# Alexander Hill — Industrial AI Founder-Builder

https://alexander-hill.me/

## Hero

Co-founded Senseye, an industrial AI company, before the category had a name — grew it to enterprise customers including Nissan, Mars, and Tata, and built the growth system that scaled it 100% YoY before Siemens acquired the company in 2022. The way I work hasn't changed much since: learn how a thing actually works, take it apart to find what matters, and rebuild it into something better — the product, the market, the pricing, the way a team moves. A decade of the work that decides whether deep-tech becomes a serious business.

### Key Stats
- 10+ years industrial AI founder-builder
- 2014–22: Co-founded Senseye to exit
- 100% YoY revenue growth at enterprise scale
- Millions of machine data points made useful

## Summary

Engineer by training, builder by choice. I like learning how a business works, taking it apart, and rebuilding the strategy, pricing logic and customer proof so a hard technical product becomes something people understand, fund, buy and renew.

Useful when something is technical enough to need real understanding, commercial enough to need revenue, and strategic enough that the story has to work from the codebase to the boardroom to the customer shop floor. I have worked end-to-end on an industrial B2B SaaS business — strategy and GTM, product and customer feedback, marketing and demand generation, sales and customer success, pricing and margins, forecasting and annual planning, board and investor reporting, senior hiring, and scaling through acquisition.

### Gaps I Work In
1. Technical possibility vs. what a customer can understand, trust, buy and renew.
2. Founder conviction vs. what the market, board and operating plan can support now.
3. Pilot excitement vs. what survives procurement, rollout, retention and enterprise scale.

## Biography

I have always liked taking complex systems apart to see how they really work — but the through-line is commercial: turning what is possible into something people understand, trust, buy and use. The technology matters most when it changes what a customer can do.

I earned a BEng in Digital Systems Engineering from the University of Southampton, graduating with honours. The course sat between computer science and electronic engineering, which is still roughly where I am most useful: close enough to the machine to understand what is possible, close enough to the market to know what matters.

I then spent five years engineering safety- and mission-critical aerospace and defence systems: embedded monitoring, wireless sensor networks, dependable systems, formal methods, European research projects and cleared work best kept general. It was an excellent education in the difference between impressive technology and technology that has to work in real life.

I deliberately transitioned into sales and marketing because the product was not going to explain itself. In 2014 I co-founded Senseye, helped win against larger and better-funded competitors, and helped build it to acquisition in 2022. The work widened quickly: strategy, pricing and margins, market education, product feedback, partner routes, forecasting, annual planning, board and investor reporting, hiring and shaping the senior team, enterprise expansion and operating cadence — and the proof to back it, from roughly two-week deployments and payback inside a quarter to millions of machine data points turned into decisions across global plants. Startups are hard because everything is missing; large companies are hard because everything already exists. The skill is knowing which problem you are dealing with.

> The useful work happens where capability meets belief: what the product can do, what customers think is possible, and what the business can repeatedly sell, renew and expand.

I can work at the technical level, then translate the same truth for a boardroom, a VC conversation, an operating plan, a sales team, an analyst briefing or a customer shop floor. The message changes shape; the substance should not.

## Work History

- **Nokia (intern)**: When Nokia meant phones and mobile felt like the future. Learned how scale behaves, how hardware meets people, and why timing matters.
- **2006–11: Aerospace & defence engineering**: Five years on safety- and mission-critical systems: embedded monitoring, wireless sensor networks, dependable systems, European research projects and cleared work best left general.
- **2011–14: Engineer → sales & marketing**: A deliberate transition. Watched good technical work lose because the market could not understand the value, then learned how customers, stakeholders, channels and buying committees actually move.
- **2014: Co-founded Senseye**: Saw a market gap in industrial maintenance and helped build a ground-up AI and cloud solution for manufacturers before the category was obvious. Aerospace discipline helped; the shop floor taught the rest.
- **2014–22: Built the commercial engine**: Led GTM strategy, sales, marketing, partnerships, analyst relations, pricing and market education across automotive, FMCG, heavy industry, utilities, aerospace and manufacturing. Carried growth and bookings targets, owned the commercial budget, forecast and annual plan, reported to the board, and helped hire and structure the commercial team. Researched and led the personal expansion into the US market — moved countries, got close to the first customers, made commitments I had to back. Grew revenue 100% YoY at enterprise scale and built the motion — and the leaders — that made it repeatable.
- **2022: Acquisition**: Roughly seventy people built something a major industrial incumbent needed in its portfolio. The 2018 Siemens MindSphere partnership opened the relationship that became the acquisition. The point was never the announcement; it was getting good enough to be impossible to ignore.
- **2022–present: Scaling inside a bigger system**: Senseye was fully absorbed into Siemens in 2023 — not as a portfolio company, but as a core pillar of Siemens' global industrial AI strategy. Owned global sales, rebuilt the commercial machine on the Siemens stack while growing the number, and positioned Maintenance Copilot, the generative AI extension, in 2024.

