Re(AI)magining AI as a System to Deliver Enterprise Outcomes
The question is no longer whether AI can create value. The question is whether enterprises have the architecture to scale that value.
Enterprise AI has moved from promise to priority. Spend is rising, roadmaps are ambitious and boards have made AI a strategic mandate. The opportunity now is to turn that commitment into results that scale across the business.
A capable model is no longer the scarce part. Powerful models are widely available, and most enterprises can deploy one in weeks. What decides whether AI delivers at scale is the architecture around the model: the systems it connects to, the data it can trust, the governance it runs under, the overall trustworthiness of the entire system and the way it works alongside people and existing processes. That is the architecture challenge Persistent is built to solve.
On its own, a model has no governed environment to run in, no trusted enterprise knowledge to draw on and no way to work with the people and systems that run the business. It stays a capable tool in one corner of the enterprise. Impressive in a demo, hard to scale.
Closing that distance is what the 3C Framework was built to do. Persistent’s approach to scaled enterprise AI rests on three layers working together: Core, Context and Coordination. Core provides the governed foundation. Context gives AI trusted enterprise knowledge. Coordination connects people, agents, applications and systems into an accountable and trustworthy execution.
Together, these three layers form the architecture behind Persistent’s AI strategy, connecting our platforms, delivery models, Responsible AI practices and client outcomes into one operating model for enterprise transformation.
This is how AI moves from experimentation to execution.
The same architecture runs across Engineering Hyper-Productivity, Business Hyper-Productivity and Enterprise Data Readiness, built on proprietary IP: 125+ patents filed in 15 months spanning the full stack of enterprise AI execution.
The 3C Framework: Core · Context · Coordination
The architecture that turns scattered AI investments into enterprise-wide results.
The AI-Orchestrated Enterprise
THE VALUE ARCHITECTURE
The enterprises scaling AI are no longer treating it as a capability to bolt on. They are treating it as an operating model to rebuild.
Scaled AI does not sit on top of existing systems and processes. It runs through them, removing handoffs, preserving institutional knowledge and connecting decisions directly to execution so intelligence shapes the way work actually moves across the enterprise. That is a structural change in how the business runs.
Making that work calls for infrastructure that can govern AI at scale, data the model can trust, workflows that agents can take part in and clear points where people stay in control, wherever judgement, empathy, compliance and accountability matter, thus ensuring that the entire system is trustworthy.
Persistent built the 3C architecture in response to exactly these realities. As Customer Zero, we apply our own platforms, architectures and methodologies inside Persistent before taking them to clients, testing what works, measuring outcomes, strengthening governance & trust and turning internal learning into client-ready execution models. The result is an AI strategy grounded in enterprise architecture rather than isolated tools.
In the Core layer, the governing principle is abstraction: separating what AI does from how it runs. When a new model arrives, Core absorbs it through its routing layer. When a new agent framework emerges, Core provides the runtime, the governance policy and the sandbox. Existing workflows keep running while new capabilities plug in without disruption. Core also makes AI spend legible. Every model call carries an identity and a cost, so when finance asks what AI costs, the answer comes back by team, workflow and business unit. Model choice becomes a policy decision and Responsible AI enforcement moves from policy intent to operational control.
In the Context layer, even a powerful AI system remains limited if it is detached from how the organisation actually works. Context closes that gap with four things working together: an ontology that models the business, a live knowledge graph that captures how customers, products, policies and events connect, a memory that carries institutional knowledge across sessions and a semantic layer that ensures every agent interprets business terms identically. When an agent can resolve ‘our top customer’ against the graph, check the definition of ‘revenue’ against the semantic layer and recall a similar analysis from three months ago, it produces answers the business can act on. Domain-tuned models extend this further, with purpose-built models tuned on enterprise data for accuracy in tasks such as claims adjudication, chart review, underwriting and code analysis.
