Enterprise

Accenture: How automation, augmentation and innovation will mean success in AI initiatives

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According to a report from Accenture, artificial intelligence is forecast to double economic growth by 2035. The opportunity is a big one; the chance to transform business, as well as society in general, is there – but overcoming ethical and technological roadblocks needs to occur before progress is truly made.

Cyrille Bataller (left), managing director of emerging technology innovation at Accenture, has been involved with artificial intelligence in some capacity for the better part of two decades. Following the 9/11 attacks in 2001, Bataller was part of the team which focused on the then-emerging area of biometrics for travel documents. These initiatives can be seen today as a forerunner to the passport e-gates used at many airports around the world.

Bataller refers to this as AI because it was ‘an example of a machine recognising people and performing an activity that was human in nature beforehand.’ Since then his work has encompassed further aspects of computer vision, natural language processing, as well as deep and machine learning. Prior to his speaking appearance at the AI & Big Data Expo in Amsterdam on June 19-20, AI News caught up with Bataller to discuss Accenture’s three-pronged vision for AI, ethical concerns, as well as the true depth of transformation available to organisations.

AI News: Hi Cyrille. Tell us about Accenture’s vision for AI.

Cyrille Bataller: Firstly, when we talk about AI, we talk about in a broader context, which is applied intelligence. Applied intelligence is combining AI, analytics, and automation, in order to transform businesses.

An example of how we transform what we do today is through automation, augmentation, and then innovation. Automation is quite straightforward and intuitive – we are able to automate repetitive, monotonous activities that at the moment we’ve had to do manually because there were no alternatives. We have come to a point where we can avoid having people work like robots; by using robots instead to do these repetitive tasks and let people focus on the higher value activities where creativity, empathy, and depth of understanding and context is required.

If we talk about augmentation, this means we can assist people with AI solutions to help them make the right decisions faster, to crunch large volumes of data and identify patterns that they would not have been able to otherwise, and to help them benefit from the collective experience of the organisations. You can take the 10 or 100 best experts, the best decisions, over the last year, and you can train a machine learning model that will assist junior employees to help them make the right decision benefiting from that collective experience.

We’re able to therefore free up budget and resources from the repetitive – insurance claims processing, trade settlement, customer service queries, and so on. What’s most common can be automated, and while we see the budget is not as much to handle these activities, we’re actually improving the outcomes on these activities as well because there’s a faster response time, it’s more accurate, more consistent, and that we can handle better peaks in demand. We can redirect both the people and the budget to new strategic activities – that will be innovation.

AI: How would this third leg of innovation work in a given use case?

CB: If AI systems behave somewhat like people, like employees – they can handle emails or business processes – suddenly you have access to a low cost and near infinite digital workforce that can complement the human workforce. For instance, passport control – you can call these robotic border guards and you can have as many guards as you like at the immigration hall and therefore handle peaks in volumes. You could have robotic video surveillance agents. When you have cities with 30,000 cameras, nobody’s watching them – they just bring out the right camera when a call comes in about an incident, or they bring out the right recording when there’s an investigation.

The reality is if you can have robotic video surveillance agents watch all cameras at once, you can have 30,000 cameras watched 24/7, and look for things that we as humans would look for, such as counting people, counting vehicles. Then suddenly you have much better situational awareness and radically new use cases.

AI: There are of course ethical concerns with regards to this, and plenty of examples where things haven’t gone right – for instance a lady in China was accused of jaywalking when the automated facial recognition system spotted her image on the side of a bus. How does this need to be resolved?

CB: I think AI is a technology at the service of people; it needs to be used intelligently, and systems need to be designed with people in mind, and with the best interests of people in mind. It is certainly not a technology that replaces people, or anything like that, but rather supports and augments them.

The example you gave is exactly why you need people in charge. AI systems automate lots of things, but final decisions need to be taken by people who have an understanding and a context that these machines are lacking, and will be lacking for the long term.

AI: You’re speaking at the AI & Big Data Expo around ‘responsible AI transformation’. Tell us a bit about what the session will hold both from a business and personal context.

CB: There are two key words in the title, which are ‘transformation’ and ‘responsible.’ Transformation because, as I said before, I’m very optimistic about this opportunity which holds enormous potential for society, for humanity in general, as well as for business, and organisations, and governments. At the same time it needs to be done responsibly – designing solutions in a way that are articulated earlier, freeing up people and money to focus on other value added activities rather than reducing head count and cutting costs. That’s an example of not doing it responsibly.

We try always to articulate our projects in a broader context that benefits organisations, customers, and employees. Everyone benefits from these solutions.

AI: What developments can we expect around artificial intelligence for the rest of this year?

CB: I think we’re at an interesting point. There are many organisations that are doing tests, pilots, proof of concepts – but in spite of such proof of concepts showing such promising results, there is a little bit of an education needed to scale and rationalise. We see some promising results when we test the pilot, but there’s a certain organisational resistance to move to scale. When it is moved to scale there aren’t as many examples as you would think when you look at the potential business cases of these solutions.

I think and I hope that we reduce this chasm over the next year. We’ll see more and more broad transformations happening and organisations reaping the benefits of these broad transformations – and that will see more organisations leverage the digital, AI-powered workforce.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech ExpoBlockchain Expo, and Cyber Security & Cloud Expo.

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