Artificial Intelligence in 2019:
26 Predictions for Business Operations

Much like business transformation, data is not a new concept but how businesses use it will be the key to whether they sink or swim. Those that understand it to enact continuous improvement and change will be the ones that survive. This is because of its single source of truth and it will be this that continues to fuel the fire for areas such as AI, chatbots and machine learning technologies.

Artificial intelligence and machine learning will lead to the largest productivity increases we’ve seen in years, not just for people, but for machines.

AI and ML applications will enhance and transform the user experience by reducing both technology and human complexity. The line between human and machine tasks will change and more of the thinking tasks of every business and system will be driven by machine intelligence.

AI and ML will continue to leverage the influx of data to drive greater efficiencies and insights that will optimize both the apps and devices we use every day. PCs will be able to predict power consumption needs based on usage patterns while apps will continue to learn from user preferences and behaviours to deliver more personalised experiences.

Even large-scale enterprise systems will use AI and ML to drive greater automation and intelligence, making it easier for humans to gather insights or make strategic decisions based on data as we move from peta-scale to exa-scale to zeta-scale.

The opportunities and need for artificial intelligence and machine learning are well understood: with projects such as IBM’s Watson already showing its potential. With this groundwork already in place, 2019 will be the year where we see more real-world applications of AI, as businesses begin to ask the big questions around ‘how’ AI will impact their operations.

For instance, how will organisations determine what questions are used to train their AI implementations? How will they address potential ethical challenges, such as the introduction of bias into the equation? Will consumers ever truly trust the technology? 2019 will see businesses and academia working together to determine the answer to these questions: working towards a trusted approach for developing AI and allowing it to find its place in the mainstream.

One business area in which AI can be a step change is that of HR. We are not talking about AI replacing the sympathetic ear of an HR manager instead AI’s unbiased algorithms and machine learning techniques can be leveraged to predict and learn possible outcomes based on masses of historical statistical data.

Hiring managers are often out of there comfort zone when hiring new candidates, with most making up their mind within the 60 seconds of the initial handshake. This leads to 39% of businesses admitting that the interviewing and assessment skills of their staff should be improved, according to The Recruitment & Employment Confederation, resulting in many missing that killer hire.

AI-based algorithms can rapidly scan large volumes of candidate CVs and internal employees to identify who not only matches the criteria for an interview but if employed are likely to succeed in the role. Post or during the interview process, AI can review transcripts, audio and video content to look for those missed signals that indicate a candidate who can succeed or fail and flag these to the hiring manager.

Many of the major talent acquisition vendors are already building out or have deployed AI teams to underpin their platform growth.

In 2019 there will be a major shift in how AI is used to look for more useful and actionable insight from customer data. Whilst sentiment analysis can be a useful tool, there are issues with its accuracy and it doesn’t predict anything on the whole.

As Forrester and others have also noted, AI text analytics will become a more powerful, alternative tool. This is because it classifies customer data by using actionable concepts and intent markers rather than basic keywords, giving a much clearer picture of the customer experience.

Contact centres will also adopt more AI to unlock complex sets of omnichannel interactions to understand what and why customers call, making it a key focus for CX in 2019.

Voice technology risks becoming the next big IT skills gap and a real barrier to innovation. The value of the market is forecast to reach $8.30bn by 2023 – but this rising demand is set to have an impact as early as next year.

While voice technology has been around in the consumer world for the past few years, enterprise adoption is the next major focus for organisations. It will impact every technology interface and could transform the employee and customer experience.

But our research found that the available talent pool to support this innovation will soon be exhausted, with salaries and day rates rising significantly as candidate availability becomes stretched.

Businesses need to get ahead of the potential talent shortage by targeting those with adjacent skills in coding languages like C, C++, Java, and PHP.

Employers must look both within and beyond their businesses for talent and consider cross-training those with relevant coding skills to fill the anticipated void in voice technology talent.

Contrary to most reports, AI can be a force for addressing gender and racial bias in 2019. If implemented correctly, the technology is far fairer than humans. We all have unconscious bias, which cannot be controlled and, in most cases, is extremely difficult to interpret. Its human nature to like what we know, and place value in our own strengths. Shockingly in the modern age, referrals remain the number one source of hires – proving that a form of nepotism remains rife.

Al, on the other hand, can be audited, giving HR departments tangible insight into bias and why hiring decisions are made. While Amazon’s tool has been widely condemned, the faults in the system were discernible in way that is impossible to interpret from humans. Amazons found its tool was downgrading CVs with certain words– how many of us could offer that level of analysis after reading a CV or conducting an interview?

