Understanding Your Artificial Intelligence (AI) maturity
The hype around AI is justified. In 2017, companies spent around $22 billion on AI-related mergers and acquisitions, about 26 times more than in 2015. Avanade’s own research on AI investments shows that businesses see the opportunities – 92% of IT leaders believe that automation technologies are key to addressing the emerging requirements of the digital business.
But enthusiasm only gets you so far. It’s clear from working with clients across a number of industries that there are different AI maturity levels. AI maturity isn’t just about the technology you use, it’s also determined by the people you have in place and the supporting business processes. Understanding what you’re doing today gives you a clear starting point so you know where to focus your efforts.
1. Just getting started – The majority of large enterprises are just getting started or forming their strategy. IT executives hear everyone talking about AI, and are being told to make it happen. Yet it raises more questions than it answers: What part of the business should we start with? How do we apply it? What ROI will we see? What, holistically, are we hoping to achieve?
2. Middle of the road – There’s a group of businesses that are heading in the right direction but are still some way from being able to realize the true value of AI. They have a holistic view of their data and may already be using it to help them understand their customers better and predict what actions they will take next.
3. In the fast lane – Finally, there are a handful of businesses applying advanced analytics with machine learning to well-ordered data sets to create interesting – and valuable – scenarios. Like a billion-dollar global business we work with that’s using AI to predict health inspections at its customers’ sites with 90% accuracy. We worked with them to create a solution, which uses Avanade Modern Analytics Platform and a host of Microsoft Azure big data, analytics and cognitive services.
What does AI maturity look like?
We’ve been working with an insurance company that started with a small experiment that it evolved and added to over time. It began by automating rote, manual processes to prioritize claims and assign them to workflows.
The company took the next step by adding cognitive services that could read all customer query tickets and understand the customer’s intent. The queries were then categorized and assigned to the right workflow. By using AI to answer more standard tickets, the company liberated people to answer more complex questions.
The new approach has resulted in 40% to 60% gains in efficiency and resolution time. It shows that you don’t need to take big steps. Simply find the right process, get started and then evolve your approach to deliver business results.
How can you get there?
From our experience, there are three areas where every business can elevate their AI maturity level:
1. Be data ready – AI can only learn from the data that’s put in front of it. The good news is that you don’t need tons of data to get started. But if that data isn’t ready, applying analytics will be futile; results will be misleading, and more work will be created. This is the most important step – take the necessary time to ensure data sets are ready, and set up processes for all future data to automatically be primed for analysis.
2. Human first, technology second – You need to understand the human processes and behaviors that are driving your business, and decide how AI can augment them, not replace them. Think less in terms of the technology, and more about the impact that you want AI to have on the people connected to your business – both customers and employees. We call it human-centered AI – an approach that focuses on augmenting the workforce to improve customer and employee experiences.
Here’s an example. Mercedes decided to switch to 100% automation on its assembly lines. However, when consumer needs changed, the factory was inflexible and unable to respond quickly enough because of the reprogramming time needed. Mercedes consequently brought in a human workforce that the new robots could support, rather than replace.
3. Ethics, governance and principles – With great power comes great responsibility, and every business looking to embrace AI should be clear about what data is being used, and for what purposes. According to our research, 89% of IT decision-makers say they have encountered an ethical dilemma at work caused by the increased use of smart technologies and digital automation, with 87% admitting they are not fully prepared to address the ethical concerns that exist in this new era.
Think about creating a digital ethics framework that sets out how you will manage the bias that can be inherent in any AI algorithm. This includes internally built applications and purchased solutions. Avanade recently created an ethics committee that is developing a digital ethics framework to help us internally and to guide our clients.
To find out more about our human-centered AI approach, check out our point of view.