AI conversations move swiftly, often faster than most organizations can evaluate where the real opportunity is. The result is a conference room filled with bold claims, shifting definitions, and high expectations that rarely translate into outcomes. Despite the inability to appropriately evaluate AI and its application within each organization, adoption is accelerating. According to Stanford’s 2025 AI Index, 78% of organizations are now using AI in at least one business function, up sharply from 55% the year before.
In our recent webinar, “From Buzzwords to Business Value: Demystifying AI for IT Leaders,” we focused on cutting through the noise and helping organizations understand where AI creates genuine operational value and where a more cautious, architecture-first approach is best.
Why AI Still Feels Vague for Many Teams
Today, 65% of organizations use generative AI regularly, nearly doubling the previous year’s AI use. But even with rapid adoption, value creation remains uneven. Gartner estimates 30% of generative AI projects will be abandoned after proof of concept by the end of 2025 due to unclear business objectives, weak data foundations, and immature governance models.
The most common places where businesses fail fall into four main areas:
- AI efforts are disconnected from practical business needs.
- Too many experimental projects with no plan to scale.
- Unclear ROI expectations and no success metrics.
- Data, identity, and compliance foundations are not ready for automation at scale.
The result is often complexity without capability.
Start With the Outcome, Not the Model
In our latest Architects Unscripted webinar, we addressed a principle that is increasingly shaping enterprise architecture decisions: AI should be a tool, not a strategy.
Before choosing a model or platform, organizations must identify the business problem, define the operational outcome, and understand the supporting data and integration requirements.
We walked through a practical evaluation approach:
- Anchor to business friction – Look for bottlenecks, inefficiencies, and process gaps.
- Validate that AI is the right mechanism – In many cases, automation or workflow redesign may deliver better results.
- Model ROI intentionally – This will quantify time saved, error reduction, throughput improvements, or avoided spend.
- Define success criteria upfront – Pilots should have clear gates and a roadmap for productionization.
This reframes AI from hype to a structured portfolio of initiatives aligned with business priorities.
What Realistic AI Adoption Actually Looks Like
Despite the noise, most successful AI programs start small. While the global AI market reached $244 billion in 2025 and is projected to grow to $827 billion by 2030, this growth is being driven by incremental, scalable improvements.
Deloitte’s recent research highlights that some of the fastest-maturing AI use cases sit within IT and operations. Leading organizations across all industries are prioritizing targeted pilots such as anomaly detection, data summarization, and workflow automation, along with a limited set of high-value use cases that tie directly to measurable KPIs. They are pairing this focus with disciplined governance around data quality, identity, risk, and transparency, while ensuring seamless integration with existing infrastructure to avoid unnecessary technical debt.
All of the data points back to what realistic AI adoption looks like. Organizations don’t succeed by deploying “more AI” but by deploying the right AI within a well-architected environment.
Capturing Business Value
Boston Consulting Group’s analysis proved what felt like a widening divide between AI leaders and everyone else. Companies effectively scaling AI expect 60% higher AI-driven revenue growth and roughly 50% greater cost reductions by 2027 compared to organizations still stuck in pilot mode. What sets these leaders apart aligns closely with Verinext’s experience across enterprise environments:
- Strong data foundations
- Mature identity and access architectures
- Clear governance and responsible-use frameworks
- Cross-functional alignment between IT, security, and business units
- A methodical approach to scaling, not just experimenting
In our experience and after reviewing recent data, this discipline is clearly becoming the differentiator between organizations that realize long-term value and those that accumulate technical debt.
The Full Framework
This recap only touches the surface. In the full webinar, we explore:
- How to build a practical decision model for AI use cases
- Real-world examples of moving from pilot to production
- Architecture considerations to avoid downstream complexity
- Governance and risk models that support sustainable AI adoption
If you’re evaluating where AI fits meaningfully into your roadmap, this conversation is for you. Watch our on-demand webinar for the full discussion, and connect with our professionals with any questions. We look forward to helping you prepare for all that comes next.
