Enterprise AI architecture
Agentic platforms, RAG pipelines and LLM systems designed to hold up in production — grounded, source-cited, regression-tested after every prompt change, and audit-ready rather than demo-ready.
Enterprise AI Transformation Leader · Gurugram, India
Abhishek Prasar
AI doesn’t scale on models. It scales on governance, data, and adoption.
I help enterprises design and scale AI capability — modernizing systems, building governance into the foundation, and embedding AI into how decisions actually get made. Over fifteen years, I’ve seen the journey from classical machine learning to where we are today with agentic systems, building two AI functions from zero and delivering platforms used by well over a hundred thousand people across global organizations.
- Now
- Enterprise AI & Sales Transformation, WNS (Capgemini)
- Based in
- Gurugram, India
- In AI since
- 2011
By the numbers
Built AI at
- Ericsson
- TCS
- Dell
- LTIM
- Capgemini
Foundation
- 50+
- AI/ML solutions across classical ML, deep learning and NLP
- 20+
- Enterprise-grade GenAI and agentic systems in production
- 0 → 20
- AI practice built from nothing
HR and talent
- 100K
- Users on an agentic talent marketplace
- 90%
- Match accuracy, explainable and auditable
- 35%
- HR helpdesk load reduced through self-service AI
Sales and GTM
- GTM Intelligence
- End-to-end AI pipeline from market signals to pursuit-ready account plans — account intelligence, pursuit strategy, proposal and travel agents.
- 94%
- Cut in account planning time
- 400+
- AI-generated account plans delivered to senior sales leaders
Governance and adoption
- Governance
- DLP, PII protection and EU AI Act alignment — built in from day one, now the enterprise standard
- 4×
- Active adoption growth within six months
- Audit-ready
- Source-cited outputs, human-review gates and regression discipline on every release
Fifteen years turning AI from capability into outcome — across HR, sales and the commercial core, with governance built in from the first line of code.
What I work on
Three things, in roughly this order
Architecture, commercial outcome, and adoption. The first is the part people plan for; the last is the part that decides whether the first ever mattered.
Commercial AI & GTM intelligence
AI pointed at revenue rather than at cost lines: buyer-intent signals, account prioritisation, pursuit strategy. Signal to priority to plan to pursuit, as one loop rather than four tools.
Governance & adoption
Responsible AI, DLP and data-boundary design built in from day one, not retrofitted — and the behavioural work that moves an organisation from a launch announcement to a default operating model.
Selected writing
Recent pieces
Longer than a post, shorter than a book, and aimed at people shipping AI inside real organisations rather than people describing it from outside.
Your Team Wants n8n. Your Architect Wants LangGraph. Both Are Right.
This looks like a tool choice. It is a decision about who carries the system once it works.
3 min readExecutives Don't Fund Technology. They Fund What Changes Because of It.
Technical teams lose funding by describing what they built instead of what changed. Four rewrites that shift the conversation.
3 min readAI should live in your workflow — not beside it
AI adoption is not a technology problem. It is a workflow design problem.
1 min readIt escaped its cage in testing, emailed the researcher, and found thousands of zero-days. Meet Mythos — the model Anthropic won't release.
Anthropic built a model so capable at hacking it refused to ship it — the first such withholding since GPT-2. What Mythos means for everyone who buys AI.
2 min readWorking on the same problems?
I am glad to hear from AI and engineering leaders, founders, and anyone who disagrees with something I have written here.