Products and Platforms
TCS HOBS™
TCS HOBS™: Engineering experiences with personalization and innovation
Highlights
- Enterprise AI value is constrained by pilot-led adoption; a platform-first, modular, and agentic AI architecture is essential to scale AI with trust, reuse, and measurable outcomes.
- AI initiatives must be driven by a structured, KPI anchored lifecycle and sequenced through a three stream value model.
- Governance, data quality, human-in-the-loop controls, and continuous ROI tracking are foundational, not optional, for safely operationalising AI.
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From pilots to scale
Artificial intelligence has progressed from experimental automation to a strategic capability shaping enterprise competitiveness. Many initiatives remain confined to isolated pilots, fragmented data environments, and governance models that are not designed for scale.
Enterprises can bridge this divide through a validated, modular AI architecture engineered to accelerate value realisation. This blueprint should span data foundations, agentic patterns, retrieval-augmented generation (RAG) mechanisms, governance controls, and scalable deployment models. This whitepaper outlines how enterprises can transition from experimentation to operational transformation, unlocking improvements in efficiency, customer experience, and innovation velocity.
Key Takeaways
- Scale AI with a platform-first, modular architecture (not disconnected pilots)
- Ground enterprise use cases with strong data foundations and RAG to improve accuracy and trust
- Prioritise initiatives using a three-stream value model to balance quick wins and long-term differentiation
- Embed governance, security, and human-in-the-loop controls as continuous operational mechanisms
- Measure ROI with KPI baselines and ongoing monitoring to sustain adoption and benefits realisation
OSS/BSS architecture: The foundation for viable transformation
For Tier 2 and Tier 3 CSPs, the transformation journey begins with a modular OSS/BSS architecture built on three pillars: Simple design, contextual intelligence, and scalable foundations.
Simple: Cloud-native and modular for agility
The shift from monolithic systems to a cloud-native, microservices-based architecture is the cornerstone of simplicity. By breaking down core functions into modular components, CSPs can eliminate the challenges of a legacy environment, accelerate updates, scale on demand, and reduce complexity.
- Reduced complexity: A subscription-based approach (Telecom-as-a-Service) democratises access to advanced capabilities without the burden of heavy CapEX.
- Frictionless updates: Decoupling the data layer allows for independent evolution of data storage and business logic, ensuring that maintenance and upgrades no longer require system-wide downtime.
Contextual: Real-time unified data for intelligent operations
Networks and customer experiences are real time. Data is only valuable when it is contextual. A unified data layer provides real-time visibility across network and customer domains, enabling predictive analytics that understand the “why” behind every event.
- Actionable insights: Instead of reactive troubleshooting, agentic AI uses real-time context to detect, predict, and resolve issues before they impact the user.
- Enhanced experiences: By closing the loop between network performance and business outcomes, CSPs can deliver hyper-personalized offers based on the customer’s current usage context and service quality.
Scalable: Open standards for interoperability
Vendor lock-in is a significant barrier to scalability. Adopting TM Forum’s Open Digital Architecture (ODA) and Open APIs ensures that the architecture is built for future expansion.
- Elastic growth: Cloud-native containers enable the system to scale elastically with demand, ensuring performance during peak loads without overprovisioning.
- Ecosystem ready: This flexibility empowers CSPs to mix-and-match best-of-breed solutions and launch new services in weeks, not years—transforming the architecture into a non-linear engine for sustained growth.