In the Coordination layer, every action traces to a defined business process rather than a single prompt. People stay in the loop where they add value, in judgement, empathy and accountability, and step out where they do not. In practice, a sales agent, a pricing model, a compliance officer and an ERP platform can all take part in the same workflow, with clear handoffs, approval gates and a complete audit trail. Because agents work across vendors and tools through open protocols such as MCP and A2A, workflows cross system boundaries without brittle point-to-point integrations. Agents are created, tested, versioned and deployed to a common standard, which is what makes them reusable, governed assets rather than one-off scripts that break when requirements change.
The enterprises getting the most return from AI are not adding it to their operations. They are rebuilding their operations around it.
From Capability to Operating Model
THE SHIFT
80%
Navigating the Failure Trap with AI Value Compass
IIM AHMEDABAD × PERSISTENT
With the AI Value Compass, Persistent helps bring structure to that momentum, turning AI opportunity into scalable execution and measurable long-term value.
That is the challenge Persistent set out to address with the AI Value Compass.
Developed in collaboration with IIM Ahmedabad, the AI Value Compass enables leaders to make confident decisions on AI initiatives. It brings together Persistent’s enterprise execution insight across approximately 100 enterprises across eight industry segments and IIM-A’s academic depth in decision science, management and behavioural research. The result is a practical evaluation model that helps leaders assess AI initiatives early, compare them objectively and translate ambition into execution.
Most enterprises have strong AI ambition and growing momentum.
A Strategic Framework for Evaluating AI Initiatives
AI focus remains concentrated in operations, sales and customer service, and IT
Generative AI pilots stall before production
95%
Enterprise AI investments are primarily driven by efficiency and productivity gains
50%
Quality. Freshness. Infrastructure readiness. Reduces hallucination and scale risk
Five Vectors. One Decisive Path.
The AI Value Compass evaluates enterprise AI initiatives across five critical vectors:
Data
Value clarity. ROI. Time to impact. Prevents low-value experimentation
Business
Explainability. Compliance. Controls. Enables safe, responsible enterprise adoption
Risk & Governance
Adoption readiness. Trust. Change friction. AI scales only when people adopt it
People
Workflow integration. Reliability. Productivity. Ensures AI can run inside the business
Operations
The output of the AI Value Compass is not just a score. It is a decision.
The framework helps leaders move from evaluation to action by combining structured scoring with informed human judgement. Scores reveal strengths, gaps and hidden dependencies across the five vectors. Human judgement then interprets those signals in the context of enterprise priorities, sequencing, risk appetite and strategic ambition.
To make that judgement actionable, initiatives are mapped onto the Execution Prioritisation Matrix, which plots Strategic Alignment against Operational Alignment. This creates a clear path forward by showing whether an initiative should be accelerated, strengthened, sequenced for later, or paused.
This shifts the conversation from intuition and fragmented debate to a shared, evidence-based language for deciding where AI investment should go next.
For enterprises ready to move from AI ambition to measurable outcomes, the AI Value Compass provides a sharper starting point and a more disciplined path to scale.
From Scoring to Decisive Action
persistent.com/insights/navigating-the-failure-trap-with-ai-value-compass/
Scan to download the AI Value Compass report
Based on Persistent Systems' analysis of approximately 100 enterprises across banking, financial services, insurance, healthcare, life sciences, software, hi-tech and emerging industries, in collaboration with IIM Ahmedabad.
The enterprises scaling AI are no longer treating it as a capability to bolt on. They are treating it as an operating model to rebuild.
Scaled AI does not sit on top of existing systems and processes. It runs through them, removing handoffs, preserving institutional knowledge and connecting decisions directly to execution so intelligence shapes the way work actually moves across the enterprise. That is a structural change in how the business runs.
Making that work calls for infrastructure that can govern AI at scale, data the model can trust, workflows that agents can take part in and clear points where people stay in control, wherever judgement, empathy, compliance and accountability matter, thus ensuring that the entire system is trustworthy.
