AI Gold Rush Triggers Executive Anxiety

AI Gold Rush Triggers Executive Anxiety
May 21, 2024

The Dizzying Directive on AI Spending

When it comes to determining where and how much to invest in artificial intelligence (AI) technology, executives are facing mixed and confusing messages. On one hand, the potential opportunities enabled by AI are projected to be massive, with one estimate by McKinsey & Company suggesting generative AI alone could add $4 trillion annually to global corporate profits. However, in an emerging technology realm, it's also easy to waste money by betting prematurely on the wrong tools or applications.

This uncertainty leaves many CEOs and CFOs torn between FOMO (fear of missing out) and FOFU (fear of failing utterly) regarding AI spending decisions. Should they be first movers who plunge aggressively into adopting AI to gain a competitive edge? Or cautious followers who risk falling behind by waiting too long?

Leveraging Consultancy Expertise

To help clarify AI investment strategies, many companies are turning to major consultancy firms who have accumulated extensive hands-on experience guiding clients on AI implementation. Having worked with hundreds of organizations testing different AI approaches, these consultancies can provide invaluable perspective on what's working well in practice - and what's not.

For example, Accenture plans to double its workforce focused on AI to 800,000 employees over the next three years to meet surging client demand. By leveraging insights across their client base, these consultancies aim to help clients spend smartly on the highest potential AI applications for their industry and business model. We spoke with experts from Accenture, McKinsey and BCG to learn what guidance they're providing clients grappling with AI investment decisions and early AI pilots.

Drilling Down on High-Value AI Opportunities

Alex Singla, Senior Partner and co-leader of McKinsey's QuantumBlack AI unit, notes his team first examines the core business problems a client is looking to address. Only then do they evaluate whether and how AI solutions can effectively help solve those challenges. This approach grounds AI recommendations in tangible real-world impact, rather than technology for its own sake.

Industry sector and geographic footprint also factor into AI opportunity assessments, with Singla advising clients "you want to be ahead of the curve on the faster ones." Additionally, existing technology infrastructure should be reviewed upfront, such as cloud platforms and data architecture, to ensure AI tools can be successfully integrated and scaled.

Recent customer inquiries range from leveraging generative AI to improve chatbots to transforming long-term customer interactions. But clients first need an overarching AI strategy tied to measurable business KPIs.

To capture value from AI investments, McKinsey focuses clients on one of three pathways: 1) Improving efficiency to reduce headcount; 2) Enhancing productivity so fewer new hires are required over time; or 3) Boosting sales performance. Without planning upfront how AI will directly impact financials, purported innovations often fail to deliver hard cost savings or revenue gains.

For example, Singla highlights Microsoft's Copilot software which helps developers write code more efficiently. Here measurable results are being achieved, with productivity improvements from 20-40% realized on software modernization projects. This efficiency unlocks headcount savings over time by enabling faster coding with existing developer staff.

In the insurance industry where Singla spends most his time, client engagements run the gamut from reimagining claims processing to overhauling the policy purchasing experience. But again, the focus lies in tying AI enhancements to metrics like handling more claims per adjuster. Otherwise, purported innovations never create positive P&L impacts.

Assessing the Appropriate AI Investment Horizon

Paul Daugherty, Accenture's Chief Technology and Innovation Officer, notes clients broadly fall into three categories regarding AI timelines. Some companies are proactively exploring the "why" - specific business issues or processes ripe for AI improvement. Others ask "when" they should start investing in AI, seeking insights on ideal timing.

And finally, a portion of clients experience FOMO seeing competitors pursue AI pilots and fear being left out. These organizations pressure consultants to inform them what cutting-edge use cases seem hot in their industry.

Daugherty stresses that investing in AI should commence with a crisp business case analysis. This includes weighing value creation opportunities against implementation costs across relevant parts of the client's value chain. High potential AI applications should then be sequenced first where clear ROI can be demonstrated at scale.

He advises generative AI investments specifically should be overseen as part of a governance framework. This entails alignment at the C-suite level on overarching objectives, with consistent guidelines in place guarding against fragmented AI efforts squandering resources.

Since generative AI remains an emerging arena, Daugherty further underscores architecting flexibility into any initiatives. As rapid advances continuously enhance system capabilities, future-proofing initial deployments is critical to avoiding costly rip-and-replace scenarios.

Implementing Early Generative AI Wins

To illustrate relatively straightforward generative AI adoption, Daugherty points to streamlining HR processes like automating job description creation. More transformative opportunities arise in industries like pharmaceuticals, where reducing new drug development timelines from 7 years closer to 3 years would profoundly impact cost structures and profit trajectories.

Sharing a client success story, Daugherty outlines how one telecom company improved customer request handling by 30% using generative AI to analyze inquiry patterns and discern underlying subscriber needs more accurately. This single use case also boosted customer satisfaction by over 60% - showcasing hard metrics on performance gains achievable today.

Clarifying the State of Generative AI Technology

Sesh Iyer, Managing Director and Senior Partner at Boston Consulting Group (BCG), notes CEOs and their leadership teams frequently request briefings on exactly how generative AI differs from previous AI approaches. Unpacking concepts like large language models helps demystify the technologies involved to aid planning and prioritization.

Iyer's team actively works with clients to map out "reference stacks" clarifying required generative AI capabilities, available platforms offering those features and typical integration considerations. This enables organizations to pinpoint tools meeting their needs among an explosion of startups and open source options in the space.

For companies already utilizing solutions like GitHub, Microsoft 365 or Salesforce with embedded generative AI components, Iyer suggests experimenting with these modules as an easy first step. Additional possibilities to generate near-term benefits center on applying generative AI to horizontal functions like customer service, finance and HR.

Achieving Generative AI Productivity Leaps

Citing client appetite to boost customer experience quality while aggressively reducing costs, Iyer highlights generative AI’s potential to drive 50% or higher productivity jumps in areas like call centers. With the right mix of virtual agents and human-in-the-loop guidance tools, average handle times plummet. This frees up human agents to focus on the most complex dialogues dependent on emotional intelligence and empathy.

But with Microsoft, Google and startups rapidly iterating their offerings, Iyer notes most clients will need to actively establish responsible AI guidelines around issues of transparency, bias detection and data handling. If governance considerations lag integration efforts, organizations risk provoking public backlash over generative AI systems that appear carelessly or recklessly deployed amidst their rush to stay competitive.

Key Takeaways for Navigating Generative AI

Despite pronounced excitement around generative AI’s possibilities, our consultancy experts emphasized that investments should still align tightly to value creation and business impact. While some FOMO exists, objective analysis is required to identify the ripest AI opportunities across individual companies' operations and market landscapes.

Proper governance also remains paramount as this technology interacts more fluidly with end users and customers. But organizations recognizing they can start small with bite-sized generative AI adoption in existing software assets or horizontal processes stand to pull ahead competitively. In today's uncertain economic climate, this pragmatism and restraint holds the key to maximizing returns on AI expenditures that will soon become compulsory table stakes across entire industries.

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