Engineering Autonomous Enterprise

The next evolution in application services

June 3, 2026

11 mins read

Kunal Purohit

President - Next Gen Verticals, Tech Mahindra

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Legacy applications only execute predefined human instructions; true autonomy requires reengineering the core application architecture.
Embedding agentic AI into the core of enterprise applications enables autonomy across the full software lifecycle from development through operations and quality assurance.
TechM’s Agentic Development and Modernization Services (ADMS) equips enterprises with a unified engineering framework and specialized platforms to create and run autonomous applications at scale.

Over decades, enterprise software has followed a rather familiar arc, one that includes automating tasks, digitizing workflows, modernizing platforms, and scaling through the cloud, all of which have enabled agile delivery and improved efficiency. Through every wave, however, enterprise applications have stayed fundamentally the same, following predefined human instructions to execute and function.

This model has finally hit its natural limit.

We are entering a new phase of enterprise applications that demand systems that act autonomously by sensing context and reasoning continuously; all while being governed, secure and predictable. Analyst research indicates the enterprise agentic market projects to reach 24-46 billion by 2030, and 40% of enterprise applications will undergo agentification by the end of 2026.^1&2This growing trajectory marks the next shift in application services, one that matures from managed to autonomous systems.

Towards Autonomous Systems

Every enterprise is on its journey towards autonomy. While some are still optimizing their foundation, others are running agentic AI pilot programs. A few early adopters are already re-architecting core platforms to support agent-driven execution.

Across industries, leaders are converging on the same destination, which is for applications to move from passive execution to active agency. These are systems that don’t simply wait for instructions, they anticipate, decide, and act with operations that continuously improve at scale.

This shift is imperative for most enterprises. The mounting pressure from customer expectations and the cost of manually intensive operations are making autonomy a practical necessity.

The Engineering Challenge

Enterprises cannot achieve autonomy simply by layering AI onto applications. Rather, it should be engineered into the core design through code, platforms, quality, and operations.

As systems become agentic, complexities arise. Code changes constantly, platforms evolve from basic hosting to self-orchestration, operations shift from simple monitoring to active reasoning, and quality assurance adapts continuously. Without coherence across all these layers, autonomy is fragile and fragmented.

Without coherence across all these layers, autonomy is fragile and fragmented.

To effectively mitigate these changes, enterprises need partners with deep engineering expertise and a keen understanding of application estates and agentic behavior that can be embedded into production systems. This is the lens through which Tech Mahindra has reconfigured to provide Agentic Development and Modernization Services (ADMS) that enables building, operating, modernizing, enterprise applications for the autonomous era.

Four Pillars Enabling the Autonomous Enterprise

To usher in true autonomy, enterprises must move from isolated to collective initiatives. Such effort can be achieved through a coherent operating model that works across the following four pillars.

1. Modernizing Platforms for Autonomy

The key to any autonomous application is that it evolves from a static nature to self-serving processes. By re-architecting legacy application estates into modular API-first, AI-native foundations, enterprises can support complete agent-driven execution.

Case in Point

For a global automobile manufacturer, AI-powered analysis of 4,000+ components and 1.6 million lines of code delivered a 70-80% reduction in manual effort and up to 60% faster business rule extraction, laying the foundation for large-scale legacy modernization.

2. Agentic Software Engineering

Embedding agentic AI across the software lifecycle transforms development from task-driven execution to intent-led, adaptive engineering. AI agents assist and autonomously execute critical functions such as coding, refactoring, testing, and modernization, thereby improving quality delivery and productivity within product-aligned DevSecOps models.

Case in Point

A leading semiconductor manufacturer applied AI across the SDLC, that empowered 250+ engineers to deliver 40-50% higher productivity, ~48% faster delivery, and 47% lower cost per associate with human-in-the-loop governance.

3. Autonomous Operations

Autonomous Ops uses agentic AI and ML, supported by knowledge graphs, to shift towards preventive execution, in which anticipating issues and initiating corrective actions occur before disruptions.

At the core of this capability is the Autonomous Operations Center. It serves as the intelligence and orchestration layer, unifying observability, autonomous workflows, GenAI copilots, self-healing agents, and Vector Squads into a connected control plane. Together, they power self-learning operations that reduce manual effort, accelerate resolution, improve resilience, and scale with greater autonomy.