| Focus areas | What studies indicate |
| Organisations using AI in at least one business function | Adoption is reported as widespread across enterprises, but the depth of adoption varies by function and maturity. |
| Economic value potential of Generative AI (annual) | Generative AI is expected to create a substantial economic impact, contingent on workflow redesign and adoption at scale. |
| Productivity uplift reported by organisations adopting AI agents | Early adopters report meaningful productivity improvements, especially for repeatable knowledge-work tasks and customer operations. |
| Cost savings reported by organisations adopting AI agents | Reported savings are strongest when agents are embedded into end-to-end workflows with clear governance and change management. |
| GenAI return on investment (value per $1 invested) | Many organisations report positive ROI signals, but results vary widely depending on data readiness, reuse, and operating model maturity. |
| Telecom benchmark: AI-driven operational use cases can reduce total network opex | Studies indicate that targeted AI use cases can reduce operational costs when paired with automation, closed-loop controls, and disciplined rollout. |
Table 1. AI adoption, impact, and value: Key industry indicators
AI adoption strategy
Successful AI adoption demands a multi-layered strategy. Enterprises must set clear objectives, foster cross-functional collaboration, and engage stakeholders for unified vision. The approach should blend technical excellence with agile implementation, allowing quick wins through pilot projects before broader rollout. Regular monitoring and feedback loops drive adaptability and effectiveness.
A robust AI enterprise is underpinned by layered foundations that promote clarity, resilience, and alignment with organisational objectives:
AI adoption framework
Figure 1: AI adoption framework
Enterprise AI adoption requires a structured, outcome‑driven lifecycle that moves from process assessment to value realisation. They should operationalise this through a repeatable flow that aligns business readiness, measurable KPIs, use‑case ideation, and a value‑stream‑based roadmap, ensuring AI initiatives scale beyond pilots into sustained enterprise impact.
AI adoption lifecycle
Figure 2: AI adoption lifecycle
To balance rapid ROI with long-term differentiation, classify the AI initiatives into three value streams based on time horizon, complexity, and strategic impact. This stream-based model helps enterprises sequence investments, demonstrate early wins, and progressively unlock higher-order transformation while maintaining execution discipline.
| Value stream | Time horizon | Typical use cases | Example KPIs | Indicative timeline |
| Efficiency gains | Short-term | Automation, triage, document processing, response generation, and engineering productivity | AHT, OPEX reduction | 0–3 months (pilot) / 3–6 months (rollout) |
| Enhanced experiences | Mid-term | Conversational AI, personalisation, guided workflows, real-time insights, decision support | NPS, FCR, TAT, self-service | 6–12 months |
| Transformative | Long-term | Autonomous operations, self-healing networks, intelligent orchestration, AI-driven product innovation | Revenue uplift, MTTR, churn reduction | 12–24+ months |
Table 2: Three-stream AI value mode
AI enterprise roadmap
This roadmap operationalises the three-stream AI value model (Table 2) into a pragmatic delivery plan for a telco as an example. Timelines are indicative and should be adjusted based on data readiness, platform maturity, and regulatory constraints. The intent is to sequence delivery so that early efficiency gains fund and de-risk mid-term experience improvements, which in turn enable longer-term transformation.
Phase 1 (0–6 months): Establish foundations and deliver efficiency gains
- Stand up the AI platform baseline: secure data access patterns, vector store, prompt library, model gateway, and observability.
- Create KPI baselines for priority operations (e.g., AHT, MTTR, order fallout rate, billing dispute rate) and define target improvements.
- Deliver 2–4 high-confidence pilots (agent assist, summarisation, ticket triage, document intelligence) and progress the best into rollout.
- Implement initial guardrails: role-based access, PII redaction, prompt injection defences, human approval for high-impact actions, and audit logging.
- Monitoring: weekly model evaluation, monthly benefits reviews, and a backlog that prioritises reusable components over one-off builds.
Phase 2 (6–12 months): Scale adoption and enhance experiences
- Scale successful pilots across functions and channels (self-care, agent desktop, field ops, NOC) with change management and role-based training.
- Integrate AI into core workflows (CRM, Order management, Billing) through APIs and event-driven triggers, moving from “assist” to “guided execution”.
- Expand RAG and knowledge management: curated domain data, source attribution, and feedback loops for content correction.
- Strengthen governance: model risk classification, evaluation harnesses, drift monitoring, and exception management processes.
- Institutionalise value tracking: benefits realisation per use case, run-cost transparency (cost per conversation/ticket), and adoption dashboards.
