Njenga Kariuki

Leading AI transformation at M-PESA, after twelve years building AI products at Amazon and AWS.

Eight markets, 100M+ customers and $2B+ revenue across Vodacom FinTech Group, reporting to the CEO. From Alexa to Amazon Bio Discovery, an agentic AI application for drug discovery, with organizations of 100+ engineers, scientists and product managers. A master’s degree from Stanford Graduate School of Business, and published research from the Stanford Institute for Human-Centered AI on AI’s global economic impact.

Work

2017–2026

Career

2012–2026
2026–todayM-PESA AfricaManaging Executive: Artificial Intelligence · Nairobi
Runs

AI strategy and the AI Center of Excellence for eight markets, 100M+ customers and $2B+ revenue.

Now

Under way across the group’s markets: AI moving from single tasks to whole chains of work, redesigned end to end.

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  1. M-PESA AfricaManaging Executive: Artificial Intelligence · 2026–today · Nairobi

    RunsAI strategy and the AI Center of Excellence for eight markets, 100M+ customers and $2B+ revenue.

    NowUnder way across the group’s markets: AI moving from single tasks to whole chains of work, redesigned end to end.

  2. Amazon Bio Discovery, AWSHead of Product, AWS Applied AI, Life Sciences · 2025–2026 · Seattle

    RanDirected a cross-functional organization of 100+ engineers, scientists and product managers, from start to launch.

    ResultLaunched 14 April 2026. In its launch campaign, 46 of 116 designs tested were confirmed binders.

  3. StanfordMaster’s degree, Graduate School of Business · 2024–2025

    RanWrote the text and analysis of published research from the Stanford Institute for Human-Centered AI on AI’s global economic impact.

    ResultPublished 7 April 2025. Cited by the Federal Reserve Board and the White House.

  4. Alexa Smart Home and Map ViewSenior Manager, Technical Product Management · 2023–2024 · Seattle

    RanLed the smart home features of the Alexa app, used by 50M+ people a month. Technical advisor and chief of staff to the Vice President of Amazon Smart Home and Health.

    ResultMap View launched in the United States, November 2023.

  5. Amazon Health AIPrincipal Technical Product Manager · 2021–2023 · Seattle

    RanFirst product lead of a computer vision and AI organization of 100+ scientists and engineers. Directed its $100M+ a year investment portfolio; managed 10 product managers.

    ResultThree validation studies in npj Digital Medicine: Body and Movement in 2022, waist-to-hip in 2023.

  6. Amazon HaloSenior Technical Product Manager, founding team · 2018–2021 · Seattle

    RanProduct lead for Body, Tone and Movement, from the founding team to launch.

    ResultBody and Tone launched in August 2020, Movement in June 2021. Six US patents from this work.

  7. Alexa ShoppingTechnical Product Manager, first product lead · 2017–2018 · Seattle

    RanFirst product lead for how Alexa understood shopping requests; built and led a 10-person evaluation team.

    ResultShopping success rose from about 50% to 85%.

  8. Prime NowProduct Manager, founding · 2015–2017 · Seattle

    RanFounding product manager of the local-store and seller platform.

    ResultPartner stores in 7 countries by 2017. Out of stock at partner stores fell from 25% to 5%; 40 hours a week of manual work automated.

  9. Amazon Bedding & BathVendor Manager · 2014–2015 · Seattle

    RanOwned a $100M+ a year profit and loss, from brand negotiations to supply-chain terms.

  10. Target, Apple categorySenior Supply Chain Analyst · 2012–2014 · Minneapolis

    RanOwned supply-chain strategy for a $700M+ a year Apple category across about 1,800 stores.

    ResultIn-stock rate up 24% and market share up 3%. Allocated the iPad mini launch to each store, November 2012.

Patents and papers: six US patents, 2022 to 2026; three validation studies in npj Digital Medicine, 2022 and 2023; published research from the Stanford Institute for Human-Centered AI, 2025.

My approach

I lead AI transformation at two levels: setting the strategy with the CEO and executive team, and working hands-on in the forward-deployed engineer’s role, building the first systems with the teams who will run them.

Move AI from units of work to systems of work

AI on a unit of work, one step in one function, changes little while the handoffs around it stay the same. I redesign the system of work, the whole chain across functions, and measure it from the first request to the result.

Out of stock at partner stores0%10203025%5%BeforeWith the daily upload

Prime NowI took the stores’ spreadsheets, then automated a matched catalog, forecasts and a daily stock upload. Out of stock at partner stores fell from 25% to 5%.

Decide what “accurate” means before building

I start from the claim a customer will read, and choose the reference the model learns from and is judged against. When the labels are noisier than the claim allows, I change where the labels come from.

Average error against a clinical DXA scan, in points0123456Acceptable4.5 to 5.92.2Halo BodyThree smart scales

Halo BodyTraining labels moved from experts’ eyes to clinical X-ray scans. Published error: 2.2 points, half that of the best smart scale tested.

Put one customer measure above the model metrics

A model can beat its benchmarks while customers think the product does not work. I run one measure of the customer’s outcome for the whole system, and hold every model metric to it.

Shopping requests judged a success0%25507510050%85%First measureAfter the loop

Alexa ShoppingI listened to 1,000 requests a week and wrote how each was judged. Success rose from about 50% to 85%.