight

Despite the headlines about AI not resulting in a return on investment, four in ten organizations say they are seeing department-wide adoption with measurable impact. Most other organizations have adopted AI at the department-wide level with no clear impact yet (28.3%), or have at least launched one pilot (about one in four). Fewer than one in ten organizations say they have no AI initiatives in pilot.

What This Means for Leaders

AI is now mature enough that most enterprises have moved past the pilot stage to department-wide adoption. Many can measure the impact of AI, showing that organizations should look for opportunities to adopt AI in high-priority areas.

AI IN THE ENTERPRISE – JUNE 2026 – INSIGHT #2

CIO/CTOs mostly still own accountability for AI, but risk losing ownership if they don’t drive measurable impact

Who is the primary owner of AI initiatives in your organization?

AI initiative owner’s impact on delivering AI with measurable results

Info-Tech Insight

Most organizations assign a CIO or CTO to lead AI initiatives, and those leaders are driving a measurable impact almost half the time. CIOs/CTOs are outperforming chief digital officers, CEOs or Presidents, and other line of business leaders that own accountability for AI. But one position is doing even better – chief AI officers.

What This Means for Leaders

IT leaders are being trusted to own AI initiatives by most organizations, but if organizations aren’t seeing measurable impact, they might consider assigning out accountability to a new executive position. CIOs and CTOs should prioritize a demonstration of measurable impact to their stakeholders to put themselves in an innovation leadership position with AI.

AI IN THE ENTERPRISE – JUNE 2026 – INSIGHT #3

Enterprises with an AI strategy triple their chances of getting measurable value from AI

Does your organization have a formal, board-approved AI strategy?

A formal AI strategy improves getting measurable results

Info-Tech Insight

Enterprises with a dedicated, governed AI strategy are three times as likely to have AI deployments with measurable impact (60%) compared to those with no active strategy (20%).

What This Means for Leaders

Organizations still treating AI strategy as a planning exercise are leaving measurable outcomes on the table. Formalize with an organization-wide strategy to link AI initiatives to business goals and set clear metrics that will demonstrate progress.

Alpine Energy jump-starts AI strategy with a partner and a proof of concept

When Alpine Energy's CIO lacked the internal expertise and organizational alignment to formalize an AI strategy, she used a targeted experiment and an executive briefing to create both.

Jessica McDonald, CIO
Alpine Energy

Industry: Utilities
Region: New Zealand
Size: 201-500 employees

Situation

Alpine Energy is a New Zealand electricity utility responsible for managing a large network of physical assets: power poles, distribution infrastructure, and associated equipment spread across its service territory. Decisions about when to inspect, repair, or replace those assets had long relied on a combination of attribute data, observed defect records, and the experience of field crews. The process was spreadsheet-driven and largely manual, which meant decisions were slow, inconsistent, and prone to over- or under-spending.

Jessica McDonald, Alpine's CIO, recognized that AI could change this, but she faced a problem that many technology leaders in mid-sized organizations will recognize: no formal AI strategy, no consolidated view of what tools staff were already using, and a leadership team that hadn't yet been brought along on what AI could mean for the business. She didn't have the in-house expertise to evaluate vendors rigorously, and she was skeptical of the larger platform providers whose licensing models she found opaque and whose costs she expected to escalate unpredictably.

Action

Jessica's path forward started not with a procurement process but with a conversation. At a Spark Accelerate conference in New Zealand, she met Mike Bayly, founder of Allexive, an Auckland-based AI startup. Rather than move straight to a contract, she had multiple conversations with Mike before committing any budget, deliberately testing his credibility and assessing whether he understood Alpine's context.

The first engagement was structured to build organizational readiness rather than deliver a technical solution. Jessica worked with Mike to develop and deliver an AI executive briefing to Alpine's leadership team. The briefing covered the current AI landscape, what it meant for leadership decision-making, what it could realistically mean for Alpine's business, and a high-level adoption roadmap. Mike had delivered similar briefings to other organizations, and his role was as much to challenge Jessica's own thinking as to present to the executive team.

With leadership alignment established, Jessica and Mike moved to a focused proof of concept on pole defect risk. The intent was explicitly exploratory: give Allexive Alpine's existing asset data and see what was achievable, without a predetermined outcome or an ROI requirement attached. In roughly five days of effort, Allexive produced a risk-ranked dashboard that assigned inspection priorities to poles and provided reasoning behind each ranking. It replaced a manual, spreadsheet-based process with a structured, explainable output.

Results

The executive briefing achieved what Jessica needed most at that stage: leadership alignment and organizational appetite for further AI investment. The leadership team came away with a shared understanding of the landscape and a concrete sense of what AI could do for Alpine, which gave Jessica the credibility and budget support to move forward.

The pole defect POC demonstrated that AI could produce a defensible, structured output from Alpine's existing data in a fraction of the time the manual process required. While Alpine has not yet deployed the approach at full production scale, the POC gave the organization something tangible to evaluate and a clear direction for the next phase of asset management work.

