Beyond The Hype: Where AI Actually Delivers Brand Value

It’s been two years since ChatGPT burst into the public consciousness and sparked huge investments across a sea of technology providers. Despite the headlines, the pitches, and the ‘what to do about AI’ strategy, we're all left with the same question – what's actually working? 

Over the past few months, we’ve been working with a range of leadership teams trying to separate AI's potential from the (expensive) distraction. While some are still chasing the shiny objects, others are quietly building competitive advantages that will reshape categories. The divide between these two groups is widening rapidly, and the consequences for those falling behind will be significant.

The AI Expectation Gap

Nearly every AI implementation begins with hyperbole and ends with a more modest reality. This expectation gap isn't unique to AI – we've seen it with previous technology waves and hype cycles – but the magnitude feels different this time. Only about a quarter of companies working on AI initiatives report significant returned value on their effort. In looking at the failures we’ve seen, three clear themes emerged.

• Tech-led initiatives consistently underdeliver. Projects that began with a specific AI capability looking for a business problem – rather than the reverse – rarely found strategic and market fit internally.

• The payoff horizons were badly misjudged. Teams expected transformative results within months, but the reality is that building genuinely valuable AI capabilities takes a year to 18 months of focused investment – when executed well. There's simply no shortcut to assembling the right data foundations.

• The most ambitious cross-functional AI projects often stalled completely, and often because of the change management complexities completely irrespective of the technology. Without a clear owner accountable for results and with sufficient authority to drive change, these initiatives became orphaned experiments.

In this sense, AI follows the same pattern we've seen over and over. New technologies are almost always initially deployed against existing processes, existing problems, and existing metrics. They become efficiency plays at best. This is where most AI investments remain today – delivering incremental gains that simply don't justify the investment or attention.

In Search of Real Value: Three Areas That Matter

Against this backdrop of disappointment, a smaller set of companies are discovering areas where AI is genuinely changing the game. These areas share a common characteristic – they fundamentally reimagine core business processes rather than simply optimizing them. Let’s look at three areas where the payoff has been most substantial.

• Predictive Demand Sensing. While traditional demand planning looks backward at historical patterns and forward at promotional calendars, AI-powered demand sensing can integrate thousands of dynamic signals – from weather patterns to social media sentiment to competitive pricing – creating a real-time understanding of demand dynamics. Last year, Gartner showed that companies that adopt AI-powered demand sensing achieve 30-50% reductions in forecast error compared to traditional.

• Dynamic Pricing Models. These models adapt to rapid market fluctuations in ways traditional systems can’t. Even before the most recent rounds of global trade induced uncertainty, consumer expectations around pricing were changing dramatically. AI has the potential to help brands model optimal pricing across thousands of SKUs and dozens of channels simultaneously, then adapt in real-time as conditions change. A Bain pricing study from last year found that CPG companies using AI for dynamic pricing achieved revenue increases of 3-5% and margin improvements of 2-7% within the first year of implementation. What makes this different from traditional pricing optimization is the scale, speed and adaptability (this will be a theme we’ll explore in more detail in this series).

• Consumer Insights Acceleration. AI is compressing traditional consumer research cycles -  from months to days or even hours. AI lets brands continuously analyze millions of consumer signals across reviews, social content, search patterns and shopping behavior. The real magic happens when these insights are directly connected to product development and more traditional innovation processes. The Nielsen Innovation Practice documented that brands using AI-powered consumer insights reduce development timelines by 40-60% on average.

Optimization vs. Reimagination

If I could distill the difference between successful and unsuccessful AI implementations into one principle, it would be this: optimization almost always delivers less value than reimagination. When AI is applied to make existing processes incrementally more efficient, the ROI rarely justifies the investment (at least for now). But when AI is used to fundamentally reimagine how a business function operates, the returns can be transformative.

This principle explains why so many "AI for cost reduction" initiatives disappoint. Deloitte's research on AI maturity models found that companies focusing exclusively on cost reduction see returns nearly two-thirds lower than those focusing on value creation and new capabilities. AI delivers real value when used to create new capabilities that simply weren't possible before.

The reimagination mindset also explains why leadership matters so much to AI success. Technical implementation can be delegated, but reimagining core business processes requires senior leaders who understand both the technology's potential and the business model it's being applied to. Without a leadership bridge between the technology and the business domain, AI becomes a solution in search of a problem.

Moving Forward

My advice to leadership teams is straightforward.

1. Be brutally honest about your current AI initiatives – are they genuinely reimagining core processes or merely optimizing around the edges?

2. Focus relentlessly on the few areas where AI can create meaningful competitive advantage, rather than spreading investments too thin.

3. Ensure you have leaders who can bridge the technical and business worlds – these translators are often more valuable than technical specialists.

The window for building AI-powered competitive advantage is still open, but it's closing faster than many realize. The majority of the competitive advantage from AI will be established in the next 24-36 months, and the companies that will win aren't necessarily those with the biggest AI budgets or the most advanced technology. They're the ones with the clearest understanding of where AI can fundamentally change how they compete and the discipline to execute against that vision.

In the next post in this series, we'll look at how AI is reshaping creative development, an area where the challenges are less technical and more cultural.