Impact of Generative AI on Services Sector

Overview

If you are following business news, i am pretty sure you are, it is impossible for you to escape news on generative AI. While the hype is real, how big is its impact on services sector? How will the hype play out in operational terms? How should you go about thinking on ways to implement AI into your operations strategy? This is a high level brief on the impact of gen AI on services sector.

Economic Impact of AI on World GDP

As per research by Goldman Sachs, gen AI is expected to raise global GDP by 7%. This is mainly based on the productivity growth of workers around the globe.

Gen AI is expected to have a higher impact on developed economies than on developing economies. This is due to the higher share of services sector and the prevalence of automation in services and manufacturing in developed economies. As per IMF, high prevalence of jobs that can be easily replaced by robots will provide opportunities for wider application of gen AI.

Thus gen AI is expected to widen the income gap between advanced and developing economies. But how will this play out in the services sector?

Productivity Gains

Services sector primarily depends on processing of information to render value to customers. By design, every interaction with a customer produces even more information. For organizations operating in the services sector, it can result in a positive feedback loop if the data generated with every interaction is effectively harvested. Here gen AI can make a difference. Generative AI, with its ability to understand patterns and act upon it, can reduce cycle times across value chains.

Throughout a customer lifecycle, from the time a prospect is identified till the customer churn, historical information about the customer and desired service is used, processed and acted upon. A customer cares about how quickly his/her service need is met with the highest quality. All the time taken in between to gather information and process them are irrelevant from a customer perspective. But those activities constitute a major chunk of work for service sector employees. It makes sense to look at these scenarios in Financial services and Insurance sectors.

Financial Services

As an example, in Wealth management the customer cares about achieving desired returns. All the value chain activities such as KYC document collection, AML reviews, risk management, trade settlement, etc. are irrelevant from a customer perspective. But these activities have to occur for a bank to effectively render services to its customer. Gen AI can help complete these activities accurately and quickly.

Healthcare

Similarly, in healthcare a customer cares about how quickly their service is pre-authorized and claim is paid. All the activities related to reviewing the service, customers healthcare history, claims history, provider’s relationship with payer, claimed amount, etc are irrelevant for the customer. But those activities constitute the primary job of healthcare services employees.

On the other hand, customers are more open than ever to utilize AI for their needs.

Knowledge Workers & Gen AI

All though, at first glance, gen AI might appear as a threat to knowledge workers, with the right organization design it can supplant their skill sets.

Yes, a lot of services sector jobs that require processing of information will be automated by generative AI. But the customer needs are always evolving. So service sector employees are better off focusing on customer needs and innovating to meet those needs than processing customer information. The need for customer acquisition will always remain. The need to retain and cross sell existing customers will continue to be a priority as well. Those employees that can harness the power of gen AI to achieve these results will thrive in a AI world.

But what can organizations do to enable this?

Action Items for Organization Decision Makers

It makes sense to view the impact of gen AI at 3 levels.

Business Model: Gen AI promises to jump start productivity growth spurt across the economy. Decision makers in financial services and insurance sector should establish an end state vision at various stages of AI adoption. In a fully AI automated scenario, how will our customer needs differ from today? Will the customer still need their wealth to be managed? If yes, what different types of services would they expect from us? If we automate the customer service interactions, will the customer be interested in new offerings from us? Does the customer need a human touch when it comes to advise on their wealth?

For insurance companies, with a reduced expense ratio, what other types of services can we offer the customer? Will AI reduce the demand of insurance from our corporate customers? If yes, where else can we make for the revenue downfall? As per HBR, Administrative expenditure accounts for 15–30% of health care spending in the U.S., of which about half is consumed by hospitals’ management of billing- and insurance-related expenses. And even these estimates are unfairly low as they ignore non-dollar indirect cost, borne by patients and their families — the time spent fighting for insurance coverage and clarification on billing. Allowing artificial intelligence to break the silos between insurers, hospitals, and consumers would automate claims management, prior authorization, and even payment planning and collections, helping to eliminate a massive drag on system efficiency.

Organizational Structure/Talent Management: As previously mentioned in the article, in the AI dominated world employees with customer orientation are more likely to thrive. Decision makers need to think how to organize their people around AI tools. How will the new business units look like? Will it be a human layer above an automation layer? Can service offerings be structured as those served by automation layer and high value customers served by the human layer? Should organizations create cross functional teams that resolve friction and enable various units to seamlessly work with AI? What type of teams are needed to feed the AI algorithm?

Generative AI Models: What use cases should we prioritize for gen AI? Which use cases have the highest impact while allowing continuous learning? How will generative AI be monitored and maintained? What Lines of Business and Business Units are better suited for gen AI adoption? How can we track the KPIs on gen AI performance?

These are some of the questions to ponder about, as we go about incorporating AI in our world.

Executive Summary

Understandably there is both excitement and fear about the impact of generative AI. In my view, while the productivity gains from gen AI addresses the supply side, the implications on the demand side cannot be ignored. How can organizations deploy gen AI to create demand for their products? Even with gen AI, the human needs as defined by Maslow’s hierarchy of needs are not changing. Those organizations that can re-design themselves to serve those needs with generative AI will thrive. Exciting times indeed.