Case in Point

A leading European telco saw strong results with an agentic AI solution, which correlated customer intent with real-time network data and historical insights to enable accurate first-time diagnosis, improving first time right (FTR), reducing average handle time/mean time to resolution (AHT)/(MTTR), cutting repeat calls and truck rolls, and lifting net promoter score/customer satisfaction (NPS/CSAT).

4. Autonomous Quality Fabric

What differentiates autonomous applications from regular static applications is their independence in quality management. By integrating assurance directly into delivery pipelines, enterprises can generate agent-driven tests and self-heal assets as systems evolve. This approach, while maintaining quality, retains critical control and decision-making capabilities with humans.

Case in Point

A leading telecom company with AI-powered test case generation tackled the complexity of multi-layered document requirements, delivering a nearly 50% reduction in effort and establishing a scalable blueprint for intelligent enterprise testing.

Individually, these pillars deliver value. Together, they create a foundation for true enterprise autonomy.

Engineering the Shift from Effort to Outcome Economics

As intelligence is engineered into apps, platforms, and delivery workflows, the economics of application services begin to shift. Traditionally, models are built on human effort that scale linearly, keeping in mind human roles, capacity, and bandwidth. Agentic systems completely change these dynamics.

As agents operate independently, there is a ripple effect on delivery. Human dependency significantly decreases. Execution decouples from queues and hand-offs. Intelligence compounds over time as agents reduce rework across the lifecycle. Engineering effort moves upstream, increasing the importance of platform design, system architecture, and governance over manual orchestration.

In this context, TechM has introduced Vector Squads: outcome-oriented, agent-enabled delivery units in which humans and agents work together to achieve defined business objectives. This approach makes outcome-aligned constructs such as service tokens practical and effective. By combining build and run costs into predictable units tied to business processes rather than activity or headcount, these models provide autonomy that is engineered end-to-end, not delivered through isolated automation.

A Cohesive Engineering Approach to Autonomous Applications

What distinguishes successful autonomy programs is not a single technology or capability but engineering overall coherence. TechM delivers this coherence through a suite of purpose-built platforms that together support the full autonomous application lifecycle. Each platform in the suite is designed to embed agentic capability within its domain while operating as part of a connected whole.

Platform	What it Enables
AppGinieZ	Embeds agent assistance directly into software engineering, enabling continuous code development within governed DevSecOps models.
Swifter.io	Orchestrates end-to-end SDLC, coordinating agent-driven workflows across all stages from development to operations.
Reforge	Applies intelligent reverse and forward engineering to modernize complex application estates into modular, API-first, autonomy-ready platforms.
LitmusT	Automates end-to-end test scenarios across technologies, covering functional and non-functional testing.
New Age Delivery (NAD)	Serves as the agentic engineering orchestration layer, unifying autonomous workflows, reusable pipelines, digital assistants, and value stream intelligence into a connected delivery fabric.

Collectively, these platforms expedites software delivery with greater autonomy.

The Final Word

Enterprises are now confronting the limits of traditional, manually intensive application models. As the opportunity for incremental gains in legacy applications rapidly closes, the cost of delaying the shift to an autonomous enterprise is escalating faster than most organizations realize.

Institutions that treat this autonomous shift as a core engineering challenge will lead the new wave with increased speed and reduced risk. The mandate is clear: true autonomy cannot be assembled through isolated innovations. It must be deliberately engineered.

TAGS: Artificial Intelligence Enterprise Applications

Frequently Asked Questions

Our FAQ section is designed to guide you through the most common topics and concerns.

Autonomous applications go beyond executing predefined rules. They sense context, reason continuously, and take actions with minimal human input. Instead of waiting for instructions, they anticipate needs, adapt to changing conditions, and improve performance over time. Governance mechanisms still ensure safety and predictability.

Adding AI on top of legacy systems only enhances specific features. It does not change how the system fundamentally operates. True autonomy requires redesigning architecture, workflows, and governance so that intelligence is embedded across development, operations, and quality rather than added as isolated capabilities.

Autonomous applications typically require four core capabilities: modular and API driven platforms, agent-assisted software engineering, autonomous operations, and adaptive quality assurance. These must work together as an integrated system to enable continuous improvement, resilience, and scalable decision-making.

