Marc Carrel-Billiard Reveals the Latest in AI at Accenture 

It has been a busy time for Accenture and artificial intelligence. Earlier this year, they released their annual Technology Vision report, which opened with Intelligent Automation as the first of five trends behind a ‘people-first’ approach. They also announced their new AI-enabled tool called myWizard, the ‘Intelligent Automation Platform to Deliver Smarter, More Efficient Application Services that Improve Business Outcomes’.  
To find out more about these developments, AI Business spoke exclusively to Marc Carrel-Billiard, Accenture’s Global Technology R&D Lead with over 18 years’ experience at Accenture and a background in artificial intelligence.  

 

Marc Carrel-Billard Headshot

Marc Carrel-Billiard of Accenture

We began by considering the broad discoveries of Accenture’s Technology Vision report. It reveals that ‘70% of executives are making significantly more investments in artificial intelligence technologies than they did in 2013’.

So are there any particular industries that are taking to AI more than others? “At Accenture we see AI being adopted across all industries”, Marc says. “Some disciplines are using AI more than others, but we really are seeing it across the board, and I would not say there is one industry specifically that is using AI more than another”.

Given that he sees the uptake of AI as universal in the enterprise, Marc is much more interested in the challenges businesses face when adopting the technology. “There several challenges, which focus around the technology itself and the people”, Marc explains. “Our vision is to put people first – you need to put people at the middle of your AI agenda”. But what’s important is the process which AI becomes involved with: “You have to consider which process is going to be the most relevant to bring in AI”, he says. “If you start to touch the process you’re going to change the interaction between the people and the machine. And this is the where we think of AI as the essential co-worker for the digital age”. Marc reveals the results of a recent survey that Accenture ran to explore management across different industries and in different countries. He explains that findings revealed that managers at all levels (84 percent) believe machines will make them more effective and their work more interesting, However, only 14 percent of first-line managers and 24 percent of middle managers would readily trust the advice of intelligent systems in making business decisions in the future. By contrast, nearly half of senior executives (46 percent) would readily trust the advice of intelligent systems.“Numbers aside, the key takeaway here is that we have a lot of educating to do with our people to explain that robots are not here to take away jobs; the robot is taking away some very core, automated activities that are not so fun to do, to give people a more interesting, different type of job. That’s the issue we need to correct, more than the technology”.

A particularly difficult challenge indeed. But who is tackling this? Are any organisations beginning to overcome it? Marc has a positive outlook, and has a specific use case in mind: “There are companies that are really leveraging the vertical application of AI in the workplace – some are implementing it as much as they can, while some are just doing trials.

If you look at a company like Shell, they have training packages which are pretty complex, and training managers to train staff. For instance if you run a refinery, or if you look for oil and gas you need to learn a lot of different things. Employees need to connect to their training managers and they need to understand what type of training they need to take based on their role, their career level etc. So Shell decided to look at using a virtual agent to replace the training manager to answer the employees’ questions directly. This saved a human training manager a lot of time in answering the same questions repeatedly; but of course they are there when necessary as well”.

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Having considered the overall landscape, Marc looks inwardly to Accenture’s position in this space.  “A lot of the research we are doing with artificial intelligence is based on some of the work in AI that we are doing ourselves at Accenture”, Marc explains. “We are very excited about our new platform, myWizard, which includes a virtual agent set of robots that we put together to help our people work more efficiently”. There are six robots on the myWizard platform currently, with additional agents planned for the future. One is robustly called Virtual Scrum Master. Marc elaborates: “This robot monitors numerous aspects of an Agile development project. IT can look across hundreds of Agile projects that we’ve run with clients, what staffing and release metrics look like, what works and what doesn’t. The virtual agent can make a unique recommendation for each client and give them the guidance that they need in the team – the size of the team, the timeframe of the project, etc”.

The Virtual Testing Savant is another robot. “This one is really cool”, Marc says. “It will look at your application, it will look at your requirement specification, and based on that, you will create some typescript automatically. It will also help you with test planning, test coverage, and it can even advise you on staffing – which type of people you need for that project”.

 

A third robot is called the Intelligent Data Scientist. “This identifies data patterns and mines data, allowing users to make more insight-based decisions. They learn patterns of data in different industry domains, and with this the can provide IT teams with information to do more with the data they have – correlate incidents, flag risks, spot trends and even protect from cyber-attacks. Our data scientist agent is equivalent to a graduate with three years’ of experience, except that it’s one-thousand times faster in data analysis”.

 

It’s clear that not only is this about automating processes to relieve people of the mundane jobs, but it’s also about retraining those people to oversee and understand those automated processes. Accenture have tested the model themselves substantially – Marc tells me how they used automation technologies to replace 10,000 roles last year, and in doing so reassigned those staff with more interesting jobs.

 

Start-ups

Having discussed the future of AI in larger, established enterprises, talk turned to the AI start-up market. Accenture’s report notes that ‘AI start-ups in the US alone have increased 20-fold in the past four years’. So what is the company’s position on what has now become a highly competitive marketplace? “At Accenture we are vendor agnostic”, Marc says. “All the work we’re doing towards AI is applied research, and if we see that it could be relevant to our clients we jump on it. We’ll test it, pilot it. We are working with many start-ups as well as ISVs. We have a department called Open Innovation dedicated to finding start-ups daily”. But “it’s not just the US”, Marc was quick to point out.

“It’s important to remember that this is global. There are Silicon Valleys in Tel-Aviv, Finland, all over the world – there are many different hubs of open innovation”.

 

Long-term future

Looking ahead to the future of AI in business, Marc insists “I’m an optimistic guy – AI is making good progress is in the right direction. But understanding people how the technology will affect them positively is crucial. There is a big hype right now – surrounding deep learning etc – and we need to temper that a little bit. People get very excited and we need to make sure they don’t get caught in the bubble of 20 years ago”.

 

Eventually though, Marc sees total implementation:

“My view is that AI is like cloud many years ago”, he says, “it will become mainstream. All these technologies will be accepted, and people won’t even think about it. Business leaders will be expected to have these technologies in their organisations”.

But the people will always come first, he concludes: “We need to make a clear distinction between machine and human. People need to be aware of when they dealing with a machine and when they are speaking to a human – it’s very important that these boundaries are clear”.

 

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