AI Will Change How You Work—If You Let It
- Mladen Tošić
- Apr 15
- 3 min read
We’re past the hype. AI is already reshaping industries, customer expectations, and how value is created. But while many leadership teams are focused on tools and use cases, the more pressing challenge is internal: is your business ready to work differently?
Just like the wave of digital transformation a decade ago, adopting AI will expose fault lines in your culture, your decision-making, and your operating model. And this time, the changes may run even deeper.
1. From Facetime to Outcomes (and Commercial Models)
AI thrives in results-oriented environments. But many businesses still reward visibility, effort, and time—rather than outcomes. That extends beyond culture into commercial models. If you’re still billing by the hour, you may be incentivizing the wrong behaviors. True readiness means putting outcomes at the center of recognition, resourcing, and how value is priced.
2. From Fear of Failure to Ownership Through Action
The most powerful AI use cases often start small—and fail fast. And they fail with purpose. Cultures that treat failure as evidence of initiative and ownership (rather than incompetence) move faster, learn more, and unlock new value. That shift starts with leadership explicitly rewarding test-and-learn behaviors.
3. From Rigid Processes to Agile Loops
AI doesn’t fit neatly into annual planning cycles or traditional delivery models. What’s needed is a capability and mindset for fast iteration. That means empowering cross-functional teams to test ideas quickly, learn from feedback, and adapt without waiting for committee approvals.
DBS Bank is a standout example here. To prepare for AI and broader digital innovation, they restructured the entire organisation around agile squads and empowered teams.
📖 As covered in Harvard Business Review, their CEO described the shift as moving from a traditional bank to a tech company in mindset.
In one project, we helped a European consumer health company build this muscle during a digital marketing transformation. Beyond setting up agile processes, we coached cross-functional teams on how to work in faster cycles—and helped leadership shift incentives to reward learning and speed, not just short-term ROI.
4. From Centralised Expertise to Organisational Learning
Yes, AI specialists are essential. But true value comes when AI is embedded in how everyday teams solve problems. That requires upskilling, cross-functional collaboration, and—perhaps most importantly—a culture where continuous learning is expected and celebrated.
Schneider Electric did just that. As part of its digital strategy, it built internal AI hubs and launched an AI Academy to upskill employees.
📖 MIT Sloan Management Review details how embedding AI expertise directly into business units accelerated real-world decision-making.
5. From Technology-Led to Purpose-Led
Your AI roadmap shouldn’t start with “what can the tech do?” but “what problems are worth solving?” The best use cases are those grounded in real customer or operational pain points, where AI can accelerate progress or elevate impact.
The Litmus Test
Here’s a simple question I ask clients:
How easy—and how comfortable—would it be for your teams to test AI internally or with clients today?
If the answer is “not very,” chances are it’s not just about tools. Something in your culture, ways of working, or leadership behaviors is getting in the way.
Leaders in talent, transformation, and strategy should be asking:
Are teams encouraged to experiment—even if things don’t work?
Do we have fast, cross-functional ways of solving problems?
Are we recognising learning, or only celebrating success?
Do we make it easy to try new tools—or hard?
AI will change how you work: Want to test your organisation’s AI readiness?
Take this short 6-question diagnostic to get quick input and reflect on how your culture, leadership, and ways of working may support or hold back AI adoption.
(We’ll share aggregated insights once enough responses are in.)
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