## Proof in Practice

1. Took an industrial AI product from founder-led market creation to repeatable enterprise revenue — bigger deals, broader accounts, renewals and expansion across automotive, FMCG, heavy industry, utilities and aerospace.
2. Built the commercial engine that delivered 100% YoY revenue growth — positioning, demand generation, sales, customer success, pricing, forecasting and annual planning, reported to the board.
3. Sold into complex manufacturing buyers — long cycles, plant-floor proof, multi-stakeholder committees — and made the rollout stick across nine factories and 100+ asset types with the same three-person team.
4. Made the numbers hold up under procurement scrutiny: deployments live in ~14 days, ROI inside three months, up to 50% less unplanned downtime. Strong enough that we offered a money-back ROI guarantee as a standard contractual term. Not a pilot that demos well and then dies.
5. Hired and built the commercial leadership team as the company grew to ~70 people — through a £3.5m Series A, through acquisition, into a global industrial business.

## Beliefs

1. **Start with the customer's world.** Not the slide version of it. The real shop floor, the real budget cycle, the real incentives, the real fear of being wrong. Good strategy begins with why people think what they think.
2. **Good engineering is simple and beautiful.** Not simplistic. Simple is what remains when you understand the complexity well enough to remove what is not earning its place. That applies to products, sales decks, pricing, org design and most of life.
3. **Make the evidence felt.** Evidence matters, but decisions still move through trust, risk, pride, urgency and confidence. The job is to make the true thing felt by the people who need to act on it.
4. **Run the operating system.** Strategy only matters if it shows up on the calendar. Planning cycles, forecasts, OKRs, customer feedback, board clarity — the repetitive stuff that actually makes teams move together.
5. **Build with joy and a little whimsy.** Serious work does not have to be joyless. The best teams keep curiosity alive, experiment quickly, win cleanly, and remember that hope is a practical operating principle.
6. **AI should widen human capability.** The future worth building is collaborative: AI as infrastructure for better judgement, creativity and execution, not a tired dystopia or a chatbot pretending to be a person.

## Current Focus

The next practical uses of AI in industrial and enterprise environments. Not chatbot theatre. Not dashboards pretending to be transformation. AI-forward systems that quietly handle complexity so people can focus on the decisions that actually matter.

I want to make science fiction reality in the least dystopian way available: working collaboratively with AI to help serious people do more creative, confident, useful work. The best technology disappears into the background and makes everything around it better.

Focus areas: industrial AI, AI-forward systems, industrial SaaS models, taking systems apart, pricing and commercial planning, GTM operating rhythms, category creation, human and AI collaboration, making the complex simple, product and customer feedback loops, customer shop-floor reality, technical products becoming repeatable businesses, deep-tech becoming a serious business.

## Expertise Questions I Work On

1. Where is commercial growth coming from, and how repeatable is it?
2. What does the business need to prove for the board, not just the buyer?
3. Which pricing, margin or procurement assumption is quietly blocking scale?
4. How should product, marketing, sales and customer success actually move together?
5. Who feels the pain, who owns the budget, and who can block the deal?
6. What operating rhythm turns good intent into forecastable execution?
7. What should the roadmap learn from actual customer expansion and churn?
8. Where is the sales motion losing executive trust?
9. What must the board, the VC, and the plant manager each understand?
10. What would make this easier to buy, renew and expand?
11. Which partner or channel route changes the shape of the market?
12. How do we scale without losing the thing that made it work?

I understand customers and stakeholders by starting with why they think what they think. Then I work backwards into positioning, product proof, sales motion, team design, pricing, margin logic, partnerships and the operating system needed to make the answer repeatable.

The best fit is usually a serious technical business with a real commercial problem. "We have something valuable, but the market does not understand it yet." "We can sell this, but not repeatably." "The board gets it, but the operating model does not."