Persistent built the 3C architecture in response to exactly these realities. As Customer Zero, we apply our own platforms, architectures and methodologies inside Persistent before taking them to clients, testing what works, measuring outcomes, strengthening governance & trust and turning internal learning into client-ready execution models. The result is an AI strategy grounded in enterprise architecture rather than isolated tools.
In the Core layer, the governing principle is abstraction: separating what AI does from how it runs. When a new model arrives, Core absorbs it through its routing layer. When a new agent framework emerges, Core provides the runtime, the governance policy and the sandbox. Existing workflows keep running while new capabilities plug in without disruption. Core also makes AI spend legible. Every model call carries an identity and a cost, so when finance asks what AI costs, the answer comes back by team, workflow and business unit. Model choice becomes a policy decision and Responsible AI enforcement moves from policy intent to operational control.
In the Context layer, even a powerful AI system remains limited if it is detached from how the organisation actually works. Context closes that gap with four things working together: an ontology that models the business, a live knowledge graph that captures how customers, products, policies and events connect, a memory that carries institutional knowledge across sessions and a semantic layer that ensures every agent interprets business terms identically. When an agent can resolve ‘our top customer’ against the graph, check the definition of ‘revenue’ against the semantic layer and recall a similar analysis from three months ago, it produces answers the business can act on. Domain-tuned models extend this further, with purpose-built models tuned on enterprise data for accuracy in tasks such as claims adjudication, chart review, underwriting and code analysis.
In the Coordination layer, every action traces to a defined business process rather than a single prompt. People stay in the loop where they add value, in judgement, empathy and accountability, and step out where they do not. In practice, a sales agent, a pricing model, a compliance officer and an ERP platform can all take part in the same workflow, with clear handoffs, approval gates and a complete audit trail. Because agents work across vendors and tools through open protocols such as MCP and A2A, workflows cross system boundaries without brittle point-to-point integrations. Agents are created, tested, versioned and deployed to a common standard, which is what makes them reusable, governed assets rather than one-off scripts that break when requirements change.
The enterprises getting the most return from AI are not adding it to their operations. They are rebuilding their operations around it.
From Capability to Operating Model
THE SHIFT
The enterprises scaling AI are no longer treating it as a capability to bolt on. They are treating it as an operating model to rebuild.
Scaled AI does not sit on top of existing systems and processes. It runs through them, removing handoffs, preserving institutional knowledge and connecting decisions directly to execution so intelligence shapes the way work actually moves across the enterprise. That is a structural change in how the business runs.
Making that work calls for infrastructure that can govern AI at scale, data the model can trust, workflows that agents can take part in and clear points where people stay in control, wherever judgement, empathy, compliance and accountability matter, thus ensuring that the entire system is trustworthy.
Persistent built the 3C architecture in response to exactly these realities. As Customer Zero, we apply our own platforms, architectures and methodologies inside Persistent before taking them to clients, testing what works, measuring outcomes, strengthening governance & trust and turning internal learning into client-ready execution models. The result is an AI strategy grounded in enterprise architecture rather than isolated tools.
In the Core layer, the governing principle is abstraction: separating what AI does from how it runs. When a new model arrives, Core absorbs it through its routing layer. When a new agent framework emerges, Core provides the runtime, the governance policy and the sandbox. Existing workflows keep running while new capabilities plug in without disruption. Core also makes AI spend legible. Every model call carries an identity and a cost, so when finance asks what AI costs, the answer comes back by team, workflow and business unit. Model choice becomes a policy decision and Responsible AI enforcement moves from policy intent to operational control.
In the Context layer, even a powerful AI system remains limited if it is detached from how the organisation actually works. Context closes that gap with four things working together: an ontology that models the business, a live knowledge graph that captures how customers, products, policies and events connect, a memory that carries institutional knowledge across sessions and a semantic layer that ensures every agent interprets business terms identically. When an agent can resolve ‘our top customer’ against the graph, check the definition of ‘revenue’ against the semantic layer and recall a similar analysis from three months ago, it produces answers the business can act on. Domain-tuned models extend this further, with purpose-built models tuned on enterprise data for accuracy in tasks such as claims adjudication, chart review, underwriting and code analysis.
