As organisations rush to embrace artificial intelligence, a growing number are discovering a hard truth: without strong data foundations, AI initiatives are destined to fail. Ronnie Sheth, CEO of SENEN Group, explains why 2026 is the year enterprise AI must move from experimentation to real business value.
Artificial intelligence has moved from boardroom buzzword to boardroom mandate. Across industries, executives are under mounting pressure to “do something with AI” — whether that means launching pilots, deploying chatbots, or investing heavily in machine learning platforms. But while AI adoption numbers continue to rise, meaningful outcomes remain elusive for many enterprises.
According to Gartner, poor data quality costs organisations an average of $12.9 million annually, draining resources, eroding trust, and stalling innovation. For Ronnie Sheth, CEO of SENEN Group, this statistic captures the core issue holding enterprise AI back: organisations are building advanced AI solutions on unstable data foundations.
“AI doesn’t fail because the technology isn’t powerful,” Sheth explains. “It fails because companies skip the basics. They rush into AI before their data is ready.”
A Veteran Perspective on Data and AI
Sheth is not new to this conversation. With a career rooted in data and AI from its early enterprise days, she describes herself as having been in the space “ever since I was a corporate baby.” Today, she leads SENEN Group, a global firm specialising in AI strategy, data governance, execution, operationalisation, and data literacy.
That experience translates into results. SENEN Group boasts a 99.99% client repeat rate, an unusually high figure in a sector often defined by short-term pilots and abandoned proof-of-concept projects.
“Our clients don’t come back because we sell AI tools,” says Sheth. “They come back because we help them make AI work — practically, sustainably, and at scale.”
The Executive Mandate Problem
One of the most common patterns Sheth observes begins at the executive level. Boards and C-suite leaders, eager to remain competitive, issue directives to “adopt AI” without defining what success actually looks like.
“There’s often no blueprint, no roadmap, and no clarity on outcomes,” she says. “Teams are told to deploy AI, so they do — but what does success mean? Increased revenue? Cost reduction? Risk mitigation? Better decision-making?”
Without these answers, organisations may celebrate high user adoption or flashy dashboards while failing to generate measurable business value.
“This is how AI becomes theatre,” Sheth notes. “It looks impressive, but it doesn’t move the needle.”
Data Quality: The Silent AI Killer
Even in 2024, Sheth encountered enterprises whose data environments were fundamentally unprepared for AI.
“The data was nowhere near where it needed to be,” she says. “Not even close.”
Issues ranged from inconsistent data definitions and siloed systems to missing governance frameworks and unclear ownership. In such environments, AI models inevitably produce flawed, biased, or misleading outputs.
“AI will always reflect the quality of the data feeding it,” Sheth explains. “If your data is broken, your AI will be broken — just faster.”
The encouraging news, she adds, is that organisations are finally starting to understand this reality.
A Shift Toward Practical AI Strategy
In recent months, Sheth has noticed a meaningful change in how enterprises approach AI. Rather than rushing to deploy models, companies are increasingly seeking help with data readiness first.
“More organisations are coming to us saying, ‘We know our data isn’t right. Help us fix that before we talk about AI,’” she says.
At SENEN Group, this shift changes the entire engagement model.
“The first order of business is always data,” Sheth explains. “We stabilise it, structure it, govern it, and align it with business objectives. Only then do we move into AI.”
This approach may appear slower at first, but it dramatically increases long-term success.
“Once the data foundation is solid, organisations can build multiple AI models, deploy different AI solutions, and actually trust the outputs,” she says. “That’s when AI starts delivering real value.”
From Raw Data to AI-Driven Decisions
Sheth points to a recent client example that highlights the importance of sequencing.
“They came to us asking for data governance,” she recalls. “But governance alone wasn’t the real issue.”
Instead, SENEN Group identified the need for a data strategy — defining why data mattered to the organisation, how it should be used, and what outcomes leadership expected.
“We worked through the ‘why’ and the ‘how’ first,” Sheth explains. “Then we layered in governance, operating models, and execution roadmaps.”
The results were transformative. The organisation progressed from raw, unstructured data to descriptive analytics, then predictive analytics, and is now implementing a comprehensive enterprise AI strategy.
“That journey wouldn’t have worked in reverse,” Sheth says. “AI was the destination, not the starting point.”
Why Enterprise AI Must Deliver Value Now
Sheth believes enterprise AI has reached a critical inflection point. The era of endless experimentation is coming to an end.
“For the last few years, it was acceptable to pilot, experiment, and explore,” she says. “That phase served its purpose.”
Now, economic pressures, regulatory scrutiny, and executive expectations demand results.
“This is not the time to say, ‘We’re innovating’ without outcomes,” Sheth asserts. “This is the time to get AI to value.”
For enterprises, that means focusing on use cases tied directly to business metrics — efficiency gains, revenue growth, compliance improvements, and risk reduction.
“AI must earn its place in the enterprise,” she says. “And the only way it does that is by being practical.”
Governance, Ethics, and Trust
Another area gaining urgency is AI governance. As AI systems influence decisions in finance, healthcare, insurance, and public services, organisations face growing accountability.
“Governance is not about slowing innovation,” Sheth explains. “It’s about making AI safe, explainable, and trustworthy.”
Without proper governance, organisations risk regulatory penalties, reputational damage, and erosion of customer trust.
“At SENEN Group, we see governance as an enabler,” she says. “It gives leadership confidence to scale AI responsibly.”
Data Literacy: The Missing Link
Beyond technology and governance, Sheth highlights a frequently overlooked factor: data literacy.
“You can have the best AI strategy in the world, but if your people don’t understand data, it won’t work,” she says.
SENEN Group places strong emphasis on upskilling teams — from executives to frontline employees — to understand how data and AI impact their roles.
“AI adoption is as much a cultural shift as a technical one,” Sheth explains. “People need to trust the outputs and know how to act on them.”
A Message for 2026 and Beyond
Sheth will expand on these themes during her session at the AI & Big Data Expo Global in London, where she plans to deliver a clear message to enterprise leaders.
“Now is the time to get practical with AI,” she says. “Especially in the enterprise.”
She cautions against chasing innovation for its own sake.
“This is not the year for endless pilots or disconnected experiments,” Sheth adds. “This is the year for execution, value, and outcomes.”
The Road Ahead for Enterprise AI
Looking ahead, Sheth believes organisations that succeed with AI will share three common traits:
- Strong data foundations
- Clear business-aligned AI strategies
- A disciplined focus on measurable value
“AI is no longer a competitive advantage on its own,” she says. “Execution is.”
For enterprises willing to slow down, fix their data, and adopt AI with purpose, the rewards are significant. For those chasing hype, the risks — financial, operational, and reputational — are growing.
“The future of AI belongs to organisations that treat it as a business capability, not a science experiment,” Sheth concludes. “And that future starts with getting practical.”