Complacency in the age of AI. What does it really mean?

Complacency in the age of AI shows up when leaders can feel the shift happening, but still move as if there is plenty of time. The business is running. Customers are still buying. Teams are hitting targets. And because nothing looks broken, it is easy to tell yourself you will deal with AI “properly” later.

That’s how the delay begins. AI gets mentioned in meetings and strategy decks, but day-to-day work stays the same. Organisations keep watching and maybe exploring with ChatGPT or Gemini. The core of the business, however, continues exactly as it did last year.

The problem is that AI does not move on the same timeline as quarter planning and training calendars. It improves through constant use. The companies that are learning fastest are the ones putting it into real workflows, seeing what breaks, fixing it, and trying again.

And over time, a gap opens up. A gap between the companies building capability through action and the ones still trying to decide when the “right time” is. Or the ones deciding which consulting firm to hire to bring in the best strategies! By the time urgency becomes obvious, others have already climbed the learning curve.

Which companies are being complacent, and how does that show up?

One of the clearest signals of complacency is not what companies claim to be doing, but what they are being pressed on.

Earnings calls are where executives speak to analysts and investors about what is actually driving performance and what will shape the business next. If something is truly material, analysts tend to ask about it, repeatedly.

Yet in 2025, Bloomberg analysed earnings call transcripts across the S&P 500 and found that fewer than half of non-tech companies were asked about generative AI at all. That is a striking gap, given how dominant AI has been in headlines and boardroom conversations.

This does not mean companies are unaware. Many have pilots, task forces, and internal experiments. But it does suggest something else: for a large part of the market, AI is still not showing up as visible performance impact.

Reuters reinforced a similar point through surveys, showing only a small portion of executives reporting meaningful margin improvement from AI, with many organisations delaying or scaling back spending plans. In professional services like law, hesitation remains high because accuracy and confidentiality matter, and leaders worry about consequences when tools get things wrong.

Sometimes, early experiments have also made companies more cautious. McDonald’s pausing its AI drive-through trial after public mistakes is a good example. When pilots fail loudly, the lesson many organisations take is “wait,” not “learn.”

So this is what complacency looks like in practice: AI is treated as important, but not urgent. It lives around the edges. It does not yet sit inside the workflows that shape costs, productivity, or customer experience. And as long as results stay fuzzy, urgency stays low.

Which companies are using AI, and what outcomes are they seeing?

The picture changes when AI is tied to real work, not just in a training classroom.

Across retail, consumer goods, manufacturing, and automotive, some non-tech companies have already pushed AI into everyday tasks, and that is where results start to show.

Mattel gave its designers Adobe Firefly to support packaging and product design work. It helped teams move faster on ideation and cut down time spent on repetitive design tasks by 50%!

CarMax adopted ChatGPT early to organise vehicle feature data and summarise thousands of customer reviews. They described it as removing years of manual work. Not weeks, YEARS! They then built internal AI assistants: one that helps employees interpret policies and regulations using company data, and another that supports customers through the car-buying journey.

Retailers have also started embedding AI where it touches customers and staff. Walmart launched Sparky inside its app to help customers compare items, summarise reviews, and plan purchases. Target rolled out a store assistant chatbot to nearly 2,000 stores to support frontline staff with process questions and onboarding.

What’s worth noticing is how practical these use cases are. Nobody started with “let’s do AI.” They started with friction: time lost, complexity at scale, repetitive work, and customer decisions that take too long. AI simply became the tool used to remove that friction.

And once AI sits inside real workflows, something else happens. Leaders can see the impact. Teams learn through use. Confidence grows because the outcomes are visible. That is when AI stops being a future conversation and becomes part of the present.

Future-proofing talent, not technology

By the time you read these examples, it becomes hard to ignore the real question. It is not “Do we have access to AI?” Everyone does. The question is whether your people know how to spot where AI can help, and whether they have the support to build and deploy solutions that stick.

This is why future-proofing is less about running more training and more about building a repeatable system. Many people attend workshops, understand the basics, and still struggle to apply them in the messiness of real work. Ideas get stuck in discussion. Experiments start but never get traction. Learning accumulates, but capability stays thin.

At Leadapreneur, future-proofing is built around execution. We develop people as leadapreneurs, autonomous innovators who can take ownership of real business problems, not wait for perfect clarity. Then we run them through a structured accelerator called the Greatness Games, where they build real projects tied to business priorities: productivity, cost, risk, revenue, and profit.

Projects are not left as slides. They are tested, refined, and measured, with the aim of being deployed inside the organisation under our guidance.

We’ve seen this approach play out across different industries with organisations we have worked with:

• At DBS, one relationship manager built an AI-enabled marketplace for wealth trade ideas, reducing sourcing time and unlocking millions in innovation value.
  
  

• At Toshiba, one participant designed a direct-to-customer commerce solution that reduced marketplace dependency and improved profit control.
  
  

• At AMK, one credit officer prototyped an AI-powered loan assessment process that reduced manual work, lowered risk, and saved thousands of hours.

These outcomes (and hundreds more like these) did not happen because the organisations bought new tools. They came from people being supported to use AI in the context of their work, guided by a disciplined process designed by Leadapreneur, and held accountable for real results.

At Leadapreneur, our mission is to future-proof organisations by developing talent that can identify real business problems, see where AI can help, and bring those solutions into everyday workflows. The point is not AI for AI’s sake. The point is building capability that compounds.

If you are ready to move beyond observation and start building this capability inside your organisation, we would love to have that conversation.

Written by Hanaa Maysoon
COO Notes