In the Coordination layer, every action traces to a defined business process rather than a single prompt. People stay in the loop where they add value, in judgement, empathy and accountability, and step out where they do not. In practice, a sales agent, a pricing model, a compliance officer and an ERP platform can all take part in the same workflow, with clear handoffs, approval gates and a complete audit trail. Because agents work across vendors and tools through open protocols such as MCP and A2A, workflows cross system boundaries without brittle point-to-point integrations. Agents are created, tested, versioned and deployed to a common standard, which is what makes them reusable, governed assets rather than one-off scripts that break when requirements change.
The enterprises getting the most return from AI are not adding it to their operations. They are rebuilding their operations around it.
From Capability to Operating Model
THE SHIFT
The AI coding tools that have shaped the engineering conversation over the past two years were built on a simple premise: make each developer faster and the enterprise will deliver faster. That premise is useful but incomplete.
Enterprise software delivery does not slow down only because individual developers need help writing code. It slows down because of what happens across teams, systems, decisions, dependencies and handoffs. Context has to be rebuilt every time work moves from one team to another. Architectural decisions are often disconnected from downstream execution. Institutional knowledge stays trapped in the heads of a few people. Programmes discover conflicts too late, often at integration, when the cost of correction is highest.
These are not individual productivity problems. They are coordination problems.
Engineering Hyper-Productivity is Persistent’s answer to this shift. It moves the AI conversation beyond code assistance to enterprise-scale software delivery, where humans, AI agents, architecture, governance and institutional knowledge work together as one system.
Powered by SASVA™ 4.0, Persistent enables enterprises to move from fragmented developer acceleration to coordinated engineering transformation. SASVA’s Brain creates a living, shared model of the programme, connecting codebase context, architecture history, dependencies, delivery patterns and team knowledge. This lets teams simulate, validate and orchestrate work before execution begins, reducing late-stage rework and improving delivery predictability across the most complex programmes.
The result is a new model for engineering: humans and AI working together across the full software lifecycle, from planning and architecture to development, testing, modernisation, deployment and support. Coordination carries this through from ticket to production, with Context supplying the architectural memory and code graphs that keep delivery coherent at scale.
SASVA 4.0 is model-agnostic, works across enterprise environments and supports outcome-led delivery models designed for complex, regulated and large-scale engineering programmes.
Individual productivity tools make developers faster. Only team intelligence makes programmes succeed, delivering the entire release to market faster, at acceptable quality.
From Individual Productivity to Team Intelligence
ENGINEERING HYPER-PRODUCTIVITY
A familiar pattern plays out across industries. A use case proves itself in a controlled environment, the model performs and the business case holds. Then the work meets the real enterprise, with its workflows, systems, compliance requirements, data gaps, approvals and operating constraints. Then momentum slows.
This is the pilot-to-production gap, and it is one of the defining challenges of enterprise AI today.
The gap is not about the technology being insufficient. Production AI simply needs more than a capable model. It needs a workflow architecture that connects AI to real business processes and a data layer that gives the model verified enterprise knowledge instead of generic inference. It also needs governance, auditability and human oversight wherever accountability demands it.
This is where Business Hyper-Productivity begins: governed agentic workflows connected to real enterprise processes, rather than isolated automation.
GenAI Hub provides the secure enterprise platform to design, deploy, govern, observe and optimise GenAI and agentic AI applications. Agent Studio enables business users and domain experts to design and orchestrate agents without deep technical expertise. ProcessIntel converts process documentation, workflow knowledge and existing automation assets into execution-ready agentic workflows, so enterprises can move existing processes into the agentic layer without starting from scratch. Together, they help enterprises move from fragmented experimentation to governed, production-ready agentic workflows, spanning 500+ enterprise agents across financial services, healthcare and life sciences, technology, cybersecurity, document intelligence and business process transformation.
