To use AI tools smartly, think like a strategist

by Phanish Puranam
Consider how specific AI tools will impact your own skills and capabilities.

Much talk and concern today revolve around the question of whether AI will render humans redundant or, conversely, if it will propel us towards superhuman abilities.

Instead of watching these trends passively to see how it all turns out, I believe each of us must adopt a strategic approach when it comes to using AI in our daily work. This approach should consider how AI tools can impact our individual capabilities.

Let's delve into a straightforward example. Suppose you must create a report summarising recent sales data and forecasting future trends. Currently, you rely on tools like Excel, Word and Tableau, as well as your own intelligence to crunch numbers and provide insights.

Here are three possible scenarios:
  • Scenario A: AI helps you without changing your existing capabilities
    • Let’s say you begin to use a canned machine learning algorithm that draws on a customer review dataset to do a language processing analysis. This improves the quality of your report. This has not altered your capabilities in any meaningful way – unless you count pressing the button on this app as a capability. Much like before, the report essentially relies on your analysis with an added-on piece. 
  • Scenario B: AI boosts a capability you already have
    • Suppose you adopt an application that guides you in using your regular Excel data to fit a more sophisticated machine learning model with greater predictive accuracy. This effectively expands your capabilities. You’re still very much the one steering the analysis and shaping the result, but AI tools expand your own skill set. 
  • Scenario C: AI eliminates the need for you to use one of your capabilities
    • You no longer look at the data and simply rely on an algorithm to do the crunching and come up with an analysis of past trends as well as predictions about future sales. You then inspect the trends visually and offer your qualitative interpretation (as before). Overall, the algorithm is doing a portion of the work you did before. And you can bet that over time, your skills relating to what you no longer do will decay. 
While the quality of your final report improves in all three scenarios, your personal skills and capabilities remain unchanged (scenario A), increase (scenario B) or decline (scenario C). The question then arises: Should you be indifferent in choosing between these three scenarios?

Making strategic choices

I could have replaced this example easily with other tasks like hiring, project investment or supplier selection. But its insight also applies to more micro-level tasks, such as creating a PowerPoint presentation, replying to e-mail or drafting a business plan. In each case, you can use AI in ways that will maintain, enhance or devalue your existing capabilities.

Corporate strategists recognise these as decisions very similar to those that companies face when outsourcing or partnering with others. Outsourcing involves reliance on external partners and potentially losing competence in outsourced tasks. However, this can be a deliberate choice, made for strategic reasons, to focus on developing other capabilities with the resources and attention released.

For example, a bank may decide that processing transactions or developing software is not core to its competitive advantages and choose to hive these tasks off to a vendor. Indeed, few of us lament the loss of navigational skills in the GPS era or manual laundry skills now that we have washing machines. We use that time for doing something else we value. In a work context, that would mean developing new skills that could give us a worthwhile competitive advantage.

But companies also often adopt strategies that involve retaining certain in-house capabilities even while relying on outsourcing. For example, many automakers still manufacture some components in-house, while outsourcing much of the rest. Chip manufacturers do R&D and file patents in technology domains that require chips, even if they’re not themselves making direct sales in these domains. This approach helps them maintain in-house skills, as well as evaluate vendors and partners effectively. It also helps them wield bargaining power while ensuring supply chain resilience.

Think it through

Likewise, individuals may choose to hold on to skills or capabilities for reasons such as resilience, bargaining power or preserving a sense of self-identity and efficacy. In such cases, one might prefer scenario B (boosting existing capability) over scenarios A or C, even if the final performance outcome seems lower in the short term.

Don't be taken in by the language of “augmentation” or “human centricity”. These terms may conceal more than they reveal. For instance, the first two scenarios (neutral effect or boost of existing capability) both plausibly fall under the augmentation category, but the second is superior to the first from the viewpoint of preserving and enhancing human skills. Human centricity is an even more ambiguous label. Presumably, the superior report you would produce in any of the above scenarios is better for at least some humans, namely your bosses! Labels such as augmentation and human centricity encompass a wide spectrum of impacts on your own capabilities, and it's up to you to safeguard them.

The decision to embrace AI and maintain or relinquish certain skills should be a strategic one, aligning with your individual goals and values. In particular, it is always essential to ask what new capabilities this will help you develop – because in the knowledge economy, if you aren’t learning, you aren’t earning.

Phanish Puranam is the Roland Berger Chaired Professor of Strategy and Organisation Design at INSEAD. 
Edited by: Isabelle Laporte.

Useful resources:
INSEAD Knowledge
INSEAD Knowledge showcases faculty research with an emphasis on practical solutions.

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