Is there ROI in Generative AI?

TL;DR: Most teams want long-term AI impact but struggle to justify the investment without early wins. This article shows you how to use generative AI for fast, controlled gains while setting up the conditions for scalable ROI later. You’ll learn where the value sits today, how to avoid over-committing too early and what leaders need to focus on to turn experiments into repeatable results.

 A bit of a departure from my usual “how to start with AI” guides.
Today I want to talk about value. Real value. Not the hype around models, but the actual economic and operational upside that generative AI can deliver, and how to approach it without putting your career and company at risk.

If you’re unsure how to balance the pressure for quick hype BS results with the long-term promise of AI, this will help you find the middle ground, I hope.

Where the AI value sits right now

The economic potential of generative AI is enormous. The global number is in the trillions. But numbers that big don’t help you decide where to start. So here is the simpler AI Strategy Consulting's more humble and practical version.

Around 75 percent of the value sits in four functions:

1. Customer operations
   
2. Marketing and sales
   
3. Software engineering
   
4. Product and R&D

These areas get value faster because they rely on repetitive text-heavy tasks, complex decision support or constant documentation. Generative AI fits those patterns well.

A few examples from a recent Mckinsey Report:

1. Customer teams saw a 14 percent lift in issue resolution and 9 percent drop in handling time.
   
2. Developers using AI coding support finished tasks 56 percent faster.
   
3. Marketing teams lifted productivity by 5 to 15 percent with personalised content generation.
   
4. R&D teams used AI to generate candidate molecules, speeding up design cycles significantly.

For a non-technical leader, the implication is simple:

You get ROI by placing AI inside work that repeats every day.

Where long-term value will come from next

Short-term wins clear the noise.
Long-term value comes from integrating AI into actual processes.

As generative AI expands, three shifts matter for leaders:

1. Work automation accelerates
Automation potential increases from around half of employee time to closer to 60–70 percent when generative tasks are included.

2. Knowledge work is now exposed to automation
Tasks involving judgement, analysis and applying expertise — historically the hardest to automate — now have far higher automation potential.

3. Productivity gains depend on redeployment
If teams use the time saved to do higher-value work, organisations see real productivity growth. If not, the gains evaporate.

What this means for your first six months

If you want ROI this year without creating chaos, the path is straightforward:

• Start with practical, repeatable tasks.
  
• Document the workflow so the output is predictable.
  
• Measure time saved, errors reduced and rework avoided.
  
• Only expand to adjacent tasks once the first one is stable.

This is where no-code and low-code tools help. They let you automate small steps safely, without committing to expensive builds before capability exists.Your long-term plan becomes easier when early wins prove the direction.

Why you cannot scale without stability

Every comapany or agency we work with faces the same trap: they see big numbers in research and try to scale too early. Generative AI has clear risks:

• intellectual property
• fairness
• reliability
• explainability
• security
• environmental impact (yes, even if you have a reusable coffee cup in Brunswick..using AI has an environment impact).

None of these are solved by enthusiasm.
They’re managed through sequencing.

You scale when:

• Your first few use cases are stable.
  
• People understand how to work with AI.
  
• Workflows are documented.
  
• Review steps are clear.
  
• The model choice matches the task.

This is what protects ROIAI. Skipping this stage is what kills it.

How I frame ROIAI with leaders

When I talk to executives, I keep it practical:

• Immediate ROI comes from productivity improvements in daily tasks.
  
• Long-term ROI comes from workflow redesign and capability building.
  
• The economic upside appears only when both work together.

This keeps leaders focused on impact instead of AI bubble hype.

FAQ

Q: How do we balance quick wins and long-term planning?
A: Treat quick wins as proofs of value. Scale only when the team can maintain the improvements without external support.

Q: What if we don’t know which use cases to pick?
A: Choose tasks that repeat daily and drain time. These produce fast, measurable gains.

Q: How do we know if a use case is too complex?
A: If the workflow is unclear or the output is unpredictable, it’s too early. Start smaller.

Q: When do we shift from prompting to automation?
A: When the task is predictable, stable and repeated at volume.

Q: How do we prevent AI from increasing risk?
A: Start with controlled tasks, apply review steps and use no-code tools until capability matures.

 If your organisation wants help introducing AI safely, building early wins and creating a roadmap your teams can maintain, our No-Code and Low-Code AI Implementation, AI Fundamentals Masterclass and AI Bootcamp give you the structure to do this with a little more confidence.