While the market remains captivated by the promise of an AI revolution, enterprises are quickly discovering that bridging the gap between a glossy sales pitch and real-world implementation is far more complicated than the hype suggests. 

As President of UST, Manu Gopinath is currently spearheading the company’s shift toward AI-powered platforms, drawing on his previous six-year tenure as COO where he built the firm’s enterprise consulting division. 

“There’s a lot of enthusiasm as much as there is apprehension,” he says. “Over the last 18 months, there’s been a lot of enthusiasm. But now, the reality is large multi-billion dollar enterprises are dealing with thousands of different applications in their environment without a lot of integration between the different systems. 

“A lot of patchwork is needed to make it all run together,” Manu continues. “For AI to work effectively, they need cleaner data and that’s a big challenge. It is a complex job to get data ready for AI systems.”

This is where the apprehension comes in, once the initial enthusiasm from the AI sales pitch wears off.

“In a sandbox,” Manu goes on, “you see a lot more accuracy in terms of how a system works but when you put it into real production data, the accuracy is not that high. This is what people are learning. 

“The solution is a redesign of your workflow, which takes time. So the belief that you can scale rapidly doesn’t happen that easily. I wouldn’t say they are insurmountable tasks but it is the operating reality.”

To navigate this gap between sandbox potential and production-level accuracy, forward-thinking organisations are shifting their focus toward hybrid workflows – redefining how human oversight and AI agents coexist on the factory floor and in the back office. 

“Everyone’s aware of what could be the impact of not having a human in the loop,” adds Manu. “I see agents coming into an organisation and changing the business in two ways. 

“The human primarily out of the loop is when low-risk, high-volume, repeatable tasks can be looked after by AI. Then, there are some workflows where the human is required to give a trigger or review work.”

For example, an AI agent monitoring factory machinery can predict an imminent malfunction and alert an engineer, proposing a specific fix for human approval before taking action. 

Manu explains: “Organisations are looking at the risk profile of the task or the workflow that is getting automated by the agent and then dividing that accordingly by which needs reviewing or more testing, or governance and strict controls. 

“Now over time, we believe the error rates will reduce and so the accuracy or predication rate will improve. This is when tasks may get further automated, but that level of comfort is still not there in the enterprise yet. It’s going to take a little bit more time before companies are just going to let the agents run their mission-critical operations. 

“I draw a parallel with the self-driving capabilities in some of the electric vehicles. They still give that, ‘Hey, we need your hands on the steering wheel’. While it can do things, there’s always that caution. So the human judgement evaluation analysis, governance control, all of that is still there.”

While keeping a human in the loop ensures operational safety, it also changes the financial equation, forcing leaders to look at specific efficiency metrics to prove the tangible ROI of their AI spend. 

“The real costs involved with AI infrastructure are not all clear yet,” asserts Manu. 

“If I were to draw some parallels to the cloud computing era, it was heavily discounted, now the real costs are showing up and the CFOs are asking questions to the technology leaders as to why the cost is up. I think we may get to that stage, but right now we’re in the mode where everyone’s looking at obvious productivity benefits. 

“For example, a healthcare company would ask ‘what is the cost per claim?’ or a service management provider would ask ‘can I reduce the ticket cost per case?’.”

Other metrics like reduction in time to market, time to resolution or time to decision are also proof of ROI for AI investments. 

“Customers are looking at solving business problems and getting to an outcome,” says Manu. “Most leaders are spending millions on managing the workflow and want to make it more efficient at a lower spend.” 

AI’s real-world impact

When discussing the tangible ROI of modern enterprise tech investments, Manu points to automation and advanced AI as the primary catalysts for institutional change. 

Rather than speaking in hypotheticals, he grounds the conversation in two of the most friction-heavy sectors of enterprise operations: insurance claims and customer service.

“Without naming specific clients, we are seeing profound shifts in how organisations handle complex, data-heavy workflows,” Manu explains. 

He notes that, while traditional software optimisation previously helped accelerate these processes, the advent of generative and advanced AI has fundamentally shifted the baseline.

A prime example lies in health insurance claims processing. Historically, evaluating a claim required navigating disconnected legacy databases and manually cross-referencing intricate business rules. By deploying AI systems capable of instantly ingesting data from multiple disparate systems and interpreting complex policies simultaneously, the processing bottleneck has effectively evaporated.

“Beyond a dramatic reduction in processing errors, the turnaround times have shifted completely,” Manu says. “We’ve seen high-value claims that previously took five to six days to process now being completed in just 15 to 20 minutes. That is a massive, structural reduction in friction.”

