AI tools have become essential for business, with most companies moving from testing to action. Yet, despite massive investment, only one in five organizations is seeing the results they hoped for, and analysts predict up to 30% of generative AI projects could be scrapped this year.
This churn isn’t necessarily a failure of planning, but a natural symptom of the early AI revolution; as business leaders are still discovering which workflows will stick, making it difficult to measure success by efficiency outputs alone.
AI Enablement Team Lead for Atera.
As budgets tighten and leadership demands proof of value, the definition of ROI must evolve. However, realizing the full potential of these initiatives requires recognizing that in this experimental phase, the most valuable “return” is the creation of an AI-ready culture.
The key is simple: connect what you measure to the reality of the adoption curve, prioritizing organizational agility over immediate speed and results.
Assessing readiness: Culture over infrastructure
Many companies have learned that jumping into AI without proper prep can be costly. However, “readiness” today requires more than just infrastructure updates; it demands a focus on adoption, knowledge and people.
Research confirms that the “human element” is the biggest differentiator. McKinsey found that organizations who emphasize communication and make it a central part of a rollout are seven times more likely to succeed with automation.
It is not enough for employees to know that AI exists; they must understand how to wield it effectively within their specific roles.
To truly build this readiness, you must move beyond passive surveys and prioritize hands-on workshops. These practical sessions allow teams to engage with the tools directly in a controlled environment.
By replacing theoretical training with active experimentation, you bridge the gap between abstract potential and daily utility. This approach not only reveals genuine skill gaps but also empowers your teams to become the driving force of this cultural shift from day one, creating momentum early.
Selecting the right battles
To navigate this revolution, choose initial projects that target high-friction points like repetitive manual tasks or data silos. Start by reviewing ticketing systems and labor costs to pinpoint where AI offers immediate value.
Crucially, prioritize tools that fit natively into your team’s existing workflows. If users must constantly switch contexts or toggle screens, adoption will drop. The most successful implementations meet users where they are, enhancing current processes rather than disrupting them.
By targeting these “native” bottlenecks, you eliminate drudgery and make the cultural leap seamless for your staff, while demonstrating clear early wins.
The pivot: From performance KPIs to adoption KPIs
Once tools are live, resist the urge to immediately track efficiency metrics like speed or error rates. In this experimental phase, you cannot optimize a tool that no one uses. Instead, prioritize adoption KPIs to measure the cultural shift.
Focus on usage rates, engagement, and whether teams are actually changing their daily behaviors. Tracking these metrics allows you to catch resistance early and validate that the culture is adapting.
This flexibility is critical; an MIT study found that companies that regularly update their KPIs to match their stage of maturity are three times more likely to see major financial gains over time.
The data difference: Why context drives success
Even with the right culture, the success rate of any AI initiative ultimately relies on the tool’s ability to connect to organizational data. Generic AI tools that operate in a vacuum often suffer from low success rates because they lack context. They can generate text, but they cannot solve specific business problems.
In contrast, tools that “know” the organization—by securely integrating with internal data, ticketing history, and documentation—have a significantly higher success rate. This is where data quality becomes a decisive factor. You must look at your data: how clean, complete, and accessible is it?
Organizations that take a thoughtful approach to this integration ensure that their AI isn’t just a novelty, but a context-aware engine that drives real business value across multiple departments.
IT as the architect of culture and integration
This integration process places IT at the very center of the transformation. IT teams are no longer just service providers; they are the architects of the organization’s future. They are the only ones positioned to bridge the gap between technical data integration and human cultural adoption.
By taking ownership of these initiatives, IT ensures that AI tools are not just “deployed,” but are deeply integrated into the company’s unique data landscape. Simultaneously, by using adoption metrics to guide the workforce, IT shapes how the organization works and learns.
The “win” today is twofold: building the technical pipelines that make AI context-aware, and fostering the human habits that make AI usable. By leading with purpose and focusing on these dual outcomes, IT teams turn strategy into lasting business impact and make AI a true business driver in every operational area.
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