Enterprise AI Adoption: Why Practicality Must Come First in 2026

Introduction: The Shift Toward Practical AI

Generally, Companies are starting to realize that adopting AI is not just about being trendy. Normally, I think they used to chase shiny tech for hype’s sake, but now they understand that without a solid base, AI just does not work.
Usually, Ronnie Sheth, CEO of the SENEN Group, says 2026 is the year to get practical with AI, to actually use it for something, not just to play with pilots.
Obviously, her firm boasts near-perfect client retention, which tells you they know how to guide firms toward real success, not just demo day.
Basically, so what does “getting practical” actually look like, why is now the moment to pivot, and what are the benefits of doing so.

The Cost of Poor Data: A Multi‑Million‑Dollar Problem

Apparently, before any AI, you gotta face the data quality monster that costs firms on average $12.9 million a year, Gartner says.
Naturally, Sheth warns execs still underestimate this, jumping into AI without checking if their data is ready, which is a big mistake.
Generally, “Companies rush into AI because of pressure or fear,” she says, “but without a roadmap they get big user numbers and zero outcomes.”
Normally, in 2024 many still lacked the data infrastructure needed, and even today they’re scrambling to fix data before looking at models.
Usually, that shift—from chasing innovation to securing foundation—marks a turning point for enterprise AI, and it is a good thing.

Building a Strong Foundation: Data First, AI Second

Obviously, enterprises that want AI to work must start with data, plain and simple, it is not rocket science.
Basically, Sheth says if your data’s flawed, your AI outputs will be flawed too, and that’s a truth nobody can ignore, it is just common sense.
Generally, the SENEN Group always kicks off with a data audit, not a model demo, which is the right way to do it.
Normally, one client came in asking for governance, but quickly learned they needed a full data strategy first, which was a good lesson.
Usually, that strategy spelled out the “why” and “how,” set up governance, and mapped a roadmap for an operating model, which is essential.
Apparently, we moved them from raw data to descriptive analytics, then predictive, and finally we’re crafting an AI plan, which is the way to do it.
Naturally, this step-by-step ensures they’re not just buying AI for its own sake, but building toward real outcomes, which is the goal.

From Experimentation to Execution

Generally, the era of endless pilots is ending, and 2026 is the year to get AI to value, which is a good thing.
Obviously, Sheth pushes firms to ask, “How can AI solve a specific problem for us?” instead of “Let’s try something new,” which is the right question to ask.
Normally, whether it’s cutting waste, boosting CX, or streamlining ops, AI must tie to concrete goals, which is essential.
Usually, Sheth will talk about this at the AI & Big Data Expo Global in London, stressing practical adoption, which is important.
Basically, enterprises that put data quality, strategy, and measurable results first will be the ones thriving, which is the way to do it.

Conclusion: The Path Forward for Enterprise AI

Apparently, speed isn’t the answer; precision is, which is a fact.
Generally, resist chasing every new AI trend, and invest in data quality, governance, and a clear strategy before you launch models, which is the right way to do it.
Obviously, as Sheth puts it, “AI is only as strong as the data it’s built on,” which is true.
Normally, businesses ready to move forward should align AI projects with solid goals and ensure data readiness, which is essential.
Usually, 2026 is the year to stop experimenting and start delivering real value with practical AI, which is the goal.