Getting real with AI: 5 areas for stakeholders to activate


There is a lot of excitement about AI, but somehow the reality is not really living up to the hype. At the moment, we don’t see enough real results or use cases emerging, even though everyone agrees that there is huge potential. I polled some of our experts to find out why this might be happening and identified five critical barriers that need to be overcome for AI to move into the mainstream.

1: You need a lot of data to build models

AI-based models probably need even more data than most because they need to learn for themselves. The explosion in the volume of data available from the Internet of Things, and particularly the increase in streaming data, should have made AI models far more ubiquitous. However, far too many organisations have not really caught up with this.

Joao Oliveira: "Streaming data is still an untapped resource for many, meaning that they are missing out on the power of analytics.”

Arturo Salazar: “Many have failed to appreciate that they may need to go hunting for new sources of data."

Ivor Moan: "Data preparation is crucial for getting value from data. Skimp on the data preparation, and you will find that you cannot rely on your analytical results.”

In other words, organisations that do not pay enough attention to data, and especially data quality, will fail to capitalise on AI.

Hidden Insights: Getting real with AI - five areas for stakeholders to activate
Getting real with AI: Five areas for stakeholders to activate.

2: Many models fail to move into production

Developing a model is relatively straightforward. The process often involves a number of different individuals and departments. Indeed, development and deployment are so different that they almost look like two separate life cycles, a bit like a salmon’s move from river to sea (and back again), according to James Ochai Brown.

Different skills are required, both project management-related and technological.

3: Users need to be able to understand insights from models

The best model in the world is only useful if it leads to action. That means that users have to be able to understand and interpret the outputs from the model to provide useful insights. Data visualisation is a crucial part of this process.

Data scientists, therefore, need to think about how they will demonstrate the outputs from their models to maximise the impact on viewers, for example, by asking themselves about the audience, and the message they wish to convey.

4: The real benefit of AI is business model transformation – but that is a long-term project

According to Christer Bodell, any organisation can introduce a single AI-based project or model, and it will improve current business practices. Getting real benefits from digitalisation requires something more: a genuine change in philosophy and a commitment to innovation that is likely to result in business model transformation.

This requires a longer-term change in thinking, says Andreas Godde, and particularly a focus on how value is generated for customers.

5: You need more users to increase use of AI – but they need to see others using it first

There is a book by Geoffrey Moore called Crossing the Chasm. It describes the difficulty of moving from "early adopters," those who adopt a product because it is new and interesting, to the "early majority," who will use products because they are useful. The early majority need to see products being used by people like them, to solve real-world problems. AI faces a similar challenge – that at present, people do not see enough real-world examples of AI being used to solve problems.

Generate some of those, and particularly examples that show how AI can improve customer experience, and both companies and customers will be more prepared to accept AI and move across the chasm.

Making analytics real

Let’s ensure your data is working for you. Discover how by joining us at Analytics Experience 2019 in Milan. Data holds the key: When used properly, your data can set your organisation up for success for years to come. After all, analytics is a powerful tool.

Join us at an event that gathers more than 1,800 data scientists and business leaders who are passionate about analytics. Let’s talk about real data, real insights, real results.

Can’t wait till October? Join #SASchat

We have something for you. On 20 September at 3 PM CET, SAS is hosting a tweet chat on Getting Real With AI. We’ll be putting the following questions to our panel:

  • What analytics-driven business model change have you found most inspiring?
  • In your experience, what are the critical drivers that deliver value from analytics?
  • What have you found to be the most common barriers preventing organisations from realising the full benefits of analytics?
  • How have mature analytics-driven organisations evolved their skills?
  • What should be on the dashboard, or balanced scorecard, of any organisation wanting to realise more benefits from analytics?

Here's everything you need to know to participate in this #SASChat! We hope to see you online!


About Author

Jelena Stankovic

Content and Communication Lead, CEMEA

Jelena has been working at SAS as a Content and Communication Lead for CEMEA region, focusing on SAS Platform and Data Science. She is communication professional with over ten years of experience, passionate about technologies and discovering ways to make complex world of analytics understandable for everyone. She earned M.A. degree in Chinese Language and Literature from Belgrade University.

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