Perhaps as importantly, the engagement model itself proved out. By starting with a low-cost, low-risk partnership with a local advisor who understood the New Zealand context, Jessica built the internal confidence and external relationship she needed to pursue a more formal AI strategy. Allexive is now helping Alpine develop a skills-based AI licensing pathway that matches tools to staff role profiles, and a more ambitious agentic AI solution for safety-critical risk reviews is under active exploration. Alpine entered the engagement without a strategy. It is exiting it with the foundations of one.

AI IN THE ENTERPRISE – JUNE 2026 – INSIGHT #4

Better data readiness leads to better results with AI

Department-wide adoption of AI with measurable impact

Organizations with no measurable impact from AI

Info-Tech Insight

Organizations that are seeing department-wide adoption with measurable impact are much more likely to rate their data quality as excellent.

Organizations without measurable impact from AI are much less likely to rate their data readiness as excellent.

What This Means for Leaders

Data readiness is mandatory for AI impact. Keep in mind that data on its own has no value. The insights it nurtures, decisions it drives, and actions you take as a result will create value.

AI IN THE ENTERPRISE – JUNE 2026 – INSIGHT #5

Most organizations chose buy over build for AI sourcing

Default sourcing preference for adopting AI capabilities

Info-Tech Insight

Eighty percent of organizations prefer to buy AI solutions over building them in-house, with those buying split nearly evenly between activating their existing vendor stack (42%) and selecting new, best-of-breed vendors (38%).

What This Means for Leaders

Buying AI solutions can be a shortcut to value, and working with your existing vendors can be the path of least resistance. Still, keep an eye on the market for new AI-native vendors that may offer the best solution.

AI IN THE ENTERPRISE – JUNE 2026 – INSIGHT #6

Three-quarters of IT executives believe that AI will disrupt SaaS

Do you expect AI to displace or reduce spending on traditional enterprise software over the next two years?

Info-Tech Insight

Seventy-eight percent of IT executives expect AI to disrupt their current SaaS model within two years. Twenty-eight percent anticipate replacing one or more major platforms outright, while 47.2% say AI will reduce reliance on some tools.

Those not expecting disruption say that either AI will complement, not replace, current software (19.4%) or that AI is adding to their software spend (2.3%) without replacing it.

What This Means for Leaders

AI provides an alternative to existing software vendor relationships not just because everyone is considering “vibe coding” their CRM instead of buying from a vendor. Rather, AI provides a new user experience that can source data from existing platforms and achieve new automations that were previously out of reach. Expect the software as a service (SaaS) model to continue to grow in the coming years, while also seeing new AI-first approaches win out in certain categories.

AI IN THE ENTERPRISE – JUNE 2026 – INSIGHT #7

When AI is deployed without proven value, organizations plan headcount reductions

Expected workforce change by AI strategy formality

Expected workforce change by AI maturity

Info-Tech Insight

Organizations with a formal, board-approved AI strategy are the most likely to expect net headcount reductions as strategy formality and workforce cuts move together (left-hand chart). However, maturity reframes it (right-hand chart): the cuts don't climb step-by-step with adoption. The orgs most likely to cut are those with department-wide adoption but no clear impact, ahead of both the teams still piloting and the ones already seeing measurable impact.

What This Means for Leaders

Don't let a maturing AI program default into a headcount-reduction plan. In this data, cuts cluster where AI is deployed widely but hasn't proven its value. This is a measurement gap, not an efficiency win. The organizations that show impact are the least likely to be cutting roles. Build the capability to demonstrate AI outcomes before building the workforce-savings case.

AI IN THE ENTERPRISE – JUNE 2026 – INSIGHT #8

AI adoption spans all business functions but remains concentrated in IT

In which functions has your organization adopted AI capabilities?

Most popular IT functions for AI adoption

N=445

Info-Tech Insight

AI adoption is high in IT (85%) but is also distributed across other business areas, with more than one-third (38%) already deploying to customer-facing functions.

Within IT, strategy and innovation is the leading area for AI adoption, with security & privacy work the next-most common.

What This Means for Leaders

Take what you’ve learned from deploying AI within IT and enable the rest of the business. Strategy and innovation work are cross-functional capabilities and other leaders will be interested in adopting AI to help in that area if they see it’s working well in IT. It’s an opportunity to shift IT away from order-taking and be cast as an innovator that helps the business achieve its objectives. Showcase the AI wins in IT and talk about the lessons learned that could apply to other lines of business eager to deploy AI.

Unanimous adoption: How LA Dept. of Water and Power inspired users with a Community of Practice

LADWP's experience shows how getting a GenAI chatbot deeply embedded in daily work creates momentum to expand into more capable AI applications

Wei Chou, AI Dev Team Lead,
LADWP

Industry: Public Utility
Region: Western US
Size: 10,000+ employees

Situation

The Los Angeles Department of Water and Power is one of the largest publicly owned utilities in the United States, serving the City of Los Angeles with a workforce of more than 10,000 employees. Like many large public sector organizations, LADWP runs complex internal operations across functions including legal, supply chain, HR, finance, and IT, each with its own document-heavy workflows and knowledge management challenges. Staff routinely spent significant time on routine knowledge work: drafting communications, summarizing information, building presentations, and conducting research.

