How AI can supercharge your P&L outcomes

Everyone, from furniture companies in the middle of Iowa to Silicon Valley giants, are trying to make sense of how GenAI will impact their business.

Birju Shah, Ex-Head of Uber AI and Professor of Product Management and AI at Kellogg School of Management, and Harpreet Khurana, Chief Digital and Data Analytics Officer at Russell Reynolds Associates, explain why leaders must reframe their thinking around AI to capture and create value.

A new approach to AI

At the beginning of the current AI hype-cycle, CEOs were expected to simply ‘get on the AI bandwagon,’ without any follow-up questions on its progress or effectiveness. That’s not the case anymore.

The honeymoon phase is over, and we’re moving into the next stage of the AI maturity cycle. In this article, we’re answering the question leaders are asking: where should you deploy AI across the organization and for what purpose?

Too often, the answer to this question is to adopt piecemeal pilots or projects that seek to cram AI into existing strategies and processes. Yet, as the old adage goes: pouring new technology on broken old processes creates expensive broken old processes.

It’s time to reframe this perspective and adopt an enterprise mindset around AI. One way to do that is to put AI in the context of your P&L––and understand how AI can impact your business’s financial health.

Here’s what we mean: every line on your profit and loss sheet can be mined for efficiency and opportunity—key areas that AI can unlock (we’ve illustrated an example in the picture below). When leaders apply a P&L lens to AI, it transforms AI from being a shiny mantlepiece ornament into a value-driving, ‘show-me-the-money’ capability.

There are two levels to putting your AI P&L to work: value capture and value creation.

Value capture with AI

Value capture means extracting more efficiency from your current business processes. And if every P&L line item can be viewed as a set of smaller tasks, then AI can be a potent tool for value capture.

The majority of companies using AI are doing exactly that, using AI to optimize rules-based tasks within their organization—customer service, outbound and inbound marketing, sales, and internal shared service platforms.

For example, a typical wealth manager overseeing $1 billion in assets across 50 to 150 clients can use AI to cover 3-4x the numbers of clients and assets. AI can help tailor highly personalized investment advice, automate portfolio balancing, and provide sophisticated tracking of holdings—a task beyond the reach of a single human.

But using AI just to capture value is just the first step in the journey.

Value creation with AI

AI’s value creation potential is what sets it apart from previous tech leaps. It’s not just able to help you become more efficient, it can help you grow.

Make no mistake: value creation is an existential issue. If your competitors are using AI to find new opportunities, what you have to offer might no longer be relevant.

That’s why we’re already seeing the pharmaceutical industry use generative AI in their research and development processes to shorten drug discovery timescales and transform the efficiency of clinical trials.

According to the Tufts Center for the Study of Drug Development, it takes, on average, 10 years and $1.4 billion in out-of-pocket costs to bring a single drug to market, and about 80% of those costs are associated with clinical development.

But McKinsey research finds that models can help researchers cut drug discovery timelines in half by using generative AI models to complete tasks like analyzing microscopy and pathology data and perform in-silico experiments.

Leaders shouldn’t just look at the costs AI can help them save but at the innovation AI can uncover, too.

How to transform your P&L with AI

GenAI projects are somewhat unique in that they are relatively easy to get started across your organization.  The relative ease can belie the complexity of driving meaningful ROI beyond the early honeymoon phase. The key to successful AI adoption is to approach it strategically rather than trying to implement it all at once.

It’s useful to think about AI applications at three levels. From there you can decide on the outcome-based use cases you want to target against your P&L.

1. Portfolio / Task-level: This is AI as an assistant for small, mundane / no-joy work tasks.

• Purpose: Enhance daily productivity by automating mundane tasks.
• Example: Using AI to summarize meetings or generate checklists, which boosts efficiency without fundamentally changing business processes.
• Impact: Incremental productivity gains that assist existing workflows.

2. Program / Department-level: This is AI as augmentation for human-driven processes.

• Purpose: Create significant value by augmenting human efforts with AI.
• Example: Functional processes could get a facelift with AI augmentation, think marketing departments validating product imagery against product information systems, HR departments rethinking employee onboarding with an always-on onboarding bot, sales reps getting questions answered on sales quotas or commissions.
• Impact: Enhanced value creation through improved processes and outcomes within specific departments. Klarna, for example, says it cut sales and marketing spending by 11% in the first quarter of 2024 by making tasks such as creating images and translating copy more efficient.

3. Platform / System-level: This is AI as an agitator that can transform entire business units.

• Purpose: Transform or reconfigure entire operations by integrating AI at a systemic level.
• Example: AI’s ability to speed up drug discovery using existing clinical datasets.
• Impact: This changes the nature of R&D forever, impacting the process and outcomes of drug discovery. Insilico Medicine, for example, leveraged predictive AI technology to bring a drug candidate for idiopathic pulmonary fibrosis (IPF) to the threshold of Phase II development in just 18 months.

By adopting a surgical approach to AI, leaders can effectively transform their P&L, driving both immediate and long-term benefits.

Authors

Birju Shah, Ex-Head of Uber AI and Professor of Product Management and AI at Kellogg School of Management.
Harpreet Khurana, Chief Digital and Data Analytics Officer at Russell Reynolds Associates.