Having this granular level of accountability can, in turn, help combat lawsuits and inform internal policies. If you can prove that no bias was present, or point to how potential flaws have been mitigated, it becomes nigh on impossible to sue. Policies too can be governed by such insight – why are so few applications coming from women? Why is aggressive and singular wording preferred? And crucially, how can these trends be reversed?

Intelligence and insight are going to get even bigger in 2019. I’m not just talking about cutting-edge developments in areas such as AI and machine learning but adding increased intelligence to the whole IT estate.

More and more solutions now offer telemetry that can help with predictive maintenance and support – for example, printers that can order their own toner. We now have more data than we know what to do with; the big trends moving forward will be turning that into insights and using it to improve operational efficiency and user experience.

Businesses are over 'AI'. The hype cycle burns faster these days and AI is experiencing a decline similar to what’s happened to the IoT. The joke going around Silicon Valley right now is that you get kicked out of a pitch if you bring up AI.

This is because AI, at the level of market perception, is unachievable by the vast majority of organisations. The challenge is that you not only need data scientists that are being hoovered up by the likes of Facebook, Google, Microsoft, etc., but you also need huge, richly codified training data and most companies are struggling just to connect their own customer data.

In addition, most of the buzz from software companies such as IBM and Salesforce amounts to nothing more than the mere evolution in natural language processing. All this means is that we are now really good at text-to-speech and speech-to-text, allowing us to create chatbots and recognise written content from interactions. What we do with that content once it’s wonderfully transcribed from voice still needs to be developed.

To caveat that though, AI is meaningful and has changed everything, including its ability to interrupt every digital record that we’ve ever created. However, it still has a long way to go to take people’s jobs -- it’s unlikely AI will aid professions like marketing in a way that is accurate enough that they'll even bother. Try uploading an image and seeing how well Watson or Adobe's Sensi really does. One of my favourite examples being, ‘Is it a Chihuahua or a muffin?’

Considering this, I think AI is dead...or at least we'll stop talking about it in 2019 with empty statements and start talking about business problems and business outcomes again…I hope.

Companies will start demanding better knowledge of the ROI of their AI investments. They will be looking for a more accurate way to measure AI’s impact on the bottom line and customer experience.

This starts by considering your unique data acquisition situation. What data do you have and what data you can get? What data do you not have, that you need to solve for? And they will look for new ways to leverage their existing AI investment by blending AI and human touch—identifying where AI can support humans in their daily roles, and when to transition from a machine-driven interaction to live person for a better outcome.

Bottom line, if you’re asking how to deploy more AI, you’re asking the wrong question.

In 2019, we foresee that chatbots will be rolled out in more strategic ways. 2018 has seen companies adopt chatbots as tactical projects or as proofs of concept (POC). As their value is proven, more strategic approaches will be taken to broaden AI implementations across the rest of the organisation and customer base.

Chatbot projects will also move out of the siloed departmental approach as businesses see the potential to engage with customers across multiple touch points via chat. The transformative power of conversational AI will increasingly become a reality.

As with traditional IT services, we are likely to see companies stepping away from single enterprise funded development (due to its inherently high cost) and towards standardised commodity AI cloud offerings from the likes of AWS and Microsoft Azure – defining an increased level of maturity in the delivery model.

This will of course mean the “siloing” of more complex AI offerings with the big providers.

The drive towards AI will become much more of a focus for businesses next year, particularly as companies begin to realise the huge benefits in efficiency when it comes to building and deploying applications in the cloud. Take Microsoft, for example; increasingly its customers are using Azure AI to build apps that are smarter, more intuitive and responsive in order to free up people power, and we will see more of this in 2019.

Businesses utilising the cloud will do well to leverage the mantra that ‘knowledge is power’, and applying predictive analytics to data that helps drive AI can mean companies can act on it and get ahead of the game. Working with a tech partner that has the technical capability to gather this data as well as provide consultancy of how it is used will be highly influential in helping organisations drive the benefit of AI in their businesses.

2019 seems as if it will be the year of analytics, machine learning and AI. These tools are already available, though their take up has often been delayed by a failure to match these new capabilities with appropriate new workflows and SOC practices. Next year should see some of the pretenders – those claiming to use these techniques but actually using last generation's correlation and alert techniques in disguise – fall away, allowing the real innovators in this field to begin to dominate.

This is likely to lead to some acquisitions, as the large incumbents, who have struggled to develop this technology, seek to buy it instead. 2019 is the year to invest in machine learning security start-ups demonstrating real capabilities.