Tech Mahindra enables this through a coherent operating model across four pillars: modernizing platforms, agentic software engineering, autonomous operations, and autonomous quality fabric. These pillars work together to support agent-driven execution across the enterprise.

The defining factor is engineering coherence. Enterprises that align platforms, development, operations, and quality into a connected system are able to scale autonomy, rather than relying on isolated innovations.

End Notes

About the Author

Kunal Purohit

President - Next Gen Verticals, Tech Mahindra

Kunal is President–Next Gen Verticals at Tech Mahindra. Under his leadership, the organisation is advancing our AI first positioning, driving verticalized growth, driving the shift to intelligent, self evolving enterprise operating models and spearheading agentic engineering, alongside expanding our portfolio with platforms like TechM Orion that have received global recognition.

Kunal also serves on the Mahindra Group’s Technology Council, a leadership forum that shapes key technology and platform decisions across the Group’s diverse businesses.

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AI adoption is broad and accelerating, but the underlying model that organizations follow, be it for competing, serving customers, or pricing their service delivery, is largely intact.The law of diminishing competitive returnsAI technologies are available to every player in the market today. Most organizations deploy the same set of tools against similar workflows and the gains experienced become industry baseline. AI investments may be soaring yet sustained financial impact on performance is elusive.This point is further reinforced by data. MIT Sloan Management in 20251 confirmed that AI may become pervasive, enhance processes, operations, but it does not provide any differentiated advantage to the users. Productivity gains powered by AI are self-eroding in the long haul. Both McKinsey2 and BCG3 put this more plainly in their respective global studies that even with near-universal adoption of AI (nearly 90-94% of global enterprises surveyed) only a tiny fraction (5-6%) generate substantial financial value from AI.So, what can these companies do differently?What AI makes newly possibleFor starters, there has to be a clear idea of how to deploy AI, focused on the new possibilities it can create for their competitive strategies.We can categorize this further into three distinctive shifts.The first shift is when they use AI to access customers and market segments that were previously considered uneconomic. Be it targeting lower price-point segments, expanding into new geographies, or addressing an underserved customer cohort, these become feasible as delivery cost reduces, response time accelerates, and the quality of personalization at scale improves. This is the argument made for revenue, not efficiency. Productivity is nice, but revenue is the most critical piece.The second shift is when enterprises reshape how they deliver outcomes and pricing accordingly. Forward-looking organizations assess how work can be restructured to ensure AI-driven outcomes are contractible, reliable, and measurable. When you produce and measure outcomes consistently, the commercial relationship with customers shifts, aligning price with the value delivered and not the effort expended.The third shift centers around how AI is treated as a lens for entering categories and capabilities that were structurally out of reach previously. This can be considered a massive transition from capital-heavy, monolithic corporate structures to a model of lean disruption. With the right stack, an AI augmented team, lower build costs, and faster iterations, these companies are able to move into new service/product lines with less time and capital.The fact is only a few firms are converting AI into enterprise value at scale. Those that do are widening the gap with their competitors in growth, margins, and shareholder returns. They are compounding the advantage, and competitors’ opportunity to secure it is narrowing.What is holding the majority back from making these shifts?Efficiency is table stakes, but growth is the real prizeThe real barrier is not technical, but organizational and gravitational.Geoffrey Moore, in work that followed Crossing the Chasm, described what he called the pull of the past. It was a simple observation about the tendency successful companies have when it comes to letting go of the business model that made them successful. They will keep optimizing it even after the ground beneath them shifts.Look at how this plays out when it comes to AI in 2026, where this pull takes a specific form. The existing operating model rewards efficiency gains, and this shows up in margins and utilization metrics that are tracked by boards quarterly. 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Instead, it is an opportunity to initiate it with clear priorities and genuine organizational commitment. The companies that generate durable advantage from AI are not necessarily the ones that start with clean infrastructure. Instead, they are those that start with a clear answer to the question of what they were building toward.Separating adoption from advantageOrganizations today shouldn’t be focused only on how much AI they are using. Nor should they only use conventional productivity metrics to measure ROI. Rather, they should question the many ways AI can be used to do something that compounds, opening new revenue streams, creating new commercial relationships, evolving capabilities that deepen with use.At its core, the shift from adoption to advantage is in the strategic question being asked. This does not necessarily require new tech investments but instead a strategic frame that questions what AI makes newly possible for the organization.

