Here we outline three organizational constraints that arise when executive teams struggle to let go of the past. We also provide examples of how you can employ AI to renew your organization’s practices for an improved competitive future and use AI to make an objective case for change.

PERFORMANCE METRICS THAT DISTORT PRIORITIES

Clinging to obsolete metrics leads companies to misallocate resources, reward the wrong behaviors and miss up-to-date performance drivers that lead to organizational success.

Take a national U.K. retail chain we studied — we’ll call it Whitford & Co. It’s a member-owned consumer cooperative best known for its food retail stores. Its sales were stagnating, margins were tightening and customer churn was rising. Out of habit, staff clung to legacy KPIs that they’d become accustomed to.

How AI helped: Metrics overhaul initiative

A core stumbling block in an organizational forgetting program is complexity. To expose which of its legacy metrics were relevant and which ones weren’t, Whitford & Co deployed two AI-powered analytics platforms to do what human analysts could not, i.e., simultaneously interrogate customer transaction data, digital interaction logs and operational records.

AI processed thousands of data points which exposed that many of the most emphasized metrics had no strong correlation with either customer retention or business profitability.

Over three months, the retail division successfully retired seven of its 12 legacy KPIs, including in-store sales conversion and footfall-to-inquiry ratios. In their place the company adopted behaviorally relevant indicators, such as multichannel path completion rates and customer effort scores.

BUSINESS IDENTITIES THAT CONFUSE THE MARKET

Corporate identities and brand positioning that were once assets can become liabilities. Take the U.S.-based software company we studied, which we’ll call FengSys. This company builds data integration and analytics platforms for public and private sector customers. It built its brand around being “data-first” in the early 2010s.

Over time, though, client expectations changed. Clients were no longer impressed by “data-first.” They had come to take data capability for granted and were now seeking partners who could demonstrate business outcomes — faster decisions, lower operational risk and measurable return on technology investment.

How AI helped: Strategic language purge

FengSys’s organizational forgetting problem required AI to first recognize patterns, specifically, the hidden maze of contradiction embedded across thousands of documents that no human reviewer could reliably detect on budget and at scale.

As internal staff were too close to the company’s history to see the incoherence clearly, the company worked with an external boutique consultancy. This firm deployed a GPT-4-based model configured specifically around the company’s own document library, to scan thousands of pages of sales decks, proposals and client communications.

The result? As Leonor, the vice president of marketing explained: “This gave us an objective, evidence-based picture of the problem that no internal advocate could dispute or deflect.”

CUSTOMERS MYTHS THAT WARP STRATEGY

A global financial services firm we studied that we’ll call SuboBank clung to the belief that “older customers avoid mobile banking.”

Even though external data suggested that retirees now constitute one of the fastest-growing groups of mobile app users, the belief that they were technological laggards was hard to shake. SuboBank worked with a single entrenched assumption, that had never been systematically tested against real customer behavior.

How AI helped: Myth-busting through behavioral evidence

To unearth this embedded belief, management configured IBM Watson Analytics to ingest and cross-reference three live data streams simultaneously: 1) in-app behavioral logs tracking how customers of all ages actually used SuboBank’s mobile platform; 2) customer-sentiment data drawn from support interactions, survey responses and complaint records; and 3) external demographic data on mobile adoption trends among customers aged 60 and over.

Wherever actual behavior contradicted what the documentation assumed, the system flagged it. As managers and senior leaders reviewed the AI-generated behavioral reports, they made their own judgments about which design changes to prioritize.

Color plays a powerful role in branding, marketing and communication — and a logo is often where that power shows up first. Logo colors are more than design details. They’re strategic tools used to influence how customers feel about, respond to and even remember a brand. The right palette can reinforce a brand’s identity, signal its values and create instant emotional connections. Think red to spark excitement, purple to convey sophistication, or green to suggest eco-friendliness.

These popular associations are often reinforced by color symbolism charts and guides designed to help businesses, brands and marketing teams choose the best color for their logos.

Over time, these tools have become popular resources for brands looking to create or perfect their logo design. But our recent research offers a cautionary tale: Relying on these tools too heavily can actually distort a brand’s message, primarily because they overlook two factors that shape how customers perceive colors.

COLORS EVOKE BOTH POSITIVE AND NEGATIVE MEANINGS. For example, blue can evoke trust and calm, but also sadness and fear. Green is often associated with growth and health, yet it can also suggest toxicity and monstrosity.

COLORS ARE INTERPRETED WITHIN THE BROADER CONTEXT IN WHICH THEY APPEAR. Context, background, and the overall design concept shape how a color is going to be perceived. Whether a color communicates something positive or negative depends on how it interacts with the larger visual, especially the background color against which it is placed.

To help brands make better decisions about their logo designs, we explored these two key areas in our research.

HERE’S WHAT WE FOUND:

1. BACKGROUND COLOR MATTERS.

Across our studies, we found that commonly used logo colors can take on both positive and negative meanings. A consumer’s perception depends not just on the logo color, but on whether the logo is sitting on a white or black background. In general, logos on black backgrounds elicited emotions and associations that are traditionally viewed as more “negative” (e.g., aggressiveness, danger, eeriness, sadness, toxicity). White backgrounds, conversely, triggered emotions and associations seen as more “positive” (e.g., fun, kindness, liveliness, optimism, trust).

2. NOT ALL LOGOS ARE AFFECTED EQUALLY.

Our research found that a logo’s design style — specifically whether it was abstract or representative — played a meaningful role in how strongly those shifts appeared.

We found that when a logo was representative, participants were more focused on the meaningful idea or concepts it depicted, making color less central to their interpretation of the logo. In contrast, when a logo was abstract, participants couldn’t immediately interpret its meaning, making color the most salient, and often only, understandable characteristic.

WHAT SHOULD YOUR BRAND DO?

To decide which direction is right for your business’s brand, start by asking: How do we want consumers to experience our product or service? What personality traits should come through?

If your brand aims to evoke bold or traditionally “negative” emotions, consider using a black background. On the other hand, if your goal is to create “positive” impressions use a white background.

Second, regardless of whether your brand is well-known or not, we recommend creating clear guidelines for how color is used in your visual identity. A brand guide can help you define when and why to use each version of your logo, based on the message you want to convey.

Color symbolism charts and guides are a helpful starting point, but they don’t capture the complexity of how color actually works in branding.