The outcomes are measurable. Enterprises running governed agentic workflows with Persistent have accelerated process transformation by up to 60%, processed work up to 80% faster, auto-resolved up to 70% of issues through Agentic Managed Services and cut operating costs by up to 50%.
The context around the model is what carries AI into production. Persistent helps enterprises move beyond generic AI by combining context engineering with model tuning, pairing a structured enterprise context layer, built from business data, rules and workflows, with domain-specific SLMs and ELMs tuned to each environment, so AI decisions are grounded in the organisation's own reality rather than generic knowledge. The result is Business Hyper-Productivity: people, processes and AI working as one coordinated system.
Business Hyper-Productivity is about removing friction from the enterprise so people can focus on judgement, creativity, exception handling and outcomes, rather than replacing human decision-making.
AI proposes. Rules verify. Humans decide where accountability matters. The enterprise moves faster.
AI does not transform a business by speeding up its processes. It transforms it by reimagining them.
Closing the Gap Between Pilot and Production
BUSINESS HYPER-PRODUCTIVITY
Scaling AI well almost always comes down to the state of the underlying data, and that is the part most organisations underestimate.
The issue is rarely a shortage of data. Most large enterprises hold more data than they can productively use. The challenge is the state it is in: fragmented across legacy systems, defined inconsistently from team to team, built for reporting rather than real-time inference and short on lineage, governance and business context. In that state, data is not ready for AI to use safely or at scale.
This is why enterprise data readiness is not a prerequisite to AI. It is the AI investment.
Organisations that build this foundation before scaling AI compound their returns over time, while those that skip it keep running into the same limits in production: uneven data quality, inconsistent outputs and slower adoption.
iAURA is Persistent’s platform for solving this at scale. It brings AI into the data lifecycle itself, using agentic capabilities to assess, modernise, govern and operationalise enterprise data with greater speed, consistency and control. It turns fragmented data estates into trusted, contextual and governed foundations for enterprise intelligence, with a shared semantic layer that opens up data access so humans and agents across the organisation work from the same business vocabulary.
Across insights, observability, migration, data operations and platform buildout, agentic AI is now embedded into every stage of the data lifecycle. AI agents assess legacy estates, modernise pipelines, profile data quality, monitor freshness and automate the operational tasks that once consumed significant engineering capacity. The result is data that stays continuously AI-ready, rather than cleaned once and left to drift.
The outcomes are measurable. Enterprises working with Persistent’s data readiness practice have reached decisions up to 70% faster, reduced audit exposure by up to 60%, automated up to 60% of repetitive data operations tasks and achieved 15–25% infrastructure cost reductions through AI-driven managed services. These are the returns on treating data readiness as the AI investment.
AI cannot scale on disconnected data, act on unclear meaning or be governed without lineage. Data readiness is where AI ambition becomes enterprise capability.
When data is ready, AI scales. That readiness is the foundation.
The Foundation that Let's AI Scale
ENTERPRISE DATA READINESS
At Persistent, we believe that every AI system is a Responsible AI system.
Twelve months ago, the common question was: can AI do this? Today, the question is sharper: can we trust how this AI works, how it decides, how it is governed and how it will stand up to scrutiny?
Enterprises cannot scale AI into high-value workflows unless trust is built into the architecture of AI itself, across security, privacy, reliability, transparency, accountability, ethics and compliance on one hand and business outcomes on the other. This matters most in areas such as credit decisions, clinical recommendations, regulatory reporting, customer communications, engineering systems and cybersecurity.
