At the SelectUSA Investment Summit 2026, amid the billion-dollar conversations around artificial intelligence, infrastructure, semiconductors, and digital transformation, one of the most sobering interventions came not from a policymaker or Big Tech executive — but from an advisor warning that the AI race may be accelerating far ahead of organizational understanding.
Uvika Sharma, founder of INTLDA and advisor to C-suites and enterprise leaders on Responsible AI adoption and governance, said companies across industries are dangerously mistaking AI experimentation for actual transformation.
“There is definitely froth in parts of the market, especially around valuations and unrealistic expectations,” Sharma said in an interview with BW Businessworld on the sidelines of the SelectUSA Investment Summit 2026. “But underneath the hype, there is also a very real infrastructure shift happening.”
Her assessment cuts directly through the current AI frenzy gripping corporate America and global markets alike. While investors continue to reward anything associated with generative AI, Sharma argues that most organizations remain at an extremely early stage of operational maturity.
“A lot of organizations are still early in their AI maturity journey,” she said. “Some companies are struggling to show measurable ROI not because AI lacks value, but because they are looking at the wrong metrics, chasing disconnected pilots, or deploying tools without tying them to actual business goals and workflows.”
That distinction — between technological deployment and organizational readiness — emerged repeatedly throughout the conversation.
According to Sharma, the biggest misconception in corporate boardrooms today is that AI readiness can simply be purchased.
“The biggest misconception is that AI readiness is mainly about buying technology. It is not,” she said bluntly.
“A lot of organizations think deploying copilots or running a few pilots means they have an AI strategy. The reality is that AI readiness is much broader. It involves data governance, leadership literacy, workforce education, change management, security, risk management, and often redesigning how work actually gets done.”
In Sharma’s view, many companies currently do not possess an AI strategy at all — merely what she describes as “an AI shopping list.”
“I was recently talking to department heads at a company and they told me they have orders from senior leadership that they need maximum usage of AI,” she said. “That’s not strategy. That’s pressure without clarity.”
Her warning comes at a moment when enterprises globally are under intense investor and competitive pressure to demonstrate visible AI adoption, often before governance structures, workforce training, or operational safeguards are fully established.
And that, Sharma argues, is where the real risk begins.
“AI Does Not Fix Broken Governance. It Scales It.”
While much of the public conversation around AI remains centered on productivity and automation, Sharma believes one of the most under-discussed dangers is the rise of AI-enabled workplace surveillance.
“AI absolutely can enable more surveillance. That concern is real,” she said. “These systems are built around data, monitoring, prediction, and pattern recognition, so there is naturally a temptation for organizations to push further into visibility and tracking.”
Her sharpest concern is what she calls “workplace surveillance disguised as productivity optimization.”
“We should be focused on outcomes, value, and impact — not monitoring every employee interaction simply because the technology makes it possible.”
Still, Sharma does not believe this outcome is inevitable. The determining factor, she says, will ultimately be governance.
“This is ultimately a leadership issue,” she said. “Organizations still make choices about how far they go, what guardrails they put in place, and whether they preserve trust and privacy.”
Then came perhaps the most defining line of the conversation:
“AI does not fix broken governance. In many cases, it scales the problems.”
That concern is increasingly resonating across corporate America as regulators, investors, and boards begin scrutinizing AI deployment practices beyond glossy product demonstrations.
“Responsible AI Is The Seatbelt”
Sharma says there is unquestionably more seriousness around Responsible AI today than even two years ago. Boards and regulators are paying attention. But operational maturity, she believes, still lags dramatically behind deployment speed.
“There is definitely more attention on Responsible AI today,” she said. “Boards, regulators, and leadership teams are taking these conversations much more seriously, especially in regulated industries.”
But she quickly adds a caveat.
“There is still a big gap between awareness and operational maturity.”
Many organizations, she says, continue treating Responsible AI as a compliance checkbox rather than an embedded operational discipline integrated into procurement, deployment, monitoring, and enterprise governance.
“I often say that Responsible AI and Governance act as seatbelts that allow organizations to accelerate more safely and confidently,” Sharma said.
“The reality is that organizations deploying AI without governance may move faster initially, but they also increase the likelihood of biased outcomes, security problems, compliance issues, and reputational damage.”
She pointed specifically to the Stanford University 2026 AI Index findings, which she says clearly show deployment accelerating faster than governance maturity.
“That is the challenge many organizations are wrestling with right now.”
“The Future Winners May Not Have The Best Model”
Despite concerns around concentration in AI infrastructure and frontier model development, Sharma does not believe the future belongs exclusively to tech giants.
“There is definitely concentration happening at the infrastructure layer,” she acknowledged. “Building and training frontier models requires enormous compute, capital, and talent.”
But that does not eliminate opportunities for startups.
“In fact, startups still have major opportunities, especially if they focus deeply on solving specific industry problems.”
According to Sharma, one of Silicon Valley’s biggest misconceptions today is believing that the model itself is the moat.
“In many cases, it is not,” she said. “Distribution matters. Workflow integration matters. Trust matters. Understanding a customer problem matters.”
She also believes open-source AI is fundamentally changing the competitive equation by lowering barriers faster than many incumbents expected.
“The long-term winners will not necessarily be the companies with the biggest models,” Sharma said. “They will be the organizations that integrate AI into real workflows in ways that create meaningful value.”
Then came another line likely to resonate far beyond the conference halls of SelectUSA:
“The future winners in AI may be determined less by who has the best model, and more by who solves the right problem.”
“Adoption Is Moving Faster Than Literacy”
Perhaps Sharma’s most important warning was not about technology at all — but about comprehension.
“Honestly, very few groups fully understand AI yet,” she said. “And that includes startups, enterprises, governments.”
In her view, the AI literacy gap is now becoming one of the defining risks of the entire technological cycle.
“Adoption is moving much faster than literacy,” Sharma warned.
And literacy, she insists, is not simply knowing how to use chatbots or write prompts.
“Real AI literacy means understanding the strengths, limitations, risks, governance implications, and failure modes of these systems.”
That gap creates dangers on both ends of the spectrum.
“A startup can move recklessly and create unintended harm quickly,” she said. “An enterprise can operationalize flawed systems at massive scale.”
Which leads to what may ultimately become the defining policy challenge of the AI era:
“The real risk is not just powerful AI. It is powerful AI combined with poor understanding and weak governance.”
As governments race to regulate, enterprises race to deploy, and investors race to monetize the AI boom, Sharma’s intervention at SelectUSA offered a reminder that the defining battle of artificial intelligence may not merely be technological dominance.
It may be whether institutions can mature fast enough to govern what they are building. |
