Moonshots are for rich kids

Moonshots are for rich kids

The endless hype around AI has some companies thinking that they need to start working on their AI moonshot. We believe the opposite path will lead to true transformational value.

5
min read time

Dream big, but start with small, practical steps.

Business leaders have a compulsion to go big. It’s kind of their mandate. There’s nothing wrong with being ambitious or having lofty dreams. But the kind of early over-thinking, over-planning, and over-committing associated with moonshots is exactly what dooms projects (and careers). 

Plenty of businesses involved in AI are doing moonshots at significant cost. OpenAI raised USD 6.6 billion in late 2024, having already used the USD 10 billion it raised in 2023. But I’m guessing your company doesn’t have those sort of resources or the luxury of waiting five years before that investment starts paying off. 

We’re lucky enough to see and be involved with seriously successful AI projects, and I can tell you that while most of them are transformational to the business, none of them started off as moonshots. Those are for the rich kids. We need to be smarter than that. 

Remember what the moonshot involved  

Let’s be clear about what the actual “moonshot” was. The Apollo program was conceived during the height of the Cold War and aimed to leapfrog the Soviets, who had most of the early successes in the “space race.”

Here is the relevant part of today's discussion: unlike earlier incremental steps, the Apollo program represented a dramatic leap involving new technologies, engineering breakthroughs, the assembly of a vast team of scientists and engineers, and unlimited amounts of money, people, and resources that a nation can provide. Does that sound like a sensible business strategy? 

The business landscape is littered with expensive moonshots. 

It’s easy to think that AI is a new technology that has only emerged in the last couple of years. But it has been around for a long time and has had plenty of time to wreck reputations and careers with high-profile (and presumably many low-profile) moonshot fails. 

Some of the eye-popping ones:

  • IBM invested $62 million to develop an AI called Watson for cancer treatment recommendations. The system provided unsafe and incorrect suggestions, such as recommending a drug that could worsen bleeding in a patient with severe bleeding. The project was ultimately cancelled, and the business unit was sold off for parts, resulting in billions in losses.
  • Zillow’s AI-driven home-flipping program, iBuy, relied on property valuation models that overestimated market trends, leading to significant financial losses. After losing USD 380 million, the company shut down the program and laid off 2,000 employees (25% of its workforce).

Why moonshots are problematic 

For most businesses, they come with three big problems:

  1. No playbook: AI moonshots are uncharted territory. Building something brand new means no one’s done it before, and there’s no playbook. First attempts are notoriously expensive and prone to failure. A moonshot could sink your budget if you’re not ready to absorb the cost of mistakes.
  2. No ROI: Big AI projects take years to deliver value, if they deliver at all. That’s like pouring water into a leaky boat and hoping it stays afloat. Most businesses can’t afford to spend millions on a project that might only pay off five years down the road.
  3. No foundation: AI is evolving fast. The tools, techniques, and even the ethics of AI are shifting under our feet. A moonshot might take years to execute—and by the time you finish, the landscape will look completely different.

So, if moonshots aren’t the answer, what is? Start small, go deep, and focus on solving problems that are hyper-specific to your business. To do this, you need to deploy cross-functional teams of a specific nature. We call them Tiger Teams. 

The Tiger Team approach

We've found that successful AI projects start with what we call Tiger Teams—small, focused groups with clear accountability. Here's the winning recipe:

  1. A business SME at the core: Not just any subject matter expert, but someone who lives and breathes the problem you're trying to solve. I've seen too many AI projects fail because they were driven by tech people who didn't truly understand the business context. Your SME ensures every decision ties back to real business value.
  2. Technology team with guardrails: Our tech teams are enhanced by working in a new way:  they work under the SME's guidance. They bring deep technical expertise but also the humility to understand that technology serves the business, not vice versa. 
  3. Product and analytics specialists: I'm a product guy, so I'm biased, but having a strong product manager and data analyst on the team is crucial. They keep the project focused on user needs and measurable outcomes. We aim for 4 week delivery cycles with clear, value-centric metrics.
  4. Other specialists: This depends on the project, but it might involve bringing in a secondary specialist… even the radical step of involving marketers (just kidding…. we love you, marketing!).

Tiger Team assembled, now what?

We use two essential frameworks at Machine & Partners to ensure success. They're so important that we discuss them frequently (on our website and in articles like this one), and I'll share the essentials here.

First, use DEW to identify promising AI opportunities:

  • Data: Do we have the data to support a successful AI project? 
  • Expertise: Is there critical knowledge trapped in people's heads? 
  • Workflows: Are there repeatable processes ready for enhancement? Look for tasks that are routine but require human judgment.

Then, run potential projects through the VVVroom test to assess feasibility:

  • Viability: Can you build it with your current resources and technology?
  • Value: Will it deliver meaningful ROI within 6 months (or whatever ambitious but attainable period you think is appropriate)?
  • Velocity: Can you get a working prototype in (example) 8 weeks?

Big Vision, Narrow Focus

The smaller the scope, the greater the impact. When you focus on one high-value use case, you:

  • Deliver measurable ROI faster (think months, not years)
  • Earn confidence in AI within your organization through quick wins
  • Create a repeatable process for future projects
  • Build the foundation of an extendable platform 
  • Keep costs under control 

The good news is that while there are many examples of moonshots failing, there are plenty of success stories for small, deep, and impactful AI projects that scale up. Here are two examples that dreamt big but started with small, practical steps:

Choose Your Next Move

If you're considering an AI project, ask yourself: Is it a moonshot or a meaningful step forward? Starting small doesn't mean thinking small. It means being smart about where you place your bets.

Leave the moonshots to the billionaires. Let's focus on what works. Get in touch if you want to get value from your AI projects and see results within weeks or months, not years. We deliver ambitious, transformative AI without sending your bills to the moon and back.

about the author

Ed is a partner at Machine & Partners. He spends way too much of his free time trying to keep up with the news and advancements in AI. The rest of the time he's playing tennis, driving his teenage daughter around, or cooking with this therapist wife.

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