Manu sees a similar paradigm shift occurring in enterprise customer service, particularly where agents handle inquiries from corporate clients governed by highly-specific, customised contracts. 

In a traditional setup, an agent might spend four to five minutes just “warming up”, which is time spent fumbling through documents and tiered systems just to understand the unique business rules that apply to the caller.

To solve this, Manu’s team introduced automated systems for a UST customer that instantly parse service contracts and business rules the moment a call is initiated, serving up nuanced, actionable insights to the agent in real time.

“Instead of forcing the agent to rope through different levels of documentation, the AI gives them the exact context they need immediately,” details Manu. “This allows the agent to respond much faster and provide a far more meaningful interaction. 

“Ultimately, by effectively utilising AI to support the human agent, the end-user experience becomes significantly better.”

For Manu, these use cases illustrate that the true value of strategic AI investment isn’t just about adopting newer tech – it is about driving near-instantaneous processing times, eliminating human error and delivering a vastly superior experience for the end customer.

Bridging silicon and software

Looking ahead, Manu sees the next wave of technological transformation moving beyond the screen and into the physical world. 

While digital AI continues to reshape operations, he points to physical AI – the convergence of advanced software with physical hardware and infrastructure – as a growth vector for the business.

“We are already doing substantial work in compliance within the physical AI space,” he says, “identifying smart factories, robotics, mobility and medical environments as the primary sectors poised for rapid evolution. 

“This is truly the next frontier of opportunity. We are looking closely at hardware-software interfaces, platform integration, embedded software and entirely new automated tooling frameworks.”

At the heart of this strategy is a focus on the point of convergence where hardware capabilities meet intelligent software design. 

Manu views this intersection not just as a technical challenge, but as a significant market differentiator: “Our core strength lies in our ability to bridge silicon with software. We position ourselves right at that interface and our clear objective is to take advantage of this shift and remain a leading player in the market.”

To capture this momentum, Manu emphasises that having the right capabilities on the ground is just as critical as the technology itself. 

Global horizons and the AI reality check

As UST looks to the future, its growth strategy is distinctly global, with a particular focus on rapidly-evolving markets. 

When asked which regions are poised for the most surprising AI adoption over the next one to two years, Manu points to the unique dynamics driving both established and emerging economies.

“While the Australia and New Zealand market remains incredibly strong, mature and stable, we see immense, untapped opportunity in Latin America,” he shares. “Whether it is cybersecurity, banking, cloud transformation or localised industrial shifts, several countries in LATAM are experiencing significant momentum, creating a prime environment for AI-driven innovation.”

UST’s regional strategy is already deeply nuanced. The company has operated in Mexico for over a decade, a market Manu notes is showing robust growth in manufacturing and financial services. 

Meanwhile, Chile is emerging as a hotbed for cloud transformation, Colombia has become a vital talent hub for data and cybersecurity, and Brazil remains a massive, largely unpenetrated market.

This geographical expansion is happening against the backdrop of a larger, highly-debated question: is the tech sector currently in a sustainable AI growth phase, or are we on the precipice of an AI winter driven by unmet ROI expectations?

“I don’t have a crystal ball, but my view is that AI spend will continue to increase,” Manu says, pragmatically. “The shift is real. While certain elements of the technology may be overhyped, the tangible benefits are undeniable.”

For individual consumers, Manu notes that the cost of research and information synthesis has plummeted. But for enterprises, the value proposition is anchored in radical productivity gains, deep automation and reduced cycle times. 

He acknowledges the unprecedented scale of capital currently flooding the market, noting that the sheer volume of investment into AI infrastructure – from chip manufacturing to massive data centers – is historically unmatched. 

This capital injection extends all the way down to the application layer, creating a hyper-accelerated startup ecosystem.

A smiling Manu continues: “We are living through a remarkably intense phase where ideas are getting funded before a single line of code is written. The current euphoria won’t last forever and we will inevitably see a level-setting. 

“But that correction is precisely where solid, next-generation companies will scale. We will eventually reach a critical mass where enterprises and consumers are utilising AI seamlessly as part of their daily workflows, without even realising it.”

Ultimately, Manu remains highly optimistic about this technological epoch and UST’s role within it.

“The potential for AI agents and machines to solve everyday business problems and elevate human productivity is practically limitless,” he concludes. 

“Our role right now is to help our clients navigate this transition and secure a distinct marketplace advantage. 

“Our thought leaders are on the ground, running workshops and working side by side with enterprises to build these systems. We are deeply optimistic about how this will shape both the economy and society at large.”