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It’s safe to say that we’re all properly settled into 2024 by now. That means it’s time to make sure your goals for your service operations are on their way to being achieved – and we’re willing to bet that implementing AI is somewhere near the top of your list, if it’s not the main priority. AI’s potential for improving ops efficiency, boosting employee productivity, and giving customers outstanding service are massive. But beyond the hype, for many ops leaders the path to getting value from AI feels very murky. Where do you start? What pitfalls should you try and avoid along the way? And how do you measure success?

In our day-to-day conversations with service operations teams in Banking, Insurance, Healthcare and BPS, two of the biggest challenges we hear organisations grappling with when it comes to AI are:

  1. Making sure their data was in a fit state to support AI
  2. Working out how to implement AI in a meaningful and practical way.

That’s why we’re excited to share that Henley Business School has just published a white paper titled The Future of Ops – how will AI reshape Service Operations?, written by Dr. Mona Ashok, Associate, Professor of Digital Transformation. The paper builds on Dr. Mona’s extensive experience of operations from both an industry and academic perspective to provide practical advice on how to make AI a reality for your service operations. It even includes recommendations for five killer AI-powered apps that organisations should be looking at right now.

Let’s get into the three key points that you need to think about when beginning (or continuing) your AI transformation journey.

1: Assess your data maturity

Operations teams are struggling with too much data, and not enough insight. Since any AI-powered app relies on data to function, the higher the quality of your data, the more advantage you will gain from AI. So, the first thing any operations leader needs to do as part of any AI transformation project is to work out where their data is at currently. Ask yourself: 

  • How much manual effort is needed to gather data and generate insights from it?
  • Are all my teams measuring the same metrics, and are they using the same terminology when referring to their data?
  • Can our data show us what has happened, why it happened, and what we think will happen?
  • How old is the data, that is being used to make decisions?

In the white paper, you’ll find a five-step model that you can use to benchmark your own operational data capabilities, and start thinking about the steps you can take to maximise the potential of your data – and therefore for AI. 

2: Get your data in check

ince good AI relies on good data, getting your ops data in check should be the first task on your AI-ready to-do list. But wherever you are on your AI journey, there will always be opportunities to improve the quality of your ops data. The white paper outlines the most common data challenges faced by organisation – and, more importantly, how to overcome them. For instance, one of the challenges is data literacy and skills: many operations lack the ability to understand or work the tools needed to harness AI. 

The solution to that problem is multi-faceted of course, but one fundamental step is the need to agree on definitions in your data. After all, if you want everyone to be able to understand the data in your operations, it helps if everyone is using the same names for everything. We’ve seen different teams measured productivity in different ways, or called it different things, which makes it hard to get an operation-wide picture of productivity and capacity. 

Want to know more? The rest of the challenges, and tips for overcoming them, are in the paper. 

3: AI apps you can adopt today

We all want to get beyond the hype of AI and start making it real for our operationstoday, preferably. The paper does this for you by showing you five killer AI-driven apps that Henley Business School believes will have the biggest impact in operations this year – all of which are designed to support and augment your human colleagues. Here’s a sneak peek of one of the recommendations: 

2. Real-time skills catalogue
Building a skills catalogue to track the competencies of operations staff is essential for improving flexibility and responding better to shifts in demand across different operations departments. This ability to share resources across teams is critical for organisations to remain agile and better manage peaks and troughs in workloads.

How is it traditionally done?
Historically, maintaining a skills catalogue has been a very manual and static process.

What are the challenges with this?
Almost from the moment a traditional skills catalogue is created, the information in it goes out of date. As a result, operations leaders are never sure if a particular employee still has the ability to carry out a certain task productively or not (maybe because it has been several months since they last performed the task and are now too rusty to be effective). And because a skills catalogue is generally managed manually, any attempt to keep it up to date and therefore reliable takes a massive amount of time and effort.

How can AI solve it?
By using AI, organisations can build a live skills catalogue that tracks tasks being performed and by who in real time. With this information, the organisation gains an up-to-the-minute view of how suited a particular individual is to perform a specific task. The catalogue can record not only how recently an individual has completed a task, but how fast they completed it and to what standard. An AI-powered skills catalogue could even automatically assign tasks based on competency without any manual input, and ensure employees get regular exposure to tasks so their skills don’t atrophy.

In addition, AI can identify potential skills gaps and recommend areas for upskilling based on an organisation’s priorities (customer needs, regulatory requirements, and so on). This ultimately builds agility levels and creates cross-skilling opportunities that can improve job satisfaction and increase career progression opportunities.


  • A live skills catalogue helps organisations increase their agility by sharing resources across teams more efficiently to better manage fluctuations in work volumes
  • Greater agility helps unlock spare capacity and boost productivity
  • Cross-skilling staff means more work can be done with fewer resources, reducing operating expenses
  • Cross-skilling staff also increases employee engagement, improving career prospects and wellbeing
  • AI can track what work is completed without manual intervention, ensuring skill levels are always up to date

Discover Decision Intelligence

Crucially, the paper also confirms the role of Decision Intelligence as an accelerant on your AI transformation journey. Decision Intelligence is described as: “the combination of artificial and human intelligence to deliver predictive and prescriptive insights for service operations to help them make better decisions.” Essentially, Decision Intelligence helps you get your operation ready for AI much faster by rapidly increasing your data maturity. Which, of course, lets you unlock all those game-changing benefits to operations that we mentioned at the start of this blog. 

To learn more about how AI is poised to transform ops, and how you can harness the power of AI in your service operations, click below to download a copy.


A guide to transform your Service Operations team

Harness the power of AI in operations management, whether you are just starting out on the journey or are already on your way.


A guide to transform your Service Operations team

Harness the power of AI in operations management, whether you are just starting out on the journey or are already on your way.

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