Harnessing Intelligence for a New Era of Service

But adopting AI isn’t about hopping on a tech bandwagon. It’s about solving real problems for real people.

During a recent Copperberg session, Dr Aymen Gatri, Director of Customer Care for Mobile Harbour Cranes at Konecranes, delivered an important message. That message? Technology is just a tool. The real challenge is using it to create actual value for customers.

So, how can service organisations harness intelligence the right way? And what pitfalls should they avoid?

Focus on the Problem, Not the Tech

It’s easy to get dazzled by AI. There’s always a shiny new tool promising to revolutionise everything. However, as Dr Gatri pointed out, the key question isn’t, “Which AI tool should we use?” but rather, “What problems are we solving?”

Before investing in AI, make sure you:

• Understand customer pain points: Are long wait times frustrating them? Do they need better maintenance plans? Are recurring breakdowns slowing down their operations?
• Identify the goal: What’s the desired outcome? Is it faster response times, fewer equipment failures, or smoother workflows?
• Choose tech that serves that goal: Tools should make things easier, not more complicated. If an AI-driven system adds steps instead of removing them, it’s not solving the problem.

Example: If your customers struggle with unexpected downtime, a predictive maintenance tool could be a game-changer. It anticipates failures before they happen, keeping operations running smoothly.

Remember, technology should serve people, not the other way around. If it’s not making life better for your customers or your team, it’s time to rethink your approach.

Data Quality: Less Noise, More Insight

We’re swimming in data these days, but more isn’t always better. Dr Gatri made a solid point, stating that collecting tons of data doesn’t automatically lead to better insights. What really matters is quality data, not just sheer volume.

Think of it this way:

• Messy data = a library where all the books are dumped in a pile—you’ll never find what you need.
• Clean, relevant data = a perfectly organised library where you can grab exactly what you’re looking for in seconds.

To make data work for you:

• Clean it up: Keep it accurate, up-to-date, and relevant. Bad data leads to bad decisions.
• Integrate systems: Make sure all your platforms talk to each other so everyone has a unified, accurate view of operations.
• Focus on what matters: Only collect data that actually helps you solve problems or make better decisions.
• Make it visual: Use dashboards or charts to spot trends and insights quickly. Sometimes, seeing is understanding.

When your data is clean and actionable, decision-making becomes a whole lot easier.

Tools and Automation: Making Service Smarter

AI and automation can work wonders for service teams, but only if they’re genuinely helpful. Handing out tablets to technicians won’t cut it unless those tools make their jobs simpler, faster, and smoother.

Here’s how smart automation can make a difference:

• Reduce repetitive tasks: Let AI handle routine tasks like scheduling, basic questions, or data entry. This frees up your team to tackle the trickier, high-value work.
• Instant support: AI chatbots or smart assistants can give technicians quick answers or troubleshooting help. No more flipping through manuals or sitting on hold.
• Streamline workflows: Automate the paperwork and reporting. Tools that sync in real time keep everyone in the loop and cut down on mistakes.

Example: Imagine a technician fixing a crane. Instead of wasting time jotting down notes and filling out forms, an AI tool logs the issue, suggests fixes, and updates the system automatically. Less admin time, more problem-solving time.

When the tools are right, your team can focus on what they’re great at: solving complex problems and delivering top-notch service.

Embrace a Mindset of Change

Adopting AI isn’t just about new tech but rather about a cultural shift. Dr Gatri emphasised that organisations need to stay innovative and adaptable. Think like a start-up:

• Test new ideas quickly: Don’t wait for perfection. Launch pilot projects, see what works, and refine them.
• Gather feedback: Talk to your team and customers regularly to understand what’s working and what’s not.
• Stay flexible: Be ready to pivot and adapt as new challenges arise or as technology evolves.
• Celebrate small wins: Recognise progress and build momentum for bigger changes.

This mindset shift helps organisations remain resilient and ready for whatever the future holds.

Predictive Maintenance

One of the most powerful applications of AI in service is predictive maintenance. Instead of waiting for something to break, predictive tools foresee issues before they happen. This can:

• Minimise downtime: No more surprise breakdowns that halt operations.
• Save money: Fixing issues proactively is often cheaper than dealing with major failures.
• Improve reliability: Consistent uptime keeps customers happy and builds trust.

But predictive maintenance isn’t a plug-and-play solution. It requires:

• High-quality data: Accurate, real-time data feeds the AI model.
• Integrated systems: Your equipment, sensors, and platforms need to talk to each other seamlessly.
• Deep understanding of equipment behaviour: Know what normal looks like so you can spot anomalies early.

Without these elements, predictive maintenance efforts might miss the mark.

Pitfalls to Avoid

AI has enormous potential, but it isn’t foolproof. Dr Gatri highlighted a few common tips:

• Chasing trends: Don’t adopt AI just because it’s trendy. Make sure it addresses a real customer need.
• Poor data management: Garbage in, garbage out. If your data is messy, your AI won’t deliver useful insights.
• Lack of alignment: For AI projects to work, all departments—IT, customer care, and field service—need to be on the same page.

Keep the Customer at the Centre

At the heart of intelligence-driven service is one constant: the customer. Today’s customers expect fast, seamless, and reliable service. AI can help meet these expectations by:

• Offering remote support: Virtual assistance that speeds up issue resolution.
• Automating tasks: Faster processes that reduce wait times.
• Predicting problems: Addressing issues before they disrupt service.

Remember: It’s not just about what customers want—it’s about why they want it and how you can deliver it better.

The Road Ahead

The future of service is intelligent, adaptable, and customer-focused. Dr Gatri’s advice is clear:

• Solve real problems
• Keep your data clean
• Stay flexible and adaptable

Good service isn’t about flashy tech. It’s about helping people, solving their problems, and making their lives easier. If AI can do that, it’s a tool worth using.