From AI Pilots To Production: Why Trust Set The Pace In 2025

For enterprises, 2025 was not the year of louder AI ambitions; it was the year of harder truths. After years of pilots, proofs of concept, and experimentation, organisations began confronting a more uncomfortable question: can AI actually run parts of the business safely and at scale?

In an interaction with CiOL, Phani Tangirala, MD & CEO, Expleo Solutions Limited, reflects on how enterprises moved from innovation theatre to operational accountability, why consulting became execution-first, and how trust, governance, and quality engineering are now inseparable from speed.

Rather than chasing novelty, enterprises spent 2025 building what Tangirala describes as the “grid” behind AI, governance, data quality, security, and assurance frameworks that allow intelligent systems to operate reliably in the real world.

During the discussion, Tangirala emphasised that the most important shift in 2025 was not technological; it was organisational.

Enterprises stopped treating AI as a feature and began treating it as infrastructure. That meant moving beyond pilots to outcome-led deployments tied directly to resilience, efficiency, and measurable business impact. At Expleo, this translated into scaled engagements across AI, data governance, application modernisation, DevSecOps, automation, and security, areas where integration, not experimentation, determined success.

He also pointed to a widening gap between intent and readiness. While executive optimism around AI remains high, governance maturity, data discipline, and talent availability continue to limit how fast organisations can move without creating risk.

Looking ahead, Tangirala sees 2026 as the year when execution discipline, AI assurance, and quality engineering will define who scales sustainably and who stalls.

Interview Excerpts

How did 2025 mark the shift from innovation pilots to enterprise-wide impact across industries?

2025 stripped away the illusion that innovation alone creates value. Enterprises stopped treating AI like a shiny new toy and started asking harder questions about whether it actually runs the business. Leaders recognised that innovation was no longer the challenge; integration was. As Andrew Ng said, ‘AI is the new electricity,’ but for years, most organisations were running it on extension cords. In 2025, the focus shifted to building the grid, with governance, data security, and quality frameworks that enable AI to scale responsibly. AI and automation have matured from proofs of concept into operational layers, with outcome-centric deployments tied to resilience, efficiency, and measurable business impact.

At Expleo Solutions Limited, we saw this shift play out as customer engagements scaled across AI, data governance, application modernisation, DevSecOps, automation, and security. By treating AI as core infrastructure rather than a feature, we’ve helped our clients move from pilots to production with confidence. That mindset will define execution, trust, and autonomy in 2026 and beyond.

Why did digital and transformation consulting become more execution-focused in 2025, and how will this evolve in 2026?

Our survey at Expleo revealed that over 90% of senior executives believe AI’s benefits outweigh its risks and that 72% of businesses plan new AI deployments in the coming year. But for most, ambition wasn’t the bottleneck; execution was. 2025 became a clear game of value over volume. Clients prioritised outcomes over intent. They wanted partners who could tie roadmaps to measurable outcomes, balancing AI and cloud modernisation with change management, governance, and AI assurance. Optimisation and workload redesign mattered more than migration counts. ROI, resilience, and automation quality became the real scorecards.

That shift explains why consulting became more execution-focused. Clients leaned on external partners to bridge the gap between strategy and reality. They aren’t buying roadmaps anymore. They want partners who can build, deploy, and run systems end-to-end, with accountability built in. Heading into 2026, with embedded AI and multi-agent orchestration, execution will be the differentiator, and AI assurance will be the prerequisite. Expect ‘advise–build–run’ models to go full throttle, anchored in responsible AI, measurable ROI, and systems that actually hold up in the real world.

What lessons did enterprises learn in 2025 as AI moved from experimentation to scaled deployment?

The biggest lesson of 2025 was that uncomfortable but necessary governance has to come before scale. As AI adoption accelerated, enterprises learnt that unless technology goals are aligned with business outcomes and robust risk controls are in place, scaled deployment only magnifies chaos. Poor data quality and unclear accountability are execution blockers, not technical issues.

In India, intent is high, but readiness remains uneven, driven by talent constraints and immature governance. Despite optimism, over 95% of organisations still allocate less than 20% of their IT budgets to AI, pointing to a clear gap between intent and execution (EY-CII Report). You can’t scale chaos, and responsibility cannot be retrofitted. That’s why quality engineering is the stabiliser – designing workflows where humans and AI collaborate by default, with assurance built into the lifecycle. Optimism alone doesn’t fund transformation; businesses must invest where impact is provable.

How did product engineering redefine speed, resilience, and sustainability for businesses in 2025?

Product engineering in 2025 shifted from velocity-first to consequential, with operational, regulatory, and reputational consequences. AI-driven engineering, simulation, and digital twins helped teams move faster, but they also raised expectations around resilience, auditability, and accountability. Speed was no longer about time-to-release. It was about how resilient systems were under change, how transparently they behaved, and how defensible decisions were under scrutiny.

Sustainability also stopped being a checkbox and moved upstream into engineering decisions. Design choices were increasingly tied to energy use, materials efficiency, and lifecycle impact, informed by academic and scientific models that linked design intent to measurable environmental outcomes.

At Expleo, engineering and digital came together through AI assurance and advanced tooling, strengthening digital resilience while helping clients meet sustainability targets, making performance and sustainability inseparable by design.

Why did quality engineering and testing emerge as strategic priorities in 2025?

Quality engineering and testing have always been strategic priorities, and in 2025, the focus was even greater because speed in embracing new technologies without trust breaks businesses. As GenAI introduced adaptive system behaviours, traditional testing couldn’t keep up. Quality failures stopped being technical inconveniences and began to manifest as business risk, regulatory exposure, and lost customer trust. That’s when buyer expectations shifted. In addition to test coverage, they wanted continuous validation, domain accelerators, and early signals on quality and risk. Quality engineering had to be embedded across the SDLC, predictive by design, and increasingly autonomous. Growth and resilience now depend on AI-augmented quality engineering.

What technology and engineering shifts should enterprises prepare for as they plan for 2026?

Several themes are hard to ignore in 2026.

First, AI will become the backbone of enterprise architecture. That means clearly defined boundaries, human oversight by design, and bounded autonomy for intelligent agents – so they can act but remain accountable.

Second, intelligent operations must be institutionalised. Digital twins, scenario planning, and end-to-end traceability will become standard. This is how organisations build resilience, stress-test decisions, and stay compliant without slowing down.

Third, trust will become the operating system for scale. AI Assurance has to evolve into a growth strategy. GenAI-augmented quality engineering, solid data governance, and shift-left/shift-right practices are what keep speed and trust moving together.

Cloud spend will also face sharper scrutiny. FinOps discipline, workload optimisation and secure-by-design architectures will define ROI. Sustainability will move inside engineering, with energy, materials, and circularity treated as performance metrics, not aspirations.

And underneath all of this sits the fundamental constraint – talent. 2026 will favour organisations that invest in people who can supervise, challenge, and govern AI – not just deploy it. Technology scales. Capability decides whether it lasts.