AI: The Gains, the Gaps, and the Hidden Costs

AI feels like a gift to business right now. Faster processes. Sharper forecasts. Leaner operations. New offerings. The promise of greater productivity is driving a rush to adopt. Leaders are under pressure to move quickly, and many are hiring AI talent or standing up transformation teams to show results.

I will say it upfront: I am an AI fan. I use it daily and cannot imagine working without it. But precisely because I use it so much, I know the excitement needs to be balanced with clear eyes. Productivity gains don’t happen in isolation. Every gain creates ripple effects across costs, risks, reputation, and resilience.

At the same time, when other CEOs move forward, leaders also recognise they cannot do nothing. The pressure not to be last is real, and waiting carries its own risks.

If you don’t look around corners, today’s wins can quickly turn into tomorrow’s problems.

AI Adoption Stories from the Frontline

• The supply chain paradox: efficiency up, resilience down. A global manufacturer optimised its supply chain with AI and hit record efficiency targets. Yet the “human” side of the role was stripped back to exception handling and anomaly-spotting. Over time, people got bored, turnover rose and resilience fell. Efficiency on paper became fragility in practice.
  

• The chatbot that broke the rules: millions wasted. A city government launched an AI chatbot to guide citizens. Instead, it advised people to break the law. The backlash was immediate, the headlines brutal, and the program was pulled down despite millions already invested.
  
  
• Automation and headcount savings that never landed. A bank introduced voice AI and announced 45 redundancies. On paper it looked neat. In reality, those tasks were scattered across roles. The union intervened, the bank quietly rehired, and overtime spiked. The savings never materialised, and credibility took the hit.
  
  
• The offshoring boomerang: savings wiped out by salaries. One company offshored, then automated its back office. The model looked cost-efficient until they had to hire AI engineers and data scientists to keep the systems running. With salaries for these roles climbing sharply, the savings vanished almost overnight.
  
  
• Cost-cutting that broke customer trust. A fintech announced that automation meant they had avoided hiring hundreds of service staff. Customers quickly noticed the drop in quality. Trust scores collapsed, complaints surged, and the company had to roll back. The efficiency win looked clever in the boardroom but damaging in the market.
  
  
• The reputational sting: layoffs and a share price dip. A large technology company cut 150 support roles under the banner of efficiency. The market read it as a signal of distress. The share price dropped by almost three percent and investors questioned the strategy.
  
  
• Recruitment efficiency that increased hiring pain. Another company automated recruitment to speed up time-to-hire. Candidates did move faster through the funnel, but reneges spiked. Without a human relationship in the process, candidates had no sunk cost and walked away when a better offer came along. Speed at the top of the funnel created cost at the bottom.
  
  

The Transformation Leader’s Checklist

If you’re leading AI adoption, whether you have just hired a Head of AI, built a transformation team, or are scaling capability, these are the questions that keep the best leaders out of the headlines:

1. Where are our biggest opportunities? Which tasks, workflow or capabilities create the most value if automated or augmented?
   
   
2. Is the work left behind still resilient, or are we hollowing out roles into brittle fragments? How are we preparing our people for the future when their jobs might change or disappear?
   
   
3. Are the savings real, or spread too thin across tasks to add up to meaningful reductions?
   
   
4. What are the second-order costs? Have we factored in vendor reliance, infrastructure, or scarce AI talent with rising salary demands?
   
   
5. Could this trigger backlash? Who might misinterpret the intent: employees, unions, customers, regulators, or the media?
   
   
6. How are we framing it? Is AI positioned as cutting costs, which feels like a threat, or freeing capacity, which feels like an opportunity?
   
   
7. What happens if the system fails? Do we still have people with the skills to step in?
   
   
8. Are we chasing speed at the expense of stickiness? In hiring and customer experience, are we trading efficiency for loyalty?
   

Regulation is Coming Fast

AI adoption is no longer just about productivity and ROI. It is also becoming a compliance issue. The EU AI Act, US scrutiny of AI in hiring and lending, and Australian moves to fold AI into privacy and consumer law all point in one direction. Bad AI is not only a reputational problem. It can also be a legal breach.

For leaders, the questions are simple. Can you evidence how AI decisions are made? Can you show regulators, investors, and customers that your systems are accurate, fair, and transparent? And are you confident your new AI hires know how to navigate this regulatory landscape? Those who answer yes will build trust and unlock approvals more easily. Those who don’t risk fines, rollbacks, and loss of credibility.

Why This Matters

AI is moving fast. The question is not whether to deploy it. The real question is whether your business can capture the upside while managing the risks, costs, and credibility issues that come with it.

That is why we built our Impact of Automation Assessment. It gives business, finance, HR, and transformation leaders rapid clarity, tailored to their context:

• Where the biggest automation opportunities sit, based on data about what people are doing today.
  
  
• How roles and costs evolve across two, three, and five years.
  
  
• Which second-order risks, including skills, resilience, reputation, and regulation, may surface.
  
  
• How to frame AI so employees, customers, and boards can support it.
  
  

It is not “quick and cheap” and it is not a six-month consulting slog. It is a repeatable model, designed to be fast to insight and grounded in your reality.

The companies that win won’t just be the first to hire AI leaders or deploy AI at scale. They’ll be the ones who pick the right opportunities, manage the risks, and preserve trust, resilience, and ROI long after the initial excitement fades.

Want to know more? Book a chat. What have you got to lose?

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To read more content in this space, why not check out;

• The Ripple Effect: How AI Adoption by One CEO Impacts all of the Rest
• The AI Roadmap Had 32 Slides. The People Strategy Had Four Lines
• Jobs Are Disappearing - And I'm Worried We’re Not Prepared

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