#322 Amanda Luther: The Widening AI Value Gap (Inside BCG's AI Research)
Episode
54 min
Read time
2 min
Topics
Relationships, Investing, Startups
AI-Generated Summary
Key Takeaways
- ✓AI Maturity Distribution: BCG's study segments companies into four tiers: 60% are laggards or emerging, 35% are scaling with pockets of value, and just 5% are "future built" with AI impact visible across EBIT margins, revenue growth, and total shareholder return. This top tier skews toward digitally native companies but includes century-old firms actively reinventing themselves.
- ✓Investment Gap: AI leaders spend roughly double what laggards spend on AI as a share of IT budget, though the average global IT budget allocated to AI remains around 5%. More telling, leaders invest six times more in employee training and upskilling than laggards do, with that investment split roughly equally between technology platforms and people-side costs.
- ✓Value Source Breakdown: Approximately 70% of AI-driven value among leaders comes from core business functions — sales, marketing, procurement, and supply chain — rather than corporate functions like HR or finance. Agentic systems currently account for 17% of that value but are projected to reach 30% within three years as orchestration capabilities mature across industries.
- ✓Agentic Implementation Reality: Effective agent deployments share three traits: humans remain in the loop for final decisions, workflow design starts from a zero-based process redesign rather than automating existing steps, and agents receive explicit context including objective functions, organizational data, and defined guardrails. Only 11% of organizations build agents entirely in-house; most use hybrid approaches combining point solutions and hyperscaler partnerships.
- ✓Laggard Strategy: Companies not yet generating AI value should resist cataloguing hundreds of use cases and instead identify one or two areas fundamental to their core strategy, then assign a cross-functional team exclusively to those. A fast-follower approach — monitoring what works in adjacent industries before committing — remains viable, but complete inaction creates compounding cost and revenue disadvantages that become structurally difficult to reverse.
What It Covers
BCG Senior Partner Amanda Luther presents findings from an annual AI maturity study tracking 1,000–1,500 companies across 41 capability dimensions. Only 5% of companies qualify as AI leaders generating measurable P&L impact, while a widening value gap separates them from the 60% still classified as laggards or emerging adopters.
Key Questions Answered
- •AI Maturity Distribution: BCG's study segments companies into four tiers: 60% are laggards or emerging, 35% are scaling with pockets of value, and just 5% are "future built" with AI impact visible across EBIT margins, revenue growth, and total shareholder return. This top tier skews toward digitally native companies but includes century-old firms actively reinventing themselves.
- •Investment Gap: AI leaders spend roughly double what laggards spend on AI as a share of IT budget, though the average global IT budget allocated to AI remains around 5%. More telling, leaders invest six times more in employee training and upskilling than laggards do, with that investment split roughly equally between technology platforms and people-side costs.
- •Value Source Breakdown: Approximately 70% of AI-driven value among leaders comes from core business functions — sales, marketing, procurement, and supply chain — rather than corporate functions like HR or finance. Agentic systems currently account for 17% of that value but are projected to reach 30% within three years as orchestration capabilities mature across industries.
- •Agentic Implementation Reality: Effective agent deployments share three traits: humans remain in the loop for final decisions, workflow design starts from a zero-based process redesign rather than automating existing steps, and agents receive explicit context including objective functions, organizational data, and defined guardrails. Only 11% of organizations build agents entirely in-house; most use hybrid approaches combining point solutions and hyperscaler partnerships.
- •Laggard Strategy: Companies not yet generating AI value should resist cataloguing hundreds of use cases and instead identify one or two areas fundamental to their core strategy, then assign a cross-functional team exclusively to those. A fast-follower approach — monitoring what works in adjacent industries before committing — remains viable, but complete inaction creates compounding cost and revenue disadvantages that become structurally difficult to reverse.
Notable Moment
Luther notes that despite widely available AI solutions for customer service, most consumers still navigate phone trees pressing numbered options. She expresses candid surprise at how slowly proven, commercially available AI tools penetrate even the most obvious enterprise use cases across the broader economy.
Episode Transcript
We have about 60% that are either laggards or emerging. They're not getting a lot of value out of AI or or maybe not even trying to. Where do you see agents being applied? What part of the business? Surprise myself at how slow the adoption is better. And I'll just take an example that we were all familiar with. It's customer service. I mean, I'm still pressing one for this or two for that on my phone. What is truly new in agentic versus what is something that a company has been could have done two years ago and is just defining as Agentic because that's the the nice term du jour. Amanda Luther. I'm a senior partner with BCG. I've been with the firm for eighteen years now. I lead our AI transformation practice globally and get to see a lot of what's going on across industries. And as part of that role, one of the fun things that I get to do is, co lead our study that we do every year on the impact of AI across, you know, somewhere around a thousand to 1,500 companies every year. I've been leading that for the last four years. I mean, the study's been going for the last eight or nine. And so we've got really great longitudinal data on what it takes to be successful in AI and how that has changed, you know, over the course of time as well. And this year's survey then and the report that followed is about the widening value gap between early adopters and laggards in the AI space. Is that right? That's exactly right. And that's that's what we ended up titling the report this year because it was one of the most interesting findings as we got into the data. You know, one of the hypotheses that we had coming in was that maybe this gap would be widening because you've, you know, started to create, the base of capabilities to build from. And what we've seen is that's really borne out in the data. You know, the companies that over the over time were investing to begin with got value from that. That value flowed through into the p and l. They reinvested part of that value into additional tech and AI investments, and now they're getting more value from doing that. And so they're really in this virtuous circle of of value generation from AI. Yeah. I have some of the metrics here, to talk about. But, generally, the companies that are getting value I mean, we'll talk about the metrics. But, what what areas are they getting value in, and how significant is the value, on the bottom line? It's pretty impressive value. So maybe I'll start with where are they getting it. Yeah. Where they're getting it is primarily from, you know, the core business functions. And that looks a little different by every sector in industry, but it's really around sales, marketing, you …
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