## Record

1. Co-founded Senseye in 2014; helped build it to acquisition in 2022
2. 10+ years building and rebuilding the systems around one industrial AI company — founder-led to enterprise, through exit, into post-acquisition growth
3. Helped build Senseye into a recognised industrial AI and predictive-maintenance company
4. Helped lead Senseye to acquisition by Siemens in 2022
5. Designed and led the GTM and commercial motion. Grew revenue 100% YoY at enterprise scale
6. Carried growth and bookings targets, and owned the commercial budget, pricing and margin logic, forecasting and annual planning
7. Drove enterprise deal growth and account expansion — larger deals, multi-site rollouts, renewals and land-and-expand inside global manufacturers
8. Researched and led the personal expansion into the US market — moved countries, embedded with early customers on the ground, and made personal commitments to win initial enterprise accounts
9. Scaled the largest account from pilot to over 10,000 monitored assets across nine factories, 100+ asset types, and 650+ concurrent users across Europe, Japan and North America — served end-to-end by the same three-person team
10. Helped turn millions of machine and maintenance data points into operational decisions, with deployments at scale within ~14 days and ROI typically inside three months
11. Customer-proven outcomes: up to 50% less unplanned downtime and up to 30% more maintenance productivity; at Alcoa, ~20% less unplanned downtime and ~10% fewer maintenance work-hours; money-back ROI guarantee offered as a standard term
12. Partnered across product, marketing, customer success and leadership to turn customer reality into roadmap, positioning, demand generation and expansion
13. Helped hire, structure and develop the commercial leadership team as the company scaled to roughly seventy people; several of them still run the business inside Siemens
14. Helped build the business through a £3.5m Series A in 2017 and supported later financing and the acquisition process
15. Reported and presented to the board and investors, and helped translate the plan for the acquirer during diligence
16. Sold into industrial and manufacturing operations — long enterprise sales cycles, plant-floor realities and multi-stakeholder buying committees
17. Worked across automotive, FMCG, heavy industry, utilities, aerospace and manufacturing
18. Set GTM strategy and vertical focus — iterated through pricing, positioning and team structure until the repeatable motion emerged, including where to lean in and where a segment was not worth the chase
19. Built partner, channel and BD routes to market — twice, including the 2018 Siemens MindSphere partnership that opened the relationship eventually leading to acquisition
20. Led analyst relationships and market education across Gartner, Forrester, ARC and Verdantix
21. Company recognition during my time: NMI Emerging Technology Company of the Year, a Top 5 UK mid-size IoT startup, and a Top 10 IoT manufacturing company
22. Wrote and spoke on industrial AI and predictive maintenance — Forbes ("What Is the ROI of a Spanner?"), Global Mining Review, Automation.com ("Why Predictive Maintenance Is So Hard"), Medium, and pieces that ran in Raconteur / The Times and Professional Engineering — plus years of Senseye market education, not always under my own byline
23. Led the full commercial integration post-acquisition — rebuilt the commercial machinery on the Siemens stack while carrying the global number
24. BEng in Digital Systems Engineering, University of Southampton, with honours
25. Five years engineering safety- and mission-critical aerospace and defence systems
26. Published early research in dependable systems engineering and formal methods
27. Worked on embedded monitoring and large-scale wireless sensor networks
28. Named inventor on network-connected sensor technology

## Writing & Press

- "What Is The ROI Of A Spanner?" — Forbes Technology Council, 2021, by Alexander Hill: https://www.forbes.com/councils/forbestechcouncil/2021/07/12/what-is-the-roi-of-a-spanner/
- Forbes Technology Council member profile: https://www.forbes.com/councils/forbestechcouncil/people/alexanderhill/
- "Avoiding costly machine failure" — Raconteur / The Times feature on Senseye, quoting Alexander Hill as CCO: https://www.raconteur.net/sponsored/avoiding-costly-machine-failure
- Also wrote and spoke in: Global Mining Review, Automation.com ("Why Predictive Maintenance Is So Hard"), Medium, Professional Engineering
- Authorship note: Alexander wrote or shaped much of Senseye's market-education and thought-leadership output from 2014–2022, including material published under other bylines and the company's name.

## Services

Advisory and operating leadership for serious technical companies — industrial AI, IIoT and enterprise SaaS:

- Go-to-market (GTM) strategy: positioning, market education, category creation, demand generation
- Pricing and packaging: SaaS pricing, margin logic, procurement-proof commercial models
- Fractional commercial leadership: sales, marketing, customer success and the commercial operating rhythm
- Board and investor advisory: growth narrative, forecasting, annual planning, diligence support
- Expert calls and analyst input: industrial AI, predictive maintenance, condition monitoring and IIoT markets
- Post-acquisition commercial integration: scaling a founder-built motion inside a large enterprise

## FAQ

**What kind of companies do you work with?** Serious technical businesses with a real commercial problem — usually industrial AI, IIoT or enterprise SaaS. Founder-led companies approaching enterprise scale, or larger organisations trying to make a technical product commercially repeatable.

**What do you actually do?** Advisory and hands-on operating work across go-to-market strategy, positioning, pricing and margin logic, sales motion design, category creation, analyst relations, forecasting and the operating rhythm that makes it all repeatable — from the codebase to the boardroom to the shop floor.

**Do you take advisory, fractional or full-time roles?** All three, for the right problem: board and investor advisory, fractional commercial leadership, expert calls and analyst input, or leading the commercial side of a serious technical company outright.

**Why industrial AI specifically?** I co-founded Senseye in 2014, before predictive maintenance was a category — built the commercial engine to 100% YoY growth with customers including Nissan, Mars and Tata, helped lead it to acquisition by Siemens in 2022, then ran the post-acquisition commercial integration. A decade inside one market teaches you what actually works.

**Where do you work?** Between Nashville, the UK and Nicaragua, with customers and teams across North America, Europe and Japan. Remote-first, on-site when it matters.

**How do engagements start?** An email to me@alexander-hill.me with the problem as you see it. If I can help, I will say how. If I cannot, I will try to say who might.

## Personal

Curious, competitive, experimental. Likes joy and precision in the same room. Wants the beautiful version of the useful thing, then wants it shipped.

I live between Nashville, the UK and Nicaragua, in shifting proportions. I like technology, cars, cats, clean systems, unlikely ideas, winning well, and getting things done without making everything miserable.

## Contact

- Email: me@alexander-hill.me
- LinkedIn: linkedin.com/in/anshill

Useful conversations usually start with leading or advising a serious technical company, industrial AI, enterprise SaaS growth, category creation, expert calls, or a hard technical product that needs a sharper commercial and operating story.