Stop scaling with people and demand services that act like software

CIOs and transformation leaders are running out of runway with labor-driven models that slow transformation more than they accelerate it. The traditional approach of throwing people at the problem and managing performance through service-level agreements can no longer keep pace. Enterprise leaders now face pressure to deliver faster, scalable change with measurable outcomes.

That was the core message at the Classic Car Club in Manhattan, when Mphasis hosted a select group of analysts and advisors to preview how it’s reshaping enterprise services. In a snug, closed-door setting (surrounded by some pretty cool cars!), the leadership team announced the launch of NeoIP™, Mphasis’s new platform approach that aims to productize service delivery, reimagine legacy transformation, and embed artificial intelligence (AI) at the core of the enterprise workflows.

Your business logic is trapped in legacy code that NeoIP extracts and converts into reusable intelligence

The biggest moment of the day was the reveal of NeoIP, Mphasis’s modular, AI-native platform designed to rewire how enterprises build, run, and transform their technology estates. At its core is OntoSphere, an intelligence ecosystem that ingests code, documents, and domain context to extract business logic and convert it into structured, reusable data. Mphasis calls it “turning source code into data,” a shift that makes business logic a shared asset, not locked in legacy systems or siloed in subject-matter experts’ memory.

While Mphasis formally introduced the NeoIP brand and unified architecture at the event, the underlying agents and engineering capabilities it brings together have already been in use with clients.

This suite of AI agents drives automation across the full transformation life cycle: NeoZeta™ extracts logic from legacy code, NeoSaBa™ turns that logic into user stories, NeoRaina™ guides and recommends the target architecture, and NeoCrux™ generates code. Together, they convert enterprise knowledge into deployable assets that accelerate change and reduce manual effort.

Use AI to turn decades of code into a faster change engine

In his keynote, CEO Nitin Rakesh argued that the real constraint on enterprise transformation is not technology, but the human effort required to understand decades of accumulated code, workarounds, and layered architectures. Each technology wave has added another veneer, making modernization progressively slower and more complex. Nitin described the shift enabled by AI as “software eating software,” the point at which systems can finally analyze the estate themselves, interpret embedded business logic, and generate the intelligence needed to redesign it.

This framing positions NeoIP not just as another engineering toolkit but as a different way of approaching change. Instead of relying on manual reverse-engineering, NeoIP uses agents to learn how systems operate today and convert that knowledge into reusable structures the enterprise can apply across modernization, engineering, and operations. It shifts the work of understanding legacy systems from people to AI, reducing dependence on scarce expertise and creating a more direct path from insight to action.

Clients are already seeing faster delivery, lower cost, and better outcomes

The firm backed this up with case examples of early implementations. In one engagement, a global asset servicer with more than 150 million lines of COBOL code asked Mphasis to accelerate its modernization efforts. Using capabilities now unified under NeoIP, Mphasis extracted logic from a 30,000-line code segment over a single weekend, achieving over 95% accuracy after two iterations. This demonstrated the potential to compress a process that often takes 12–18 months into a matter of weeks for individual components, laying the groundwork for broader modernization over time.

Across industries, early phases of NeoIP implementations are beginning to show promising traction. Insurers are targeting 30%–40% reductions in incidents and mean time to resolution by shifting from reactive to proactive resolution using AI-driven operations. Capital markets firms are aiming to cut onboarding time in half by automating document validation, compliance logic, and integration tasks. Within its own business process outsourcing operations, Mphasis is reducing unit costs and cycle times by applying NeoIP across underwriting and know-your-customer workflows. These engagements are structured around phased commitments, with outcomes delivered incrementally as implementations progress.

Mphasis is pairing this platform-led approach with a shift in how it structures its deals. More than 60% of new engagements are now AI-led, and the company is taking on greater delivery risk through outcome-based commitments, where pricing is no longer tied to input effort. This reflects what the leadership described throughout the day: if automation is handling more of the underlying work, value should be measured in outcomes, not hours. For enterprise buyers, this signals a willingness to align economic models with business impact.

The Bottom Line: Transformation fueled by headcount is a dead end. The future belongs to platform-driven partners that productize delivery, embed AI from the start, and take responsibility for achieving real outcomes.

CIOs should now evaluate every service engagement through this lens: Is your partner productizing delivery into reusable intellectual property, embedding AI from the start instead of bolting it on, and standing behind measurable business results rather than input effort? If the answer is no, you are scaling cost and complexity instead of intelligence, and firms that pass this test will define the next chapter of enterprise transformation.