Artificial Intelligence in 2019:
16 Predictions for AI Development

Investors and the tech press are all abuzz about machine learning, but those neural networks are only as good as the training data from which they learn. High-quality datasets—typically the bigger the better—yield more accurate models. As ML initiatives scale, we expect that the pain of cleansing and preparing high-quality data for ML models will become more apparent in 2019. Data preparation is still widely regarded as the biggest bottleneck in any data project, which means that data scientists often spend more time preparing data than actually building and tuning machine learning systems. In order for ML to make an impact at scale, organisations will need to first accelerate their data preparation processes.

As we move into 2019, organisations will deploy more technology that enables predictive insights into IT infrastructure. Right now, most IT managers are taking a rearview mirror approach when reacting to unplanned downtime caused by interruptions related to software or hardware error, component failure or something even more catastrophic in the data centre.

Incorporating predictive technologies will enable proactive monitoring for downtime and faults so IT managers can take preventative action before a disruption ever occurs. Being more prescriptive can lead to fewer disruptions and less downtime in operations.

More frequently, AI is enabling predictability and will play a key role in data protection in 2019 and in the future. As businesses are continuing to adopt more complex IT environments, such as hyperconverged infrastructures and other modern workloads, data protection will also need to adapt. AI consistently learns from the system as these dynamic IT environments adapt and change.

Enterprise cloud environments became more complex in 2018, with web and mobile transactions now crossing an average of 35 different technology systems, compared to 22 five years ago. This complexity will grow over the next 12 months, with 53% of CIOs set to deploy more technologies into their cloud environments.

As a result, we’ll see IT teams accept that the old way of IT operations cannot cope with the ever-growing amount of technologies, systems and their dependencies. In 2019, we’ll see cloud operations be transformed by the adoption of artificial intelligence to help IT teams understand and manage this complexity, and also lay the foundations to begin automating cloud operations.

Artificial intelligence is changing the entire business landscape. Gartner predicted that in 2018, AI will create more jobs than it eliminates, and in 2020 it will become a positive net job motivator, creating half a million new jobs. The link between digital transformation and the customer experience is an imperative – and 2019 will be the breaking point.

As companies implement new AI-driven technologies such as chatbots and voice search, they neglect to think about the impact to the customer experience. Customers will become increasingly frustrated with the lack of human interaction and a fractured customer journey as businesses struggle to tie together channels. Customers expect to seamlessly switch from self-service options on web and mobile to chat, email or phone.

Businesses should take a step back, plan customer-centric technology implementations and consider how it ties into the whole customer journey. To win, they must offer customers the right mix of new technology and traditional service. This includes maintaining the human touch by making it easy to access the best person for more complex issues.

2019 will be the year that machine learning begins the shift to artificial intelligence through the use of complex simulations of biological neurons instead of simple mathematical ones. Machine learning models currently use simplified mathematical models of neurons.

But with specialised hardware, better neuron simulations will lead to the next generation of machine learning, the simulation of biological brains. We can see this in specialised hardware such as the SpiNNaker project, Brainchip's Akida, and Blue Brain's neuromorphic cortical columns. While AI is not real yet, we’re starting to see early evidence of the shift.

Cloud migration, increasingly critical big data systems, and spiralling costs and complexity mean that 2019 has to be one of change and renewal. The industry is racing towards new ways of using cloud services to spin up and down a more agile big data stack, and indeed, hybrid cloud models are emerging that take into account internal and external service delivery so that enterprises receive a really customised, cost efficient operation.

If we accept this premise then we’d expect to see AIOps melding with DevOps as a top priority for the enterprise as data is funnelled into training and improving AI-oriented application development and delivery.

As an unrelated factor, it’s a widely acknowledged truth that the talent gap within the DevOps and big data space is becoming quite the challenge. 36% of enterprise business and IT decision makers in a recent survey, conducted for Unravel Data by Sapio Research, discovered that one of the biggest pain points cited was talent scarcity at 3%, followed, perhaps inevitably, by the simple fact that it takes too long to get to insight, as 34% of the sample claimed.

Given 2018’s indications, TensorFlow and H2O will be breakout technologies in 2019, and Spark and Kafka will continue to see accelerating growth as the New year progresses. In fact, the ecosystem of tools the DevOps teams need to draw on will only continue to expand.

Enterprises must find a way to stitch the fabric of the data stack together to get fast, usable insights into the operations powering their BI, customer service, and forecasting applications. Efficiency and effectiveness, however the business measures the KPIs, must be tightly controlled else the whole stack will rapidly spiral out of control in 2019. Unravel’s research showed that although 84 per cent of respondents claim that their big data projects usually deliver on expectations, only 17 per cent currently rate the performance of their big data stack as ‘optimal’. This isn’t a state of affairs that the business can allow to continue.

There needs to be a balance of innovation and control, testing and production, efficiency and effectiveness. Finding that balance will require enterprises to take some calculated risks, but this can be done without live production suffering as automation and intelligence supports the DevOps team in their task of making the magic happening.

In 2019 we can expect to see AI crossing new frontiers - we are only just beginning to explore applications of AI in healthcare, for example, where new technology has the exciting potential to help prevent, detect and treat a wide range of diseases.

But the industry as a whole has its work cut out for it, if it wants to curb some of the uglier sides to advances in AI. I am not just talking about data misuse, but technical limitations as well. The main issue which will derail the development of AI if not dealt with effectively, is the issue of ‘bad data’ - incomplete, unethically-sourced and poor quality data. When it comes to generating revenue from mobile apps and websites, as organisations only see what happens inside their own apps, sites or ecosystem, a reliance on third-party data from questionable sources provides an incomplete view of the mobile user journey.

In essence, when it comes to AI, it’s arguably better to have no data at all than to rely on bad data. Without the full visibility provided by complete mobile user journey data, the results will be ineffective campaigns, wasted ad budget, and poor consumer perception. We’ll see more businesses coming to realise this in the new year, and taking a more strategic step in their data strategies.

Migration to the cloud has become a megatrend. This has led to new requirements in terms of securing services and the required infrastructure. In particular, star-shaped WAN topologies with central Internet access must be redesigned with regard to their compatibility with increasing use of cloud services - keyword SD-WAN.

IoT and Industry 4.0 also open up new areas of attack. Companies should increasingly think about device recognition in the network in order to segment out smart devices accordingly.

Even if it is a truism, e-mail remains the primary gateway for malware. Users can now protect themselves much better against this with intelligent email security products.There is still a lot of catching up to do here. Therefore, all necessary security technologies should always be preceded by a well-founded education of the employees.

Companies must develop a comprehensive security awareness programme that addresses the most important security issues. The solutions will continue to evolve towards automation in 2019.