AI Driven Digital Transformation

Part 2

Part 1 is here.

Finding an AI Suitable Business Problem

About a year ago, I began working on one of my most difficult projects for the Victorian Government. It was a project that quickly looked like it would end in a consulting train wreck and possibly cost me my career.

The project began with our team sitting around a desk with the client and being handed multiple data sets. We were then told to “do some AI with it”. Not only that but we were given four weeks to produce something of value that wasn’t obvious to the client.

Given the time frames, we got straight into the task but approached it a bit differently given we knew we were missing a key part of the project – the business problem.

The first two weeks passed quickly with us tackling both cleaning and preparing the data but most importantly, one of us was spending a significant amount of time with the client trying to understand their current challenges and link these challenges back to their data and then request more data as we realised what was missing.

By taking an agile approach and having a highly collaborative team (with the design thinking part working closely with the analytics part of our team), we were able to navigate a challenging project and “do some AI” with the data set.

We discovered a business problem which we could share valuable insights on and quantify people’s intuitions, which helped the client as it killed the HiPPO[1] and replaced it with facts.

Quantifying gut feelings then led to several clear actions for the client to take which improved their service delivery, i.e. their core value proposition. We will see below why this is important and how it can determine the difference between failure and success of AI driven digital transformations.

Although not recommended, this approach to analytics succeeded but how should you approach such a challenge if given the choice?

Via the Business Problem

Someone once asked me,

“How do I understand the value of a data transformation? I mean, how can I know what it’s worth?”

The answer to this question is simple – on its own, a data transformation is worth nothing.

To be valuable, any data driven activity needs to solve a business problem. That is, why are we joining this data or building a massive data warehouse? What is the point of this analysis?

Most importantly, what action will be taken based on this analysis?

These questions lead us to the answers of the final questions we had in the previous post [Link here].

This idea of analytics leading to action is so important that at Bunnings the internal analytics team has three tenets to their analytics work with the third part being the most important:

  1. Aggregate
  2. Analyse
  3. Act

More than just creating a data warehouse, the purpose of analytics work is to derive insights and then do something based on that. Bunnings may have refined its approach over years but what should you do now?

The Right Way to do Analytics

There are many recommendations as to how to approach the challenge of deriving value from analytics. One was proposed by Gartner and looked at a company’s analytics maturity – however, this was wrong.

According to Gartner, a company’s AI capabilities are measured on 4 levels:

With each of the levels answering the following question:

  1. Descriptive: What happened?
  2. Diagnostic: Why did it happen?
  3. Predictive: What will happen?
  4. Prescriptive: What should I do?

As we saw in the Victorian Government example above, what counts is to have a business problem to solve and then to find a more efficient, digital way to solve this problem – not analytics maturity for the sake of analytics maturity. Once we have the business problem, and if we are new to the analytics space, we can use the Gartner model not to go too deep too fast and risk derailing the effort.

Finding the Right Business Problem

So we now agree that we need a valuable business problem to solve. Best is a corporate challenge burning across a P&L or balance sheet right now. Problems people currently face are ones they want solve – now.

However, how do we find the ones suitable to ignite an AI driven digital transformation?

The suitable challenges can be found at the core of the business. The challenges conducive to leading to something big are ones that form part of the chain of delivery of a business’ key services – the challenge itself doesn’t need to be big, it can be small but still have a big outcome!

For example, an amazing Australian success story, which successfully carried out an AI driven digital transformation, was by a company once called Aerocare.

Aerocare was founded in 1992 to provide ground services to airlines and airports. If you’ve ever looked out a window after landing at an Australian airport and see a team unloading your baggage from the aircraft, you are most likely looking at former Aerocare employees.

Aerocare’s value proposition is the cost-efficient delivery of people and equipment to quickly and professionally load and unload aircraft. Their value chain begins with forecasting airline service requirements at airports and ends with the unloading and then reloading of the plane on the day and time of the arrival of the plane (even if different to the plan).

If we examine the value chain and look at where an AI driven digital transformation can have the greatest impact, it is anything that improves Aerocare’s delivery of services. Aerocare also understood this, so they quickly identified a bottleneck in the Excel based rostering of people to deliver their services at gates and planes.

As the number of Excel tabs began pilling up and choking calculations – leading to weeklong rostering activities, the AI driven digital transformation opportunity had arisen. No unnecessary data warehouses for the sake of it, whatever was to be done would lead to change.

So Aerocare realised they needed to replace the current Excel based processes with AI tools to automate the roster creation and delivery.

This meant little or no change management – just better rosters. People followed their previous processes but with better tools. Like our health client, we’d found a part of their service delivery which could be easily replaced with AI tools. This then fundamentally transformed the way they delivered their services without changing the way the company did business as well as avoiding the risks associated with a large digital transformation.

Very quickly this led to more efficient allocation of resources, more competitive bids for service provision at airports and significant company growth. The result for Aerocare?

In 2017, the global ground handling giant Swissport acquired Aerocare for an undisclosed sum of money.

Why Did This Approach Work?

In 2015, MIT Sloan review published an article which incidentally revealed why the Aerocare approach to AI driven digital transformation worked. They stated:

“[M]aturing digital businesses are focused on integrating digital technologies, such as social, mobile, analytics and cloud, in the service of transforming how their businesses work. Less-mature digital businesses are focused on solving discrete business problems with individual digital technologies.”

The Aerocare solution seemed to solve a discrete business problem, however, it was one that transformed how the business worked, as intended in the first part of the MIT Sloan quote.

So we don’t have to look to uproot an entire organisation – we just need to find that part of the service delivery that is currently being done on a regular basis with:

1. Paper based tables; or

2. Excel sheets; or

3. Complicated, manual database queries.

Assume some general delivery of services or products as shown in the following diagram:

Then if any of the bullet points in the white boxes are being carried out using one of the above three manual methods then each could be tackled independently and easily via an AI tool. Once we’ve found the part of the service delivery in the above stylised diagram which is like 1, 2 or 3 above, then it is time for AI driven digital transformation.

Turning Data into Value – AI driven digital transformation

The answer to the success of the transformation question seems simple but it is only so in hindsight:

Find the key part of your service delivery that is not automated. This can most likely be recognized by the usage of Excel or regular, manual database queries.

Then replace this part of the service delivery with AI tools. The value is clear and immediate so let’s make clear what we’re doing:

Replace error prone, slow, manual and insecure processes with robust, fast, automated and secure AI techniques.

This now can resolve our question we posed earlier:

“How do I understand the value of a data transformation? I mean, how can I know what it’s worth?”

By choosing a business problem associated with your service delivery, it then becomes clear and easy to identify the value of the improvement as the change will directly improve the bottom line. It will improve the way your business operates and the value of the business. AI driven digital transformations allow one thing above all – more profitable and faster scalability via better decisions and automation.

Not only does this show us how we turn the siloed piles of data into value (we don’t touch them unless necessary), a McKinsey study showed that digital transformations that use some form of artificial intelligence, are almost 50% more likely to be successful. AI is one of the greatest factors that differentiates companies with successful digital transformations from those that aren’t.

So where does your Excel bottleneck exist? What part of your business can you transform with an AI driven digital transformation?

Reach out to us below to find out how and discover more companies embracing AI.


[1] Highest Paid Person’s Opinion

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