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AI has gone from a distant marvel of the future to a driving force in today’s business ecosystem in a remarkably short period. With Gartner predicting that up to 40% of enterprise application will have embedded conversational AI by 2025; AI promises a seismic shift in the way businesses operate, manage employees, and fundamentally make careful decisions to better serve their customers. Leaders and managers across the globe are rallying to understand how they can incorporate AI into the facets of their service operations, to deliver better outcomes equally for their customers and their business. 

In this blog we’ll take a step back to look at AI’s role in transforming service operations, helping you gain a clearer understanding of both its potential and its applications when advancing your business. 

So what is AI?

Typically, Artificial Intelligence refers to the simulation of human-like intelligence in machines that are programmed to emulate thinking and learning patterns. But in the media lately we’ve all been exposed to the terms “Advanced AI” and “Generative AI”.

Let’s break down the definitions of Traditional AI, Advanced AI, and Generative AI. 

  • Traditional AI is what many of us are used to thinking about when it comes to AI. Think of it as an overly complex flowchart of IF THIS > THEN DO THAT instructions. While it’s great at processing known data, Traditional AI is only really good at providing outcomes which don’t change all that much. Robotic Process Automation is a great example of this technology in action in back-office operations. 
  • Advanced AI is where AI begins to level up its capabilities, thanks to deep machine learning. By reviewing large data sets and being taught how to spot patterns, Advanced AI can learn to make decisions and generate insights from your data. Under the hood, advanced AI uses algorithms which learn and improve from every experience, getting far better and much more efficient as it goes. 
  • Generative AI is the new kid on the block that doesn’t just follow the road—it creates whole new highways. By providing its algorithms with enormous amounts of text, images, video, audio, and your feedback, Generative AI can extrapolate from what it’s learned in order to create new data – including text, images, video, and ideas.

What kinds of business operations tasks can AI perform?

While Artificial Intelligence can be designed and developed to carry out a vast array of tasks, its true allure for business operations is to efficiently and effectively sift through, analyse, and derive actionable insights from the massive amounts of operational data businesses generate daily. 

This isn’t just about automating replies or streamlining customer outcomes. It’s about unlocking the treasure trove of insights buried within your data, and for operations management, this means a shift from reactive firefighting to proactive problem-solving. 

Broadly speaking, integrating AI into your operation can deliver four types of insights and value. 

1: Descriptive Analysis: Understanding the Past

The vast majority of operations already analyse historical data to understand what’s happening in their operations. Descriptive analytics powered by Artificial Intelligence do it faster, more comprehensively, and in greater detail than manual or semi-automated processes can. As a result, you can understand trends, pick up on missed opportunities, and receive granular insights into what has happened inside your business within a specific timeframe. 

For instance, AI could effortlessly analyse your historical employee performance data and generate reports to show you patterns in your workload and capacity over the past week, month, or year that you may not have spotted yourself. That insight can fuel better decisions about the overall planning and management of your workforce. 

2: Diagnostic Analysis: Unravelling the Reasons Behind Events

Diagnostic insights don’t only delve into your past trends, but connect the dots to show you the reasons behind your outcomes. 

For example, AI could analyse your workload, capacity, productivity, and HR data to conclude that the dip in productivity was due to the absence of three high-performing key team members, highlighting a critical skills gap in the team. 

These insights not only explain the drop in overall productivity, but also provide you with critical foresight to plan for future events where this may occur again. 

3: Predictive Analysis: Anticipating Future Outcomes

This is where things start to get really clever. Through the collection, study, and application of historical data, predictive analysis forecasts future events that might require attention. 

For example, this capability could predict that productivity may significantly decline in the upcoming week due to additional team members going on leave, or that workload is due to spike in three months, or perhaps a drop in overall performance due to a public holiday. 

By accurately forecasting possible issues, AI enables managers to proactively address potential challenges before they become problems. 

4: Prescriptive Analysis: Guiding Decision-Making

Perhaps the most advanced application of AI in service operations is prescriptive insights, which goes far beyond the simple collection of data and predicts future outcomes to recommend what actions a manager should take. 

Faced with the prediction of decreased productivity, for instance, AI could suggest reallocating resources from another team that it knows has spare capacity and the necessary skills to fill the temporary gap. 

This prescriptive advice can help organisations get to decisions even faster, and even improve the quality of decision-making across teams to optimise their operations, ensuring that productivity remains stable despite fluctuating team dynamics.

Getting ready for AI – overcoming the data challenge

While Artificial Intelligence can be designed and developed to carry out a vast array of tasks, its true allure for business operations is to efficiently and effectively sift through, analyse, and derive actionable insights from the massive amounts of operational data businesses generate daily. 

This isn’t just about automating replies or streamlining customer outcomes. It’s about unlocking the treasure trove of insights buried within your data, and for operations management, this means a shift from reactive firefighting to proactive problem-solving. 

Broadly speaking, integrating AI into your operation can deliver four types of insights and value. 

Many organisations are at the early stages of the data maturity model above. Nearly everyone has data that tells them what happened, and most can get their data to tell them why something happened.  

But that data isn’t always easy to find, understand, or use. Departmental silos are extremely common, making it hard to get an overall view across your operations. Different departments – or even different teams within departments – often use different language around data, too. They might define productivity differently or calculate it in different ways. Some may use third-party software to track productivity and capacity, others might use apps they’ve built themselves, and others may still be manually entering data into spreadsheets. And it’s almost certain that different types of data will sit in different systems – holiday and sickness data in one, work in progress data in another, and so on. 

With data that’s fragmented, siloed, and (usually) not in real- time, it’s going to be extremely challenging to get an AI to make decisions that are useful. 

Paving the way for AI in Operations

Preparing your environment for AI transformation involves several critical steps. Each is aimed at ensuring your AI systems have comprehensive, timely, and accurate data they need to deliver optimal results. 

You need to ensure availability and accessibility of high-quality data from right across your entire organisation to guarantee that your AI solutions deliver quality insights. This means: 

  • Breaking down departmental silos to get an accurate picture of your entire operation. 
  • Standardising your data formats and its definitions to make sure you can get a consistent picture across your entire operation. 
  • Establishing real-time data flows from your key critical systems so collection, analysis, reporting, and insights are never performed on old data. 
  • Integrating data from all of your existing systems to provide a holistic 360° view of operations, allowing AI to analyse the full spectrum of your operational data. 

If you’re ready to get real about AI, download the new white paper from Henley Business School in partnership with ActiveOps, titled The Future of Ops – How Will AI Reshape Service Operations.  

Authored by the esteemed Dr. Mona Ashok, this guide offers practical advice to harness the power of AI in operations management – whether you are just starting out on the journey or are already on your way. You’ll discover how to solve those data challenges we mentioned, and the role of Decision Intelligence in accelerating your journey to AI-readiness. 

Download the report now and embark on your journey to AI transformation with Decision Intelligence.

GET REAL ABOUT AI

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.

GET REAL ABOUT AI

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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