The top 5 AI trends for 2019

The top 5 AI trends for 2019

Tim Sheedy, principal advisor at Ecosystm, details where organisations need to make ‘smart investments’ as AI reaches mass market adoption.

A major merger is on the cards

Tim Sheedy, Ecosystm

The next year will see some AI technologies reach mass market adoption, predicts Ecosystm.

In a new report on 2019 tech trends, the disruptive technology research and advisory firm says 2019 will also be the year AI starts to impact employee and customer experiences, from the board to the living room.

“Businesses that make smart investments in AI will be ready to create more personal, more effective, and easier customer experiences in order to drive top and bottom line growth,” writes Tim Sheedy, principal advisor at Ecosystm, and author of the report.

Sheedy shares with CIO New Zealand his top five AI trends for the coming year and how CIOs, their teams and C-suite colleagues can prepare for the changes ahead:

1. Machine learning and IoT sensor analytics will drive AI growth

AI is not a single market – it is made up of many components – often thought of as the building blocks of intelligent applications, explains Sheedy.

The Global Ecosystm AI Study, from which the five trends were taken, shows that growth in AI over the next 12 months will come from machine learning (ML), he states. This is because this capability is applied to a range of problems and challenges across the organisation.

IoT Sensor Analytics will also see strong growth, he says, due to the rise in IoT implementations and subsequent exponential growth of data coming off these sensors, plus the desire for organisations to do something intelligent or different with this data. Robotic process automation (RPA) will continue to grow, as will chatbots and virtual assistants. 

Adoption of AI Technologies – 2018 & 2019

Source: Ecosystm, 2018
Source: Ecosystm, 2018

He thus advises organisations to build AI competency centres with machine learning at their core. A key skill is the ability to help business leaders understand how ML can help them such as where to apply it, and where not to.

The competency centre staff should be trained not just on technology but on design thinking, customer journey mapping and other customer experience (CX) disciplines to ensure they put improving the customer and/or employee experience (EX) at the core of their ML projects, says Sheedy.

2. Growth in IoT will also fuel growth in AI

Sheedy notes that many organisations are already deploying or have deployed an IoT solution, and with these, sensors that generate large amounts of data.

“While these sensors today are, for the most part, one-way (‘collect and analyse data’), we are getting closer to the point where many of these sensors will be bi-directional (‘sense and respond’), he states.

Thus, businesses will look to AI tools - particularly IoT sensor analytics and ML – to help them learn from that data and respond accordingly.

Examine your own AI and data architectures – will they be able to serve smart endpoints?

Tim Sheedy, Ecosystm

Many deployments will not have time for the data to be sent back to a central database or central ML tool. Some will need the learning system to be closer to the sensor for it to act differently, in constantly or regularly changing environments.

The systems and architectures we have built today will not always work in the constantly-connected, constantly-learning environments of tomorrow, says Sheedy. “Examine your own AI and data architectures – will they be able to serve smart endpoints?”

3. Short-term, AI will create more jobs than it removes

Sheedy cites three major reasons why this will be the case over the next few years.

First, AI is doing a lot of jobs that are not even done by humans. These include analysing images for trends that humans did not see or looking for correlations in data sets that we didn't know existed.

Second, even where automation and AI are driving productivity, the majority of organisations are reskilling the affected people – or perhaps to offer a more human service.

Automation and technology-led productivity gains have been happening for over 20 years now, but employment levels have not dropped, he points out.

He sees AI-driven profit being ploughed back into businesses and creating more employment opportunities.

Third, organisations have started to hire for skills they will need to make their business smarter with AI. Many of these jobs today are in addition to, not replacing existing resources.

“Start training staff on AI skills,” he advises businesses. This will reduce the overall costs in the long run, as they will not need to hire externally for these skills.

Learn how and where AI can help your customers and your business, he adds. “Look for chances to make the CX or EX more personal and more ‘human’ – that is where you will find your opportunities to invest in AI.”

4. CIOs and their IT departments will slow down AI implementations

Many of the digital capabilities that businesses have been building over the past five or so years have not needed active participation by the IT team.

What started as ‘shadow IT’, he observes, became the standard way to deliver customer and business value as organisations pushed their technology resources into the product and customer teams, so they could drive innovation at pace.

Sheedy notes, however, that AI initiatives involve training algorithms with data – the more data the better the algorithms. Business leaders will need to work with IT to get access to this data, which typically resides in back-end systems, to train their models.

The type of IT leadership in your business will determine the rate at which you can access that data and hence how quickly you can implement your AI-driven solution, writes Sheedy.

Some organisations, though, have a CIO who has actively come up with ideas on how to make the processes and systems more intelligent and automated.  

His advice to organisations: “Work with your IT team to understand the delivery times and security/privacy requirements for this data. Help them to understand your requirements now and in the future – and encourage them to set up a process to quickly and easily provide data to business teams for their AI/ML requirements.”

5. A merger of massive scale will be driven by AI assets

The global Ecosystm study finds four companies - Microsoft, IBM, AWS and Google - account for 62 per cent of current and planned AI implementations. Ecosystm expects this dominance to continue in the near future.

At the same time, technology companies that are used to dominating their industries - like Cisco, HPE, Dell EMC and SAS - could be left behind if they do not get scale quickly in the AI domain.

“A major merger is on the cards,” Sheedy predicts.

“The question, ‘what happens if this company is acquired?’ needs to be considered in all your AI systems, platforms and tools sourcing decisions,” he concludes.

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