Boosting productivity: The CEO's guide to AI strategy

CEOs play a key role in championing an AI business strategy, creating value, and encouraging experimentation. They need to identify use cases where generative AI can deliver benefits, invest in data quality and security, and foster a culture of learning and iteration.

The rapid rise of generative AI is taking place against a backdrop of decreasing productivity. Canada is not keeping pace with productivity on a per capita basis, relative to our international peers.

Moving forward, generative AI could become part of the answer to combatting economic stagnation and productivity gaps, not just in Canada but around the world. In this environment, business leaders need to be technology leaders, and CEOs need to decide if they’re going to lead the way in becoming a data-driven organization — or risk being left behind.

CEOs should keep in mind that generative AI isn’t just about personal productivity—such as drafting emails and creating presentations—though that’s where it gets the most attention. Productivity with AI is also about identifying efficiencies for core businesses operations, like machinery, equipment, systems, and resources. In other words, it’s not just about the productivity of people, but the productivity of an organization’s other assets across the entire value chain.

For example, an insurance company might build generative AI into claims processing to reduce fraud, while a manufacturer might use generative AI-enhanced predictive maintenance to boost the life span of equipment and reduce unplanned downtime. At KPMG, we’re building generative AI into our audit methodology to further de-risk audits—such as performing automated matching of cash to revenue using data from client accounting systems—which enhances our overall productivity.

One of the biggest challenges that organizations face is transforming raw data into insights that deliver business value; most of the time and effort will be spent on data preparation before any real AI takes place. But this is an essential step, since poor data quality could lead to inaccurate results and misguided decision-making.

There needs to be awareness at the board and executive levels around the power of AI, but also the risks associated with the technology—as well as an understanding that generative AI may not yield immediately quantifiable impact.

Initially, generative AI projects may seem overly simplistic, but that doesn’t mean AI isn’t working in their organization. Success will take time, so rather than get demotivated, CEOs will need to take a push and pull approach with growing AI —pushing for innovation and pulling for an assessment of failures and gaps. Becoming a data-driven organization requires a mindset that values iteration and learning.

Generative AI is still in its infancy, but it could disrupt entire industries and change the way we interact with computers. In the short term, generative AI can create efficiencies and enhance customer service. In the medium term, generative AI can enable more personalized and accurate services. In the long term, generative AI can transform the human-computer interface and create new possibilities.

This might mean creating an AI centre of excellence or working group to assess the impacts of generative AI across the organization over periods of time. This centre of excellence would govern what’s happening with use cases across the organization and what’s required to achieve key metrics. It would also look at how AI could put the organization at risk and what security guardrails need to be implemented. Simultaneously, creating an AI centre of excellence would equip the organization with in-house AI experts.

The short-term

There are many examples of real-life AI use cases happening now, including:

Legal
KPMG in Canada has built an accelerator that can analyze and extract key information from contracts, such as obligations and entitlements; it can also generate contracts. This not only saves hundreds of hours on manual tasks, but also delivers faster and, often, more accurate results.

Customer service
KPMG in Canada is helping a business integrate generative AI into their existing customer relationship management (CRM) system for contextual information retrieval. The AI quickly retrieves a customer’s previous interactions and preferences, so a customer service agent can immediately (and appropriately) respond to a query, providing better and faster customer service.

Insurance
Claims processing has traditionally been a time-consuming and labour-intensive task. KPMG in Canada is working with an insurance firm to automate several stages of the claims journey, significantly reducing the time and effort previously required. Typical use cases include the preparation of standard mailings to claimants and engagement letters to external service providers. Not only can they settle claims faster, but they can also combat fraudulent claims by analyzing huge troves of data to detect fraudulent behavior patterns.

The medium-term

In two to three years, we can expect to see more personalized AI services, including the ability to have human-like conversations with chatbots that will be more accurate than human agents. We’ll also get instant access to information, enabling faster problem-solving and decision-making. Most organizations are not there yet, since they’re still getting their data in order. However, if the data you need can be retrieved in seconds, the possibilities for the front, middle, and back-office are endless.

The long-term

How we interact with computers will completely change over the long term. To put this into context, in the 1950s and ’60s, we used punch cards. Eventually we started typing commands onto a green screen, and then we started interacting with a graphical user interface. The technology of the future will understand what you’re trying to do and get you what you need based on natural language—it will be an entirely new “interface” between humans and computers.