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Brain Circuits
How to resolve the five trade-offs of AI
The democratization of AI can bring great productivity and efficiency gains, but it also poses significant risks. Michael R. Wade, Sarah E. Toms, Amit M. Joshi, and Michael D. Watkins identify five key strategic tensions that you need to resolve to capture value.
1. Speed versus caution
- You don’t want to be left behind in the race for productivity and other advantages, but there’s also a danger of over-investing and moving too quickly when you don’t know what the risks and benefits might be.
- Start with lower-risk, simple applications in areas that will have the biggest return on investment, then monitor, learn, and adapt as you go.
- Starting simple has many benefits, including giving you a quick win and letting your people see first-hand what it takes to ramp up to more complex AI projects.
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2. Internal versus external use cases
- Don’t get stuck trying to decide whether to leverage AI to improve your internal processes or to create new products or services. GenAI can do both – and so should you. The decisive factor for the organization is where it will add the most value.
- Identify a set of clear business challenges, both internal and external, and focus on where AI can provide the most benefit.
- Avoid the temptation to launch dozens of interesting but disconnected AI initiatives.
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3. Build versus buy
- Be agile in the way you architect and execute your AI strategies.
- It’s good practice to utilize commercial tooling where possible…
- … but make sure your plan allows time for the technologists to adjust the AI solutions you adapt from external providers or develop in-house.
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4. Re-skill versus replace (or reduce)
- Responsible employers will also consider the social and ethical implications of using AI to replace human labor along with productivity and cost gains.
- Explore strategies for retraining and upskilling affected employees.
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5. Environmental impact versus efficiency gains
- The environmental impact is often overlooked in the debate around GenAI adoption, so ensure that sustainability is one of your key AI principles.
- Educate your organization about AI’s footprint and prioritize the measurement of resource consumption.
- With this transparency in mind, strive consistently to reduce the harmful impact of AI on the environment.
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Key takeaway
You have to balance the need for accurate predictions with the ability to interpret and explain AI decisions, especially in high-stakes applications such as healthcare or finance – but get it right and you will reap big productivity and efficiency gains, and likely gain competitive advantage.
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Further reading
AI’s five strategic tensions and how to resolve them
Bias in Generative AI: A risk that must be addressed now
Three ways GenAI could transform supply chain management 
Think AI is a useful ‘copilot’? Soon it will take the controls 
All views expressed herein are those of the authors and have been specifically developed and published in accordance with the principles of academic freedom. As such, such views are not necessarily held or endorsed by TONOMUS or its affiliates.
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