AI ROI vs AI R&D: Knowing Which Game To Play

Hi there,

"We need AI to transform our business", the CEO declares in the morning. "But it needs to pay off within 6 months", adds the CFO in the afternoon. Sound familiar?

One of the most common ways companies fail with AI isn't technical - it's strategic. They confuse two fundamentally different games: ROI projects and R&D initiatives. But when you mix them, you get the worst of both worlds: R&D that's too rushed to innovate, and ROI projects saddled with unrealistic transformation goals.

Today, I'll show you what these games look like - and how you play them right.

Let's dive in!

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Two Different Games

Let's break down these two distinct approaches to AI implementation:

Game 1: ROI-First (aka Quick Wins)

When you're playing the ROI game with AI, you're looking for proven patterns and immediate impact. Think automation that cuts costs, recommendations that boost sales, or analytics that reduce waste.

Success looks like:

• Clear financial returns within 3-6 months

• Small, focused projects solving specific problems

• Proven AI approaches already working in your industry

• Minimal technical risk and experimentation

Example: Automating invoice processing with AI to reduce manual labor costs by 30% within three months, using off-the-shelf machine learning tools.

Game 2: R&D (Long-term Transformation)

The R&D game is about building capabilities that could transform your business - even if you're not sure exactly how yet. You're investing in possibilities, not guaranteed returns.

Success looks like:

• Technical breakthroughs and capability building

• Finding novel approaches that could change your industry

• Gathering new insights that could eventually lead to better business

Example: An industrial manufacturer developing AI that can learn to operate new equipment with minimal training.

When Companies Mix Them Up

The problem occurs when leaders try to dress up R&D as ROI to get funding ("Sure, we can deliver that in 6 months!") or burden simple ROI projects with transformation expectations ("This chatbot will revolutionize our industry!").

This causes both games to fail:

• R&D gets killed for missing short-term targets

• ROI projects get overcomplicated trying to be "transformative"

• Teams lose credibility as promises go unmet

• Future AI initiatives become harder to approve (AI fatigue)

So eventually, confusing ROI and R&D will cost you more than hard cash. It will cost your credibility (which is much worse). The solution (obviously) is to be ruthless honesty about which game you're playing.

If it's R&D, say so. If it's ROI, keep it focused.

Warning Signs For When Games Get Mixed Up

How do you make sure you don’t accidentally sell an ROI project as R&D or vice versa? Here are some warning signs:

⚠️ High ROI in Short Time Promise

The most dangerous pattern is promising ROI timelines for what's really R&D work. Watch for these red flags:

🚩 AI is seen as the quick fix for a long-lasting, organizational problems
🚩 You're spending more time on discussing the project instead of actually doing it
🚩 Project goals keep shifting without any good reason

⚠️ Buzzword & Complexity Land

Equally problematic is loading transformation expectations onto simple ROI projects:

🚩 Simple automation branded as "digital transformation"
🚩 Over-engineering solutions to be "future-proof"
🚩 Blocking quick wins because they're "not innovative enough"
🚩 Analysis paralysis from trying to solve everything at once

A Rough-Cut Decision Framework

If you're not sure whether your project should be primarily focused on ROI or R&D, here's a simple flowchart for you:

If you're solving a problem that's new to you, you're probably in R&D land from the start. If the problem is known, but you need results in less than 12 months, you're usually looking at an ROI project. If you don't need immediate returns, your project could be both, depending on whether or not your focus is on building strategic capabilities.

Balancing Both Games

Of course, the most successful AI organizations run both ROI and R&D initiatives at the same time. But they keep them explicitly separate. They maintain distinct teams, success metrics, stakeholder expectations, and reporting structures for each type of initiative.

A prominent example of this is Toyota. Toyota that has explicitly separated its AI initiatives into two distinct streams: ROI-focused initiatives invest heavily in AI to improve operational efficiency and enhance its existing product lines in its core business, while its Toyota Research Institute was already provided 10 years ago with a $1 billion investment to focus on long-term AI research, particularly in robotics and autonomous driving.

An ROI AI portfolio collects your quick wins strategically. These projects are about delivering predictable value in predictable timeframes.

It's also the kind of AI project is like working on most because it's easy with those to reach $10K+ additional profit in one quarter even for a very small organization.

Popular ROI cases include areas like:

• Customer service automation

• Marketing & sales optimization

• Pricing & finance operations

For an AI R&D Portfolio, the cards look a little different. Your success metrics are more output/learning-oriented than financial.

Your R&D portfolio might explore:

• Novel customer experiences

• Industry-changing capabilities

• Fundamental technical breakthroughs

• New business models

To manage both of these portfolios successfully, you have to set clear boundaries. Concretely, that means: Separate budgets, different reporting structures, different success metrics.

Communicating Your Game

The key is, that you communicate these intricacies to your stakeholders openly and transparently.

Here are some communication examples of how successful teams frame different types of AI initiatives:

ROI Projects:

• "We're implementing AI-powered email response suggestions that will:

  1. Save 15 minutes per agent per day

  2. Reduce response time by 30%

  3. Pay back investment in 4 months."

• Success = $200K annual savings by Q3

• "Our AI pricing optimizer will:

  1. Increase margins by 2% on commodity products

  2. Generate $1.5M additional profit in first 6 months

  3. Require minimal IT changes"

• Success = Profit increase verified by A/B testing

R&D Projects:

• "We're exploring AI-powered product design that could:

  1. Reduce development cycles by 50%

  2. Enable mass customization at scale

  3. Transform our business model by 2026"

• Success = Proof-of-concept showing 3x faster prototyping

• Our conversational AI research aims to:

  1. Develop natural language understanding beyond current limits

  2. Enable truly personalized customer interactions

  3. Build foundational capabilities for future products”

• Success = Demonstrating understanding of complex customer intent

Connecting ROI And R&D With Your AI Roadmap

Even when keeping ROI and R&D separate in daily operations, it's a smart idea to connect them to your larger strategy:

• ROI wins build credibility for R&D bets

• R&D insights inform ROI opportunities

• Both contribute to long-term success

What good AI roadmaps do is that they define clear swim lanes for ROI and R&D projects, but allow to align them in a way that quick wins pay forward to the long-term vision, making dependencies explicit.

For example, I've recently helped a call center company explore text-based chatbots for internal purposes, but at the same time we also keep an eye on current voice-to-voice capabilities (without integrating that already). Once these are reliable and cheap enough, they can combine the experience gained from text-based chatbot and ramp up an AI voice assistant pretty quickly.

I'll write more about effective AI roadmaps in my Profitable AI Notes over the next couple days.

Conclusion

Success with AI isn't about choosing between ROI and R&D forever - it's about being crystal clear about which game you're playing at any given moment. You'll often find projects stuck between games, trying to serve both masters. These will die sooner or later.

Create a roadmap that shows how your ROI and R&D initiatives tie into your larger strategy. Let your quick wins build credibility and capacity for your longer-term bets.

The most successful companies aren't the ones with the biggest AI budgets or the most ambitious goals, but the ones that are honest about what game they're playing-and then play it well.

See you next Friday!
Tobias