We’ve all heard the scare stories. The availability of endless data will allow organisations to become less reliant on the human workforce.
Artificial Intelligence (AI) is going to be smarter than humans. And automation will take away lots of our jobs.
How much of this is really true though? Despite advances in these technologies, like conversational AI, they’re just tools to be used in the endeavour of making our lives easier and organisations more productive. But even a tool with contextual and conversational capabilities can’t provide the unique flexibility of human touch and true ingenuity that we all desire and admire. It’s in our nature.
Which begs the question, how can we continue to make the most of them in our work and lives? The answer lies in using them to empower us. In doing so, they’ll free us to do what we’re best at. Communicating, connecting with people, empathising and ultimately, problem solving – AI can solve any of the problems we can think of, but it can’t think of any problems we can solve. In other words, we need to harness them to be data-driven and technology-enabled in service of us, but not to replace us.
To achieve this, leaders need to focus on three things. Firstly, they need to understand what data is available to them. Secondly, they must figure out how to make sense of it with AI and Machine Learning (ML). Then they should look at ways to use automation to take on the simple tasks we’re not best suited for.
Understanding the data available
The first step to empowerment is as simple as understanding what information there is, where it sits within the business, who has access to it and whether there are any holes limiting its effectiveness.
To decide which of that data holds the most value, a good starting point is identifying the biggest challenges faced by the organisation. For example, is gaining a single customer view difficult because communications happen over different channels? Understanding the problem is the first step in fixing it.
Once a business has worked out these challenges, it needs to get solutions in place to collate and analyse the data ready for interpretation. Finally, it needs to ask the right questions by creating suitable hypothesis which the data analysis will support in solving. In every step of that journey, the importance of human involvement cannot be understated.
To achieve the results required at scale, the organisation must create a data-centric culture from top to bottom. This might sound a bit like management speak, but in reality, it simply means ensuring everyone knows that to make the best decisions, they need to be based on data and insights. It also requires investment in a range of specialist roles, such as data engineers, data visualisers and data analysts, who identify, collect, organise, study and report on data from different systems to provide business insight. These resources cannot be siloed in a think tank bunker somewhere in the organisation. No, they must be integrated with business teams in the operation, with access to change and process experts to be effective.
Making sense of the facts
But of course, this is easier said than done. Data isn’t always easily understandable – even to data analysts. Also, most businesses today generate millions of real-time data points that no human can ever hope to make sense of in their working day. And nor should they try. It’s not what they’re best at.
This is where AI and ML comes in. These algorithms can be used to sift through data and present it in a digestible way. For example, in dashboards, maps and graphs rather than spreadsheets and tables. And as anyone in the industry will know, this software works incredibly fast. In theory, the latest techniques allow AI to learn and act at the speed of light. We might not have this speed in widely available AI software yet, but it goes to show just how quick it can be.
To unpack this a little more, imagine a customer service team working in a large business. The agents will be creating vast amounts of data all the time. Some of it customer data, some of it data about themselves. The latter might include information about their own work patterns or performance, as well as when customers make contact and through which channels.
With the right tools, AI can sift through it all almost instantly and make sense of things by noticing the trends and stress points, like peaks and troughs in work, whether someone needs a break, or if they might need some extra help or training. If presented to a manager on a dashboard with recommendations, they can make informed decisions about what to do rather than relying on gut feel. In some cases, these recommendations can also be automated and the manager simply informed, saving time and effort with the same outcome. This can have a positive knock-on effect on customers and team morale.
Automating the tasks we’re not best suited for
So, it’s clear how data can be found, understood and presented to help people make better decisions. The next step is adding automation to the mix. Imagine again the example of a manager with many dashboards in front of them.
The manager could spend all day watching the graphs and charts. Waiting for a red flag to show up or constantly taking small actions with the team. Perhaps giving someone a break or rerouting work. Alternatively, automation software could do it for them, using the insight gained from the data and AI. Someone’s taking longer than usual to respond to a customer? Perhaps they’re tired and struggling. Allow the automation to schedule a quick break for them. Poor customer feedback or a bad NPS comment? Automate some support actions like a refresher training programme or a targeted coaching session.
Automation can take on nearly any manual task – the applications are endless. It can process invoices in finance teams. It can route customers to the most appropriate agent. It can collect information and put in in reports. Or triage emails into a department. Coupled with AI, it’s like having digital assistants to take on the time consuming and repetitive activities that sap time and stop us from doing our best work.
The future is people-centric
While these technologies are nothing new, they do continue to advance at pace. This presents the opportunity to leverage them to help solve some of the biggest challenges we face in society as well as in business. But, we will only succeed when we remain as the masters of the technology, not the servants. Using AI and automation to empower people, not replace them, allows organisations to be data-driven yet technology-enabled and people-centric. Where these pieces of software are used as tools to help humans do their best work and remove the drudgery of manual tasks.
And it makes complete sense. Because there will always be a moment of truth when a human must be involved at a crucial point. An automated process might take someone 75 per cent of the way, but a person needs to complete the rest. And if they can put all their effort into that 25 per cent, the result will be a better outcome for the employee, the customer and the organisation that brings them together.
Ultimately, those who try to remove people from the equation are destined to fail. Because by our nature, we all want a human touch and people will always be the most critical resource any organisation has. The key is to empower them with intelligent technology to achieve more.
This is why data, AI and automation will never replace humans. Fact.
About the Author
Matt Rumins is European Head of Customer Success at Intradiem. Intradiem provides Intelligent Automation