12/09/2025
We’ve all heard big promises about AI in the workplace, but many business leaders are asking: when will the hype translate into real added value and ROI?
It’s not that companies aren’t experimenting. According to Gartner’s 2025 AI Hype Cycle report, 78% of enterprises have already integrated or plan to integrate generative AI, and many are also piloting AI agents and other forms of AI in daily operations.
Still, too many initiatives fall flat. Sometimes it simply comes down to unrealistic expectations. In other instances, it’s poorly chosen use cases or inadequate change management. Whatever the causes, the path to value with AI still feels as uncertain as ever.
Especially in fast-changing industries like retail, manufacturing, finance and insurance, AI’s potential is vast, and there’s too much at stake to get it wrong. The question is: where to focus now? From our perspective, digging deeper into these five truths about AI in the workplace can give decision makers a realistic overview and help them focus on what really matters.
1. AI brings clarity in complexity
AI is capable of processing huge amounts of data, delivering actionable insights and reaching clear, logical conclusions much faster than humans.
We’re already seeing banks using AI to reduce false positives in fraud detection, investment firms scanning reports for risks, manufacturers predicting machine failures before they happen, and retailers spotting shifts in customer behaviour. Use cases like these are driving adoption of causal AI, which goes beyond spotting correlations and actually explains why outcomes happen.
The truth is: these tools are useless without high-quality data. If your organisation is exploring use cases, start with areas of the business where your data is reliable and well-governed. That’s where AI can deliver meaningful clarity fastest.
2. AI multiplies skills
Talent gaps are everywhere. Banks can’t find enough compliance specialists. Insurers are short on underwriters. Manufacturers struggle to hire engineers. Retailers need merchandisers who understand both products and data.
AI doesn’t eliminate these shortages, but it helps existing teams do more with less. For example, underwriters can use AI to speed up routine data gathering. Wealth advisors can walk into meetings with AI-prepared briefs. Plant managers can keep machines running with predictive maintenance. Merchandisers can test promotions without hours of manual work.
But if you want AI to multiply your workforce’s skills, you have to start with some upskilling in advance. AI literacy is now as essential as data literacy, yet most firms lack proper training frameworks and even advanced adopters face shortages. If companies want AI to close their skills gaps instead of widening them, they need to make AI literacy a core priority.
3. AI depends on trust at scale
Smart leaders don’t just hand over decisions to a system they can’t explain. For example, banks won’t issue loan approvals they can’t defend, and plant managers won’t trust predictions they can’t verify when it comes to safety-critical equipment.
That’s why most firms are keeping humans in the loop in their AI initiatives. AI takes care of routine tasks, while people step in for the exceptions. Insurers are already using this approach with tasks like motor claims: simple cases are handled automatically, while complex ones go to adjusters. This boosts efficiency without losing accountability.
If you want trust to grow, start by being clear about where AI adds value and where people need to stay in control. Choose systems that explain their outputs. Keep employees in the loop and make sure they know they are still accountable for outcomes.
4. AI delivers efficiency under pressure
Many companies see AI as a much-needed boost to operational efficiency. There are many signs that this is true. We’ve seen companies use it to orchestrate back-office tasks, manufacturers reducing downtime with predictive maintenance, and retailers improving forecasts to avoid overstock and waste.
But efficiency can come at a price. Gartner warns that cloud cost spikes from unpredictable GPU use and retraining are already cutting into ROI. Unlike traditional software, AI comes with ongoing, variable costs that can spiral if left unchecked. Leaders who want real savings need to build cost governance (FinOps for AI) into their adoption plans from the start.
5. AI powers workplace personalisation
People-focused employers understand the importance of giving employees a more personalised work experience so they feel happier, continue learning on the job and get more done. Some companies are already using AI to tailor training, suggest tasks, provide information at the right time and help managers support their teams more effectively.
In financial services, AI agents can prepare compliance and meeting briefs so advisors can focus on clients. On the shop floor, predictive tools help plant managers schedule maintenance and staff more intelligently.
The results so far are mixed to say the least. Fewer than 30% of AI leaders say their CEOs are satisfied with GenAI ROI. The companies seeing real impact focus on embedding AI into existing workflows and making employees’ lives easier. If people can see that an AI tool offers clear benefits in their day-to-day work, they’re more likely to adopt and use it to add value.
Facing the truths head-on
In our experience, over-the-top promises about AI only tell part of the story. What companies need now is the whole truth. That means a realistic assessment of their current capabilities, guidance on where to improve, help identifying high-value use cases and support in aligning workplace AI initiatives with employees’ real pain points.
While it’s never as easy as many AI vendors would like you to believe, the path to AI success doesn’t have to feel so uncertain. Fortunately, we’re gaining new experience and insights every day to help companies decide what works and what doesn’t. Benefit from our expertise. Contact us now to talk about turning your AI initiatives into real business value.
