This article originally appeared on Tech Times on August 17, 2026. You can view the original <a href="https://www.techtimes.com/articles/324807/20260817/happyfuncorp-research-how-successful-17-make-ai-work.htm" target="_blank" rel="noopener">article about HappyFunCorp's AI research here</a>.
The bulk of mid-market companies are eyeing artificial intelligence in 2027, seeking efficiency, research, and cost wins by integrating automation and machine learning. But according to data from&nbsp;<a href="https://www.happyfuncorp.com/" target="_blank" rel="noopener sponsored">HappyFunCorp</a>, while 92% of those organizations are committed to AI-forward initiatives, only 17% report high levels of success in implementing them.
The HappyFunCorp data shows how the organizations leading AI implementation make a distinct set of decisions, including:
<ul>
<li>Aligning leadership and execution focus</li>
<li>Choosing the right problems to solve with AI</li>
<li>Fixing data and systems before building on them</li>
<li>Creating accountability via ownership of AI projects and processes</li>
<li>Bringing outside expertise in early</li>
</ul>
<a href="https://www.happyfuncorp.com/ai-research">HappyFunCorp surveyed technology and business leaders</a>&nbsp;across four industries:
<ul>
<li>Healthcare and life sciences</li>
<li>Travel and hospitality</li>
<li>Retail and ecommerce</li>
<li>Media and publishing</li>
</ul>
In all, 501 leaders responded to the April–May 2026 survey, carrying a ±4.4% margin of error. Findings included:
<ul>
<li>43% of organizations report successfully scaling AI initiatives, but only 17% clear the higher bar of AI operating at a meaningful scale across the business.</li>
<li>44% say they've struggled to scale or sustain AI initiatives.</li>
<li>13% say they're in the early stages of AI projects.</li>
</ul>
Three groups emerged based on the data:
<ul>
<li>Builders.&nbsp;Around 22% of respondents were in exploration or pilot stages, with limited integration of AI and little to no visibility into ROI.</li>
<li>Executors.&nbsp;Around 56% were in active implementation, navigating from pilots to scaled AI use.</li>
<li>Leaders.&nbsp;Around 22% reported meaningful AI use across product systems.</li>
</ul>
<h2>They Align Leadership and Execution Teams</h2>
Of the organizations that showed up as AI Leaders according to the data, 26% credit stronger alignment between leadership and execution teams as a driver of progress. Only 13% of Builders still in the early stages of AI adoption say the same.
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Alignment closes the gap between those setting AI priorities and those working to achieve them. When decision-makers, budget owners, and technical teams share a common definition of success, initiatives move faster and better withstand scope changes that can come with new technology. Without alignment, AI projects can drift, get reprioritized mid-build, or stall while teams wait for decisions that leadership assumed were already made.
In practice, aligning leadership and execution teams can mean:
<ul>
<li>Having technical leads sit in on strategy conversations</li>
<li>Defining a shared success metric that the C-suite and engineering teams can point to</li>
<li>Hosting regular check-ins between budget owners and project teams</li>
</ul>
<h2>They Bring in Outside Expertise Early</h2>
Around two-thirds of AI Leaders say they're very likely to engage an external partner for help with AI initiatives in the next six months. And 46% of all respondents said external expertise is a key driver of AI progress. Only around 18% of organizations are building AI primarily in-house.
Depending on where an organization gets stuck, outside expertise might come from:
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<ul>
<li>Engineers and architects with senior-level experience in AI systems</li>
<li>Partners who understand data infrastructure and governance</li>
<li>Specialists familiar with an industry's compliance and legacy-system challenges</li>
</ul>
Leaders aren't seeking outside help because they're behind; they're bringing in partners because specialized expertise is hard to build and maintain in-house. Outside skills and knowledge can accelerate work, and HappyFunCorp's research shows that AI maturity doesn't reduce the need for outside support. In fact, it seems to increase the willingness to use it strategically.
<h2>They Choose Measurable Initiatives and Build in ROI Metrics</h2>
When asked whether they defined ROI metrics for AI:
<ul>
<li>72% of Leaders said yes.</li>
<li>15% of Executors said yes.</li>
<li>No Builders said yes.</li>
</ul>
It's not enough to measure how AI performs, though. HappyFunCorp reports that Leaders are proactive in choosing problems to solve with AI, prioritizing those they can clearly define and measure, even before implementation begins.
<ul>
<li>57% of all respondents said identifying the right project or opportunity was the most important factor in making AI progress.</li>
<li>63% of Leaders said it was the most important.</li>
</ul>
A measurable initiative is one where:
<ul>
<li>Success is defined before implementation begins.</li>
<li>A specific metric, such as cost savings, time saved, or revenue impact, is tied to the initiative from the start.</li>
<li>Progress can be tracked against that metric at regular intervals.</li>
</ul>
Without this type of analytical discipline, teams can't identify what's working or not. They may make decisions about scaling without proof that supports the investment.
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<h2>They Fix Data and Systems Before Scaling AI</h2>
Data and systems readiness show up repeatedly in HappyFunCorp's findings as prerequisites for AI success.
When asked what would most improve their ability to execute on AI, 59% of all respondents pointed to better data infrastructure, while 54% credit data and systems readiness as a driver of the progress they've already made. In Healthcare &amp; Life Sciences, where compliance and fragmented clinical systems complicate everything, the share citing better data infrastructure climbs to 67%, the highest of any industry.
Organizations that address data and systems before scaling AI might:
<ul>
<li>Audit existing data sources for quality and completeness before connecting them to an AI initiative</li>
<li>Consolidate fragmented systems, such as customer records, operational data, and legacy platforms, so AI tools can draw from a single reliable source</li>
<li>Build governance around who owns and maintains data once AI systems depend on it</li>
<li>Treat infrastructure upgrades as part of the AI project's scope and timeline instead of a separate initiative to be addressed later</li>
</ul>
<h2>They Put One Owner in Charge</h2>
Having an internal owner of implementation and/or AI processes drives success, according to half of AI Leaders in the survey.
Clear ownership creates accountability. When one person is responsible for an AI initiative, prioritization decisions have a single point of contact, and someone is answerable for project outcomes. Without that, AI efforts can become committee-driven: many stakeholders weigh in, but no one is responsible for scaling the work or explaining why it stalled.
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In practice, this can look like:
<ul>
<li>Naming a single decision-maker for each AI initiative instead of leaving prioritization to a rotating group of stakeholders</li>
<li>Giving the owner authority over budget and scope decisions, not just reporting responsibilities</li>
<li>Making that person the point of contact when leadership asks for status or results</li>
</ul>
<h2>Closing the AI Execution Gap</h2>
HappyFunCorp's data demonstrates that organizations wanting AI success should focus on operational discipline. The 92% committed to AI have the mandate. What separates the successful 17% is execution: aligning teams, measuring results, fixing infrastructure early, assigning ownership, and bringing in outside expertise before problems compound.

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