The first mile
Picking the right work, mobilizing the organization, and redesigning the workflow before automating it. Most teams freeze, or automate the wrong thing.
Enterprise AI-Native Transformation
The hard part of AI-native isn't the model.
An embedded engagement that changes how the work gets done, not just which technology you adopt.
A working session with you and a couple of your leads, on a workflow you already care about.
25+ years across Microsoft, Google, and Meta — leading GenAI agents and large-scale data-platform work in production.
Not a strategy deck, and not a vendor. I sit with the frontline, decompose the actual work, redesign it, and upskill the people who will own it after I leave.
Abhishek Mishra
LinkedIn
- 70-20-10 the split every engagement is designed around
- Full stack hardware and infra through to the application
- 30K support professionals, one org rebuilt
AI-native is won on people and process
The model is the smallest part of the work. Transformations live or die in how people operate and how the work is redesigned around them.
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70%
People and process
Change management, workflow redesign, training, and getting the data ready to be used by AI.
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20%
Data and evaluation
Pipelines, ground truth, evaluation harnesses.
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10%
The model
Increasingly rented, not built.
BCG's research across thousands of engagements lands on the same split, 10-20-70. More than two-thirds of AI transformations fail on the people side, not the technology. Source: Boston Consulting Group
Data shows up in two places on purpose. Pipelines and evaluation harnesses are engineering, and they are the 20%. Deciding who owns a dataset, agreeing what a field actually means, and keeping ground truth current after launch are organizational, and they are the 70%. That is also the half that quietly decays once the program is no longer new.
This is not a framework I read somewhere. It is what I watched happen from the inside, on programs where the model was never the thing that was broken.
It is also how I score a company in diligence. Most technical reviews grade the 10%. The reason a deal disappoints is usually sitting in the 70%.
Two places AI-native stalls
Most enterprise efforts never clear one of two gaps. Neither is a modeling problem.
The last mile
A proof of concept that wins the room but never survives production, trust, and governance. This is where most pilots quietly get shelved.
Tools and strategy decks cross neither gap. People and process do.
Every layer, not just the top one
Enterprise infrastructure and applied AI across the whole stack. The decisions that decide whether an AI program works are rarely all on one layer.
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Application
Products, agents, and redesigned workflows
Agentic systems with humans in the loop, approval gates, and audit trails — built around the work, not bolted onto it.
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Retrieval
RAG and context engineering
Grounding on your own corpus: chunking and indexing strategy, retrieval quality, freshness, permissions, and citation.
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Models
Model selection, adaptation, and serving
Build-versus-rent calls, fine-tuning and distillation where they pay for themselves, inference cost and latency budgets.
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Data & eval
Pipelines, ground truth, and evaluation harnesses
The 20% almost everyone underfunds: ground-truth sets, offline and online evals, and CI gates that stop a confident-but-wrong change.
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Big data
Large-scale data platforms
Lakehouse and streaming architecture, lineage and governance, and the platform economics behind petabyte-scale workloads.
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Infra
Enterprise infrastructure and hardware
Compute and accelerator strategy, capacity and cost, cloud and on-prem trade-offs, security and isolation for regulated environments.
Depth at each layer is what makes the redesign credible to the engineers who have to build it, and to the risk teams who have to approve it.
Three ways we work
Three depths of the same work, from a two-week read to a multi-year build. Same underlying question: does the technology actually change how the work gets done?
Due diligence: technology and team
An operator's read on whether the technology, the team, and the claims hold up. Most useful for investors making a decision on a clock.
- Technical due diligence on architecture, data, evals, and defensibility
- Separating a real AI moat from a wrapper with good demos
- Talent mapping — who to hire, who to back, what the team is missing
- Portfolio support: architecture reviews and AI roadmaps post-investment
How it runs: I run the founder technical session myself. A few hours of your team's time, the rest is on me. Written up the way your IC actually decides.
AI-native transformation, run 70-20-10
The embedded engagement: get inside the workflows, redesign the work, then automate it — with your people owning the result.
- Embed with the frontline and decompose the work function by function
- Redesign the highest-leverage workflows before automating them
- Stand up the data, evaluation, and governance foundation
- Upskill internal AI champions so the capability outlasts the engagement
How it runs: a named business owner and a named technical owner on your side, alongside me. Weekly working sessions with the people doing the work, not a monthly steering review. First arc is 90 days with one named outcome and an explicit re-scope at the end.
Run before, from the inside. A 30,000-person support organization at Google, and Meta's analytics function.
Product, technology, and technical scale
For teams whose AI product is working and now has to survive scale and enterprise buyers.
- Product and technical strategy — what to build, rent, or drop
- Architecture for scale: cost, latency, reliability, and data flywheel
- Evaluation discipline and release gates before customers find the bugs
- Engineering org design and the hiring bar for applied-AI roles
How it runs: in your design docs and your code reviews on a weekly cadence, three-month minimum with monthly renewal. The longest engagements are the ones where I am effectively part of the team.
