Lean Strategy & Applied AI: Cutting through the Noise.

June 12th, 2025

Everyone’s justifyingly racing to “do AI” — but an important question is often overlooked in the scramble:

To what end?

It’s no longer a question whether a company should invest in artificial intelligence - it has undoubtedly arrived and embedded itself in the minds of all who lead and work in modern organizations. But in the sprint to act, many skip the most strategic step: Thoughtfully connecting those efforts to real value streams, real friction, and real experiences customers care about.

Let’s be clear: AI is not a strategy. It’s a capability.

Without anchoring that capability to your organization’s most pressing problems…and aptly bringing transparency and prioritization to the matter…what develops is something resembling organizational chaos — no matter how powerful the algorithm. Results? —> Experimenting for the sake of claiming experimentation. Build or buy decisions for model development get bogged down or worse yet, never discussed. The uninitiated believe AI-vendor pitches and start questioning and interjecting themselves into internal AI work.

So what is Applied AI in practice?

Applied AI refers to the use of AI techniques such as machine learning, natural language processing, and predictive analytics in targeted, domain-specific applications that solve concrete business problems.

“Applied AI bridges the gap between abstract algorithms and real-world use cases by aligning data science to business context and decision-making.”

— Ransbotham et al., MIT Sloan Management Review, 2023

While large foundational models (like GPT-4 or domain-trained LLMs) are essential enablers, value is unlocked only when those capabilities are embedded into specific workflows — automating claims processing, predicting customer churn, enriching product data, surfacing customer search intent, or accelerating customer support.

It’s not about choosing between foundational models or applied AI. It’s about building the bridge between them with strategy as the architect.

The Strategic Disconnect

Most companies remain stuck in experimentation rather than capturing real value from AI:

• BCG (Oct 2024) reports that 74% of companies are piloting AI but only 4% generate substantial enterprise value, with 22% showing early but limited gains. The AI leaders seem to have narrowed their focus to a limited set of key business functions, i.e., linked the focus to a more broader company strategy and targeted list of functions.

• The Wall Street Journal (Apr 26, 2025) highlights a stark reality: While 78% of companies deploy AI in at least one function, only 1% have scaled AI enterprise-wide, and most report cost savings under 10% and revenue gains below 5%.

Clearly, there’s value creation through AI but at this stage, companies are continuing to work their way through determining what AI’s real value is to their business. As the hype cycle continues, there will undoubtedly be companies that hit the “trough of disillusionment” with a harder thump than others - I contend those will all likely have one thing in common: No focused strategy.

Key takeaway:

“AI without strategic anchoring is noise — pilots don’t pay the bills. Only when AI is aligned to decision‑making, friction‑points, and operational workflows does it deliver measurable ROI.”

That’s why, in the BANKDENS approach, strategy leads and AI adapts to fit — not the other way around.

The BANKDENS Approach

We’ve built a framework to fix this. Our Lean Strategy Diagnostics apply classical structured problem-solving to today’s most complex opportunities - AI included.

Here’s what sets it apart:

• We start with friction, not features

• We engage decision-makers across silos to ensure alignment

• We move quickly to synthesize insights into executable plans

• And we anchor innovation, AI or otherwise, to strategic business relevance, not hype

This isn’t a traditional AI readiness assessment. It’s a strategy-first approach that clarifies what’s working, what’s missing, and where emerging capabilities, like AI, can actually drive value.

Closing Thought

If your AI initiative doesn’t map to defined business friction or measurable strategic goals, you’re not investing — you’re gambling. While an “AI-first” mindset has taken hold in modern companies, it’s a mistake, never mind expensive, to spin up cycles of experimentation just because AI “can probably handle it”. That’s not to say there is no place for experimentation. The point is like any capability, AI needs to map to a larger purpose and value and the experiments inform that plan.

Case in point: I’ve witnessed organizations prioritize tests and investments in AI-generated product review summaries but not basic product data quality capabilities (AI-powered or not) that could have prevented over 30% of negative reviews, never mind reclaimed millions in lost sales from customers exiting online product pages entirely on account of inconsistent product data.

When everyone is moving at the speed of business, that’s exactly where a strategy-first approach is unifying.

I’d love to help you make sure that type of signal cuts through the noise and the shine.

Connect with us to run a diagnostic sprint and pressure-test where your strategy meets capability.