Artificial intelligence adoption is accelerating at a breathtaking pace. SMBs and enterprises alike have experimented with generative AI, automated workflows, and predictive analytics—yet many leaders are feeling an unexpected sense of frustration. Despite having more tools and more data than ever, real decision-making hasn’t gotten easier. In fact, for many businesses, progress has stalled.
This phenomenon has a name: the AI plateau. It’s the moment when experimentation gives way to confusion—when AI exists inside the organization, but insights don’t translate into action, and confidence in the data starts to erode. The issue isn’t a lack of technology. It’s a disconnect between data, people, processes, and trust.
The takeaway: Most businesses don’t hit an AI wall because the technology failed—but because the data, processes, and trust weren’t ready.
Why This Matters in 2026
AI tools are everywhere, but insight is still scarce. As businesses invest more in AI, the real differentiator won’t be who has the most advanced technology—it will be who can trust their data, understand AI recommendations, and act on them with confidence. In 2026, breaking through the AI plateau isn’t about adding more tools—it’s about turning information into decisions that actually move the business forward.
To unpack why AI progress so often stalls after early experimentation, I spoke with Vishakha Shenoy, Director of Data Science and AI Services at Domo, an AI and data products platform, about what’s really holding companies back—and how they can break through.
Rieva Lesonsky: AI adoption is accelerating, yet many SMBs feel stuck. When you talk about an “AI plateau,” what does that actually look like inside companies?
Vishakha Shenoy: The rapid surge of generative AI prompted many companies to experiment enthusiastically; however, beyond some manual task automation, many SMBs’ adoption and utilization of AI plateaued. The root causes range from data quality, readiness, and governance issues to integration gaps, skill shortages, privacy and security risks, and a lack of a defined roadmap.
SMBs have been able to operate for a long time without strong data quality and readiness; however, these are both foundational for AI. Weak data governance and system integration keep the data siloed, further limiting adoption. Most companies have also cited privacy and security as their top concerns in using AI, along with a lack of expertise in scaling AI. This has resulted in the absence of a clear roadmap. Pursuing AI without a roadmap is like navigating to an unknown destination without a map. Domo customers are seeing that when AI isn’t purposefully orchestrated into how people operate day to day, progress flattens quickly, and that’s the plateau.
Lesonsky: What’s the biggest disconnect you’re seeing between having AI tools and actually using AI to make better decisions?
Shenoy: The three things you need to use AI tools to make better decisions are:
- WHAT is the context?
- Why is a certain prediction or forecast made? Or WHY the tool is asking me to take a certain action?
- WHEN is the model expected to perform poorly?
Here is an example: A business sees a huge spike in orders. Without the right context, AI can treat it as a fraud or system hack and recommend temporarily shutting down the system. However, having the right context—that an aggressive promotion was recently put in place will help AI make a different recommendation—to make sure the infrastructure is strong enough to sustain this surge, to see if we have the right amount of product in stock, etc. Additionally, all models have limitations; knowing what they are and when a model reaches its limits will help me decide when and how much trust I can place in these tools.
Lesonsky: Is this plateau more about technology—or about people, processes, and trust in the data?
Shenoy: It’s overwhelmingly about people, processes, and trust. The technology is moving faster than most organizations can adapt culturally. If teams don’t trust the data, they won’t trust the AI built on top of it. If processes aren’t designed to accept and act on AI-driven recommendations, nothing changes.
The tech itself has advanced to the point that AI often exposes organizational weaknesses rather than hiding them. Siloed data, unclear ownership, and fragile governance all become more visible. And companies that simply treat AI as a technical upgrade miss the point. The real work is orchestrating the data, the AI, and the human interactions.
Lesonsky: You’ve worked with many businesses. What mistakes do business owners commonly make when they expect AI to magically produce insight or action?
Shenoy: The most carefully trained and planned models to just that—magically produce insight or action. The one-size-fits-all models are the problem. Each business need must be evaluated carefully to craft a unique solution. Even so, these solutions must be regularly evaluated and recalibrated.
Lesonsky: Many executives say they have more data than ever—but less confidence in it. Why does AI sometimes make decision-making feel harder, not easier?
Shenoy: Having more data isn’t necessarily the best solution. Data readiness, data quality, data governance, and data integration are the foundation of AI. Without a solid foundation, AI will fail to deliver, and as a result, executives will find it hard to trust. Because of the time investment necessary to lay that solid AI foundation, it might make decision-making harder, not easier.
Lesonsky: What separates companies that break through the AI plateau from those that stay stuck experimenting?
Shenoy: The Domo customers we see succeeding most with AI stop treating it as a feature and start treating it as infrastructure. They embed AI directly into workflows, applications, and decisions that already matter to the business.
These companies also take trust seriously. They invest in governance, transparency, and shared definitions so people believe what they’re seeing. Once teams trust the data and understand how AI arrives at conclusions, adoption will accelerate naturally. But it’s that confidence, not the novelty of something shiny, that drives momentum.
Lesonsky: If you had to give business owners one piece of advice for turning AI insights into real business action in 2026, what would it be?
Shenoy: Start small at first—start with the area of business or the decision you want to change. Define WHAT you want to change about the business and WHAT action needs to be taken.
Prepare the right data needed for the change. Orchestrate AI into the workflow to trigger the right action. And always evaluate your results, regularly and meticulously. Once this framework works, expand it to other areas of the business.
Closing: Moving Past the AI Plateau
The takeaway is clear: hitting an AI plateau doesn’t mean AI has failed—it means the work has shifted. The companies that will make real progress in 2026 aren’t chasing the latest tools or piling on more dashboards. They’re doing the less glamorous but far more valuable work of cleaning up data, building trust in insights, and embedding AI into decisions that already matter.
For business leaders, the opportunity isn’t to “do more AI,” but to be more intentional about how it’s used. When AI is treated as infrastructure—not an experiment—it becomes easier to move from curiosity to confidence, and from insight to action. The businesses that break through the plateau will be the ones that stop asking what AI can do and start focusing on what they need to change.
Rieva Lesonsky is the founder of Small Business Currents, a content company focusing on small businesses and entrepreneurship. You can find her on Twitter @Rieva, Bluesky @Rieva.bsky.social, and LinkedIn. Or email her at Rieva@SmallBusinessCurrents.com.
Photo courtesy Galina Nelyubova for Unsplash+