Every engagement starts small enough that you see how the work goes before committing to the arc, and ends when your team can run it without me.
An operator who has done both miles
Not a strategy deck, and not a vendor. Someone who has shipped AI in production and changed the organizations around it.
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Changed how 30,000 people work
At Google I built and scaled the platform behind every Alphabet support and sales operation, moving 30,000 support professionals off third-party systems onto an internal one. 120M calls a year, over $100M in annual savings, and a data intelligence layer processing 500M+ customer conversations.
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Shipped agents people actually adopted
At Meta I led 20+ GenAI agents across support, global operations, and analytics, worth more than $100M annualized. The internal analytics agent is used weekly by three quarters of the analytics organization, and by more people outside analytics than inside it. Getting an agent used is harder than getting it built.
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Where AI has to be trusted
Working inside Vitea on healthcare LLM governance, and Zettabolt on AI infrastructure and evaluation.
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Transfer, not dependency
You are buying judgment and a team that can run without me, not a bench of contractors.
“My job is to translate in both directions: between the people doing the work and the leaders funding it.”
What partners say
Founders, operators, and investors I have worked with — in their own words.
The value keeps compounding
Abhishek Ranjan LinkedIn
CEO, Zettabolt
We have worked with Abhishek Mishra for several years and the value keeps compounding. When we needed real database depth, he built and ran a course for the entire engineering team. When we wanted to take on AI agent work, he helped with resources that helped the team’s rampup. And when a GenAI project of ours was stuck in pilot, his recommendations got it into production in a few iterations.
He has an unusual ability to take a lot of information and turn it into steps a team can act on. He is also the person I call before a big strategic decision or handling nuances in a new client contract.
He works like an operator, because he is one
Shantanu Nigam LinkedIn
CEO, Vitea · Managing Partner, SeedtoB
Working with Abhishek feels less like hiring an advisor and more like adding a seasoned operator to the team. He is in with our engineers every week, hands on with our design docs and strategy, and the person I turn to when a customer asks the question I don’t have a clean answer for. He goes deep and provides precise guidance.
He understood what actually makes healthcare AI hard faster than I expected, and time and again his sense of which problems matter has saved us months and kept us building the right thing. I trust his judgment on the hardest calls we face. He works like an operator, because he is one.
A read we could take to our IC
Suhani Doshi LinkedIn
VP Investments, Mela Ventures
We brought Abhishek in for technical diligence on an AI deal where we needed a read we could take to our IC. He gave us a clear view of the engineering risk, ran the founder session himself, and wrote it up the way we actually make decisions.
Rare to find someone who has built at that scale and can still get into the details on a one week clock.
Frequently asked questions
Who is this for?
- Venture funds needing technical due diligence and talent maps they can act on
- Enterprises past the pilot stage that need AI to change the operating model, not the tool list
- AI-native startups scaling product and technology past the first customers
- Leaders funding AI work who need a translation layer between the frontline and the board
Who is this not for?
- Anyone who wants a strategy deck and a vendor shortlist rather than change on the ground
- Teams looking to buy a bench of contractors — the goal is transfer, not dependency
- Programs where the frontline can't be reached, since that's where the 70% lives
Do you advise, or do you build?
Both, and in that order. The first mile is decomposing and redesigning the work; the last mile is a capability that survives production, trust, and governance. I work alongside your engineers on real workflows rather than handing over a recommendation.
How long does this take?
Diligence is two to three weeks. AI-native transformation starts with a 90-day arc built around one named outcome, and we re-scope at the end rather than rolling forward by default. Product and scale work runs on a three-month minimum with monthly renewal. Organizations move at different speeds, and a full operating-model change can take a year or more. That is why I do not sell it as one engagement. Each phase ends, you see the result, and you decide whether to continue.
Have you run this at an enterprise other than Google and Meta?
Not yet as an outside advisor, and I would rather say that than dress it up. The organizations I have changed at this scale I changed from inside. A 30,000-person support operation at Google, and Meta's analytics function, both of which had every reason to keep working the way they already did. What travels is the method and the failure modes, and those are consistent across companies. What I do not bring is a bench of consultants, which is the point.
What I have run as an outside advisor is narrower and real: healthcare LLM governance inside Vitea, AI infrastructure and evaluation at Zettabolt. Smaller stages, same method.
What about regulated environments?
Governance goes into the architecture, not the appendix: human approval on consequential actions, full audit trails, versioned instructions, and evaluation gates that run inside your secure environment.
How do we start?
Email akmishra@ainativex.ai and ask for a working session — ninety minutes with you and a couple of your leads, on a workflow you already care about. If there's a fit, we scope a small first phase so you see how the work goes before committing to more.
Get started
Start scoped, not sweeping
Begin with a working session, ninety minutes with you and a couple of your leads. If there is a fit, the first phase is two to three weeks. Low risk, concrete, and you see how the work goes before committing to the full engagement.
Email akmishra@ainativex.ai — tell me the workflow you'd start with and who would be in the room. Or reach me on LinkedIn.