Persistent’s Responsible AI story is part of a broader Digital Trust approach. Enterprisegrade trust takes more than policies. It calls for formal organisational programmes across Responsible AI, cybersecurity, data privacy, ethics and compliance, with structured frameworks, operating models, controls, audit readiness and continuous improvement, and it depends on building those principles into AI systems and the wider digital ecosystem from the design stage.
This is why Persistent helps enterprises operationalise trust through three connected motions.
Build programme trust by designing and operationalising enterprise digital trust programmes for compliance, accountability, governance, policies, controls and audit readiness.
Automate trust through specialised platforms, tooling and accelerators, because trust is no longer viable across the enterprise via documents and spreadsheets alone.
Embed trust controls into individual AI and digital systems, so trust is engineered into how systems operate from the start rather than applied after deployment. Across all three layers, a deterministic layer enforces business rules, validation gates and policy checks, so AI proposes and rules verify before any action completes. This approach helps enterprises reduce compliance risk, improve audit readiness and strengthen their overall trust posture as AI adoption scales.
In FY26, this commitment was independently reinforced through Persistent's ISO 42001 certification, the global standard for AI Management Systems. We also advanced structured Responsible AI and Privacy implementation frameworks, giving enterprises a scalable mechanism to govern their own AI programmes with greater rigour.
Because trust depends on people as much as systems, we keep investing in workforce enablement, AI literacy and responsible adoption practices across the organisation. The human capability behind Responsible AI keeps evolving, so that trust continues to compound.
Trust cannot be optional. It must be engineered from the start.
Trust is Not a Feature. It is the Foundation.
RESPONSIBLE AI
Every deployment should make enterprise data richer. Every workflow should make context deeper. Every agent should make execution faster. Every governance layer should make trust stronger.
That compounding is not automatic. It takes deliberate infrastructure, deliberate data strategy, deliberate workflow design and deliberate governance. The 3C Framework is the architecture that makes it possible.
Persistent enters FY27 with this architecture in place across SASVA™, iAURA, GenAI Hub, Agent Studio, ProcessIntel, Responsible AI and our broader AI-led engineering ecosystem. Our partner ecosystem extends this architecture into the infrastructure, data and application environments where enterprises already operate. Our Customer Zero approach tests and refines our platforms inside Persistent before they reach clients.
Behind all of it is a workforce built for this moment. In FY26 alone, Persistent professionals earned more than 30,000 internal AI certifications, covering the tools and the discipline of applying AI in complex, regulated, real-world environments. They bring the engineering depth, domain knowledge and execution rigour that no platform delivers on its own.
The compounding advantage is not just technical. It is human.
We are helping enterprises move from pilots to production. From experimentation to execution. From fragmented AI activity to coordinated enterprise transformation.
The next phase of enterprise AI is not AI as a tool. It is AI as an operating model.
That is what it means to Re(AI)magine™ the Enterprise: moving from AI ambition to engineered execution.
The next decade of enterprise AI will not be defined by who experiments fastest. It will be defined by who builds the architecture that lets AI compound.
The Compounding Advantage
RESPONSIBLE AI
Execution Prioritisation Matrix
In practice, a single request moves through all three layers. Core governs access, policy, model routing and cost visibility. Context resolves the entities involved, aligns definitions through the semantic layer and surfaces relevant history. Coordination then assigns the work across agents and people, enforces approval gates, updates the systems of record and captures a complete audit trail.
Together, these vectors help leaders see beyond technical feasibility and assess the full set of conditions required for AI to deliver sustained value.
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2026 Annual Report
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Re(AI)magining
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Contents
Home
FY26 Performance Highlights
Building the Workforce for the Next Era of Enterprise Execution
Persistent University
Environmental, Social and Governance
Persistent Foundation
FY26 Awards and Analyst Recognitions
Re(AI)magining™ AI as a System to Deliver Enterprise Outcomes
Re(AI)magining™ Client Success
Partnering to Transform, Innovate and Re(AI)magine
Re(AI)magining™ the Enterprise From Ambition to Engineered Advantage
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