When It Comes to AI, Productivity Is Not Strategy

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Today, AI’s chief use cases are productivity and efficiency. It’s tempting for companies to assume that leveraging AI to realize some of these gains may be sufficient to appease anxious stakeholders, generate some good will and some good press. However, this may prove a little short-sighted, and not just for those operating in highly-exposed knowledge-intensive industries like the legal and professional services.

You see, every organization today is made up of essential business units and functions, that for the most part will be similarly exposed to AI. Start with customer service and keep going till you’ve included marketing, HR, finance and accounting, operations, supply chain management, legal and compliance. The fact that knowledge-intensive industries and functions are highly exposed has been well documented. The interesting question is what will the result of these productivity gains look like, at least in the near term?

Impact of higher productivity

This is still speculative, but it’s reasonable to assume that as usage of newly developed AI solutions and systems proliferate and more tasks and processes become automated, the supply curve for impacted services will shift to the right and, assuming demand stays constant, result in lower prices. These services will become commoditized because AI can replicate these at scale.

Assuming AI tools continue to develop at the same pace, services rendered will become less differentiated, with price becoming the main competitive lever. Will at risk firms be incentivized to continue to compete for these services? Or will they be assumed by other AI-enabled service providers who can assume and automate high volume, lower-medium complexity tasks at scale via AI solutions; doing for information services what BPOs previously did for customer support?

The emergence of large-scale multi-purpose AI-enabled service providers?

Let’s examine this possibility a little more. How will these new service providers operate? It’s likely that they will target in-house consumers of at-risk services versus the service providers themselves. Think in-house legal teams instead of law firms. Why? Look at the incentives. Law firms may use AI tools to improve productivity but will they pass on the savings to customers? In-house counsel are mostly comprised of lean teams dealing with a heavy load comprising of voluminous mix of low value and high value work. They are well incentivized to hand off lower value work to credible service providers to have a chance of upgrading the services they offer to internal stakeholders. Law firms, and other companies, that are being disrupted will naturally try to stave off the impact of the disruption (or live in denial) for as long as possible because making sense of disruption takes time, there are trade-offs to consider and business models and strategies to adapt.

However, we digress. These new service providers can leverage economies of scale to offer commoditized services at very low cost (or free) with their investments in technology being amortized over large numbers of transactions or users. Some may employ loss-leader strategies or even bundle their service offerings. Maybe all non-essential business functions will eventually be outsourced to a service provider that leverages AI agents, who never sleep, eat or take a break; and simply get better and better at what they do for your business the more time they spend doing it because…machine learning.

Too fanciful to believe? Well consider that for a time, businesses used to host their own data servers and infrastructure until cloud services like AWS, Azure, and Google’s Cloud Platform developed and offered infrastructural services on a commoditized basis. Why’s it so hard to imagine that the ‘infrastructure’ of an organization might be similarly outsourced?

What happens after productivity?

I’m not (always) a pessimist, but… people are going to lose their jobs because companies are going to be forced to change or go out of business. In fact, they already are. As industries and companies shrink and become leaner, demand for certain tasks and their corresponding roles will simply fall or disappear and in other cases, in-demand work will simply be performed by smaller and AI-augmented teams.

What about people who lose their jobs? They can always up-skill or re-skill right? Well, there are lots of reasons why that isn’t nearly as simple as it sounds but the more pertinent question is: re-skill or up-skill to do what exactly? Especially when most low-mid tier white-collar roles are at risk and a massive surge in demand for blue collar services remains unlikely in the near term.

Well, perhaps the answer lies with business leaders. Specifically, on their willingness and ability to do the hard work and determine what the future will look like? It calls for them to not fixate on or mistake productivity gains for competitive advantages, and to ask the hard questions and make the difficult choices. New jobs arise because there are new problems that need new solutions. To get here, business leaders need to actively consider and anticipate what the future needs of society might be and set out to validate and solve for these. In fact, this is what the high-performers are already doing.

So the real question is: how might at-risk industries leverage AI, not just to realize productivity gains, but to create entirely new value propositions and enhance their competitive advantages? If we adapt McKinsey's Three Horizons of Growth for our purposes, business leaders might consider asking themselves the following:

• Horizon 1 Perspective: What must we excel at today to maximize our current strengths and stabilize our core business using AI? This involves identifying areas in the core business that can benefit from AI to enhance efficiency, reduce costs, or improve product quality. It's about ensuring the company remains competitive in the short term while setting the stage for future growth.

• Horizon 2 Perspective: What new opportunities can we pursue that align with AI advancements and our organizational strengths? Here, the focus is on medium-term opportunities that AI can unlock. This might include new markets, new customer segments, or products and services that can be developed using AI. It’s about leveraging AI to build on the company's capabilities and extend its market reach.

• Horizon 3 perspective: What are the transformative aspirations that AI can help us achieve in the long term? This is about looking into the distant future to explore how AI might fundamentally change the industry or create entirely new business models. It involves imagining future scenarios where AI leads to significant shifts in how businesses operate and deliver value, and then setting an aspiration that aligns with these possibilities.

The impact of AI disruption is undeniable. Most will react by focusing on productivity. Leaders respond by focusing on strategy.

Thanks for reading,

Hardesh.

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