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When we see the Level 4 progress, it is concentrated in specific domains that includes fault management, energy optimization, and certain transport layers. Telefónica is running 12 Level 4 use cases across three countries. China Mobile reports1 an 80% drop in major network faults across eight Level 4 domains. While considered solid outcomes, the pace of improvements across the entire operator base is still not progressing fast enough.Around 20% of operators have reached Level 4 or 5 in selected domains according to Bain-TM Forum 2025 survey2. McKinsey’s research3 points out that AI-driven operational use cases are cutting network OpEx by 15-30%, but only when workflows are redesigned end-to-end and not just automated as they are today.Three traits only operators can ownWhen I look at the operators who’ve moved AI from proof of concept to production, three traits show up repeatedly.The operator owns the decisions.The operator owns scenario selection while the vendor executes. Telcos hitting Level 4 today have picked two or three scenarios where they could measure a closed loop (MTTR, truck rolls, energy, SLA breaches) and run those as legitimate production programs rather than extended pilots.AI learns from the operator’s network memory.In our experience, operators who redesign workflows around AI capture lasting value. In comparison, those who simply embed AI into existing processes mainly do the same things a bit faster. McKinsey’s 2026 research study4 highlights that three-quarters of the surveyed telco executives cite weak change management is their biggest barrier to scaling AI.Delivery always starts now.Successful AI programs are built for incremental delivery and never for multi-year transformation projects. On average, European operations carry 56% tech debt. Some of the OSS systems have been running for more than two decades. This is never a question of whether modernization is overdue but about how to get this done without taking on too much risk.Telefónica’s Autonomous Network Journey (ANJ) program offers the insight that one can embed AI directly into network platforms rather than running it as a separate five-year program. A number of studies show that 80–95% of AI pilots in large enterprises never reach production-grade impact. The root cause is attributed to a delivery rhythm that doesn’t tie each release to a specific, measurable outcome.What telcos must do in the next two yearsIn the first phase, which is the next 90 days, inventory the network knowledge that AI will depend on. This includes topology, configuration history, fault hierarchies, change records, and vendor-specific operational quirks. This will build a strong foundation that determines if the AI investments made are actually producing reliable results.In the next six to 12 months, pick two high-value scenarios and run them as real production programs. You can find a closed loop that can be measured. The most common starting points are fault management and service assurance among leading operators, but TM Forum’s high-value scenario list has plenty of others.Rather than launch yet another monolithic OSS transformation, develop an incremental modernization roadmap where each phase is tied directly to a P&L target. At Tech Mahindra, we are working for a Tier‑1 European operator, wherein our closed-loop automation approach is targeting a 60% reduction in field costs and a 55% reduction in MTTR. These numbers come from scoping tightly, grounding AI in the operator’s own network data, and letting go of the five‑year program model.Pitfalls to navigate and the value unlocked from scaling AIWhile moving from proof-of-concept to production, there are a few pitfalls to navigate, and this can be done if we keep the following three factors in mind.First, it helps to build an underlying data fabric that connects network topology, configuration history, fault hierarchies, change records, and even vendor-specific operational quirks. Without it, even the best AI models struggle to deliver reliably. Second, a ‘North Star’ architecture for scaled deployment is essential. This cannot be a rigid, multi-year blueprint. It has to be something sustainable and flexible, which can absorb new use cases, models, and operational demands over time. And third, a robust governance process around AI, including responsible AI principles. This makes sure automation behaves predictably when it meets the complexity of a live network.When you scale AI thoughtfully across network operations, the benefits become tangible.Workforce: A different kind of workforce becomes possible, where AI agents and human teams work together, each doing what they do best, enabling focus on higher-value decisions.MTTR: Mean time to repair improves dramatically when you embed AI into fault management and service assurance processes. We see faster detection, better diagnosis, and in some cases, automated healing.Agility: The ability to launch new products and services accelerates when AI is embedded in network operations. This kind of speed significantly benefits customers, given the quick responses, reliable experiences, and services that adapt to real demand.The real focus areaThe difference between operators scaling AI successfully and those still working through pilots isn’t fundamentally a technology gap. The platforms are widely available, and the models are increasingly commoditized. The real opportunity lies in the operating discipline that includes data fabric, architecture, governance, and incremental delivery.

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