Deploying AI in this environment is not straightforward. As a municipal utility handling sensitive citizen and operational data, LADWP is subject to government-grade data compliance requirements. Any AI solution had to operate within those constraints before it could be broadly adopted. And like most organizations at the start of an AI rollout, LADWP had no pre-AI baselines in place to measure productivity, which meant that demonstrating value to leadership would require a different kind of evidence than a formal ROI calculation.

Action

Wei Chou, AI development team lead within LADWP's Technical Innovation group, deployed Microsoft 365 Copilot to 1,000 users across IT, leadership, and business functions, running it within Microsoft’s Government Community Cloud High environment to maintain compliance. Rather than mandating specific use cases from the top down, Wei built the conditions for adoption to spread organically with a bi-weekly Community of Practice. The CoP is open to all Copilot license holders and run by IT. Each session carries a specific agenda. Early sessions focused on showing individual business units how Copilot could apply to their workflows, tailoring examples for HR, finance, and other teams rather than presenting generic productivity advice.

As adoption matured, sessions shifted toward more advanced topics, including how to build agents directly from licensed data using Copilot Studio. Prompts surfaced through CoP discussions are documented in a rated SharePoint repository, organized by objective and use case, where users can see what their peers have found most useful and build on it.

Leadership engagement proved to be a critical accelerator. Senior managers became daily Copilot users and consistently appeared among the top 20 most active users across the organization, as tracked through Microsoft's usage monitoring dashboard. Their visible participation created organizational permission for others to invest time in learning the tool.

Results

Approximately 99% of LADWP's 1,000 licensed Copilot users engage with the tool daily, an exceptionally high utilization rate for an enterprise software rollout of this scale. Staff report significant time savings across email management, document drafting, presentation building, and research tasks. Seeing the enthusiastic adoption of AI has convinced leadership to commit to funding it.

The adoption of chatbots also lead Cho’s team to explore agentic AI. Because Copilot became genuinely embedded in daily work habits across the organization, LADWP had the organizational confidence and momentum to expand into more capable use cases. Copilot Studio agents are now live in legal and supply chain, delivering document retrieval and generation capabilities that would have been a harder sell without the foundation of broad, visible daily use already in place. The CoP that drove initial adoption continues to operate as the mechanism through which new use cases are identified, tested, and shared across the organization.

Widespread, visible adoption creates its own kind of evidence, and a structured community for sharing what works is one of the most practical tools available for turning a chatbot rollout into an expanding portfolio of AI use cases.

AI IN THE ENTERPRISE – JUNE 2026 – INSIGHT #9

Almost all IT executives are expecting AI budget increases in the next 12 months

Expectations for AI spend change in 12 months

Impact of formal strategy on AI budget confidence

Info-Tech Insight

Ninety-six percent expect AI budgets to increase over the next 12 months. Forty-six percent expect increases of more than 25%. No respondents anticipate reductions.

Budget confidence also correlates with strategy: 73% of formal-strategy orgs report high confidence vs. 34% of ad hoc orgs.

What This Means for Leaders

AI is seen as an area to invest for almost all IT executives, and having a board-approved AI strategy is the best way to build confidence in supporting that budget. Work to formalize the AI strategy even as you navigate your AI pilots and deployments.

AI IN THE ENTERPRISE – JUNE 2026 – INSIGHT #10

Cost cutting goals rarely lead to effective AI outcomes

Types of goal set for organization’s most effective AI use cases

Info-Tech Insight

Among organizations' most impactful AI use cases, only 11% named cost reduction as the primary goal. Productivity & throughput leads at 38%, followed by revenue growth (14%) and risk reduction (12%).

Organizations mostly aren’t setting out to reduce overhead with their AI initiatives, but instead looking to augment staff.

What This Means for Leaders

Don't try to cost-cut your way to AI impact. Build business cases around productivity, risk, quality, and revenue outcomes. Cost savings may follow — but they should not be the anchor.

Methodology

The AI Adoption and Impact Study – AI in the Enterprise 2026 survey is designed to capture a structured view of organizational AI adoption and impact across multiple dimensions.

Conducted between April and June 2026, the survey received 551 completed responses from senior leaders actively involved in enterprise strategy.

Additional findings from this survey, along with in-depth interviews with enterprise leaders, will be featured in Info-Tech’s AI Adoption and Impact Study Dashboard and Insights Report.

For this insights report, the bottom 5% of survey respondents measured by least time taken to complete the survey were removed from the results.

• Sample size: 551 completed responses
• Collection window: April to June 2026
• Survey method: Online survey
• Geography: 97% North America; other respondents in APAC, EMEA, and other