Phase 3 (12–24+ months): Enable transformation through agentic automation
- Introduce multi-agent orchestration for end-to-end workflows (plan–execute–validate) with clear human override and escalation paths.
- Move toward closed-loop operations where appropriate (e.g., detect–diagnose–recommend–execute for low-risk actions; recommend–approve–execute for higher risk).
- Expand to predictive and proactive experiences: churn propensity interventions, proactive outage communications, and personalised offers with policy constraints.
- Evolve the platform: multi-model routing, standardised agent skills, and continuous evaluation at scale.
- Audit and compliance maturity: evidence-based controls, traceability from output to sources/actions, and continuous risk reviews for regulated data.
Governance and operating model (applies to all phases): establish an AI CoE (standards, evaluation, guardrails), domain product owners (use-case outcomes), and platform engineering (reusable services). Use a tiered approval model based on risk (inform, assist, recommend, execute) and enforce controls through policy-as-code, audit logging, and continuous monitoring.
Beyond the balance sheet: Systematic value creation as the new engine of monetization
The ultimate financial question is not simply how to incrementally improve quarterly revenue, but what constitutes genuine value creation in this new cycle of disruption. This pivot demands that the finance function evolve far beyond reporting and control to become the strategic enabler, actively directing capital toward organisational agility.
This involves having the courage to decommission legacy systems and strategic projects that, while successful in the past, now actively inhibit future speed and responsiveness. When capital is allocated through this lens, prioritising investments that build systemic value for both customers and the environment, the enterprise is positioned to generate compounding value. Monetisation then becomes less of a discrete transaction and more of a systemic inevitability, building a non-linear engine for sustained, future-proof growth.
| Domain | Efficiency gains (0–6 months) | Enhanced experiences (6–12 months) | Transformative (12–24+ months) | Example KPIs |
| BSS (order, billing, revenue assurance) | Ticket/order triage, document understanding, billing dispute summarisation, test case generation | Guided order resolution, proactive bill explanation, assisted collections scripts, personalised plan recommendations | Autonomous fallout remediation (low-risk), closed-loop | Order cycle time, fallout rate, billing disputes, revenue leakage, cost per order |
| BSS (assisted + digital) | Agent assist, call/chat summarisation, knowledge retrieval, intent classification, and routing | Personalised self-service journeys, proactive support, omni-channel context continuity, dynamic scripts | Autonomous case resolution for defined intents, end-to-end service recovery journeys, and next-best-action orchestration | AHT, FCR, containment/deflection, CSAT/NPS, cost-to-serve |
| OSS (service assurance, inventory, provisioning) | Alarm correlation assist, incident summarisation, knowledge search for runbooks, automated post-incident reports | Guided troubleshooting, recommended configuration changes, field technician copilots, proactive capacity insights | Self-healing for defined scenarios, agentic change planning with approvals, and autonomous remediation playbooks | MTTR, repeat incidents, change success rate, truck rolls, capacity utilisation |
Table 3: Telco-specific use cases mapped to the three streams
Evaluate AI models for cost, performance, and security
One of the most crucial steps is to evaluate AI models not only for technical capability, but also for operating cost, latency, scalability, and security. Enterprises should benchmark candidate models against defined KPIs such as accuracy, throughput, cost per inference (or token), and data-residency and compliance requirements.
For example, customer chatbots should balance response quality with latency and unit economics, while meeting privacy and regulatory constraints. Additional evaluation criteria may include supported modalities (text/image/audio/video), prompt caching, licensing terms, model update cadence, and integration patterns with enterprise systems.
Security is paramount: models should be rigorously tested for vulnerabilities, including adversarial attacks and data leakage. Enterprises can employ penetration testing and ongoing audits. Performance metrics should be monitored in real time, enabling model tuning and switching as newer, more efficient algorithms become available.
Build robust frameworks
Scalable AI requires platform-level abstractions rather than point solutions. Build a modular, model-agnostic framework that embeds governance, reuse, and operational controls as foundational capabilities, enabling safe evolution of models and consistent consumption across enterprise systems.
Identify data sources: Accuracy and ownership
Data fuels AI. Enterprises must map all potential data sources, customer interactions, billing records, and network logs and assess their accuracy, completeness, and timeliness. Clearly assign data ownership to business units or dedicated data stewards, ensuring responsibility for quality, privacy, and compliance. For example, improving NPS requires integrating real-time customer interactions and high-accuracy sentiment scoring data from multiple channels, with clear governance.
Governance and human-in-the-loop control
Enterprises should embed governance and human oversight as continuous control mechanisms rather than post-deployment checks. Human review, policy enforcement, feedback capture, and lifecycle governance operate as an integrated loop, ensuring ethical use, regulatory compliance, and sustained alignment with business objectives.
AI governance lifecycle
Figure 3: AI governance and human-in-the-loop control cycle
Deploy changes incrementally
Incremental deployment is essential for minimising risk. Start with pilot projects targeting well-defined user groups or business functions. For example, launch an AI-powered self-care app for a subset of customers before scaling to the wider user base. Incremental rollouts allow enterprises to refine solutions, address unforeseen challenges, and build stakeholder confidence.
Measure and track ROI
Develop robust methods to measure and track the return on investment (ROI) for AI initiatives. Link KPIs to tangible business outcomes, such as reduced operational costs, improved customer retention, increased upselling rates, and faster time-to-resolution. Leverage analytics dashboards and periodic ROI reviews to inform strategic adjustments and ensure continued value creation. For example, track how automating network issue detection impacts incident response times and overall customer satisfaction.
A reference architecture for enterprise AI
An enterprise AI architecture optimised for scale, modularity, and domain specificity. Its agentic AI design enables intelligent orchestration across case management, product configuration, operational workflows, and testing, featuring:
- RAG pipelines grounding AI responses in enterprise-validated knowledge.
- Multi-model inference capabilities supporting flexibility across diverse models and tasks.
- Vector-driven retrieval systems improving precision and contextual relevance.
- Reusable prompt engineering frameworks streamlining development processes
Governance and security are intrinsic to every architectural layer, including audit trails, granular access control, and hallucination mitigation to ensure AI deployments adhere to rigorous enterprise and regulatory standards.
A comprehensive agentic, modular architecture comprising planner, worker, and critic agents collaborating on complex workflows. This autonomous orchestration is characterised by:
- Planner agents interpreting intent, decomposing challenges, and designing execution flows.
- Worker agents executing API calls, performing grounding and reasoning, extracting documents, and handling domain logic.
- Critic agents validating output quality, enforcing guardrails, and ensuring compliance with enterprise constraints.
The architecture further incorporates an extensive prompt library, database-grounded retrieval, and model‑agnostic inference, delivering adaptability across evolving enterprise landscapes. Rigorous guardrails, including role-based access, consistency checks, and safety filters, ensure operational integrity and trustworthiness.
About the authors
Niren Moharir, Global Head, TCS HOBS™
Niren Moharir has three decades of Industry experience providing IT and consulting services to Telecom and affiliated industries. He currently heads TCS HOBS™ with focus on delivering current and competent platform-based solutions across Subscription, Device and Data domains.
Shiva Voleti, Telecom BSS consultant and architect, TCS HOBS
Shiva Voleti is an experienced BSS consultant and architect who heads new initiatives for TCS HOBS™. His expertise spans telecom, GenAI, BSS, Java, Cloud, and Microservices.
Mahesh P, Platform Architect TCS HOBS™
Mahesh P is a Platform Architect with TCS HOBS. He has worked extensively with clients to enable and implement various AI use cases leveraging the platform. His experience includes platform architecture, solution design, and supporting enterprise-scale adoption initiatives.
Tarun Goswami, Head, Product Engineering of Network Operations, TCS HOBS™
Tarun heads the network operations platform within TCS HOBS. With around two decades of experience, Goswami is responsible for drawing solution roadmaps and the architecture and design of products and solutions for telco operations. His areas of expertise include network assurance, service and network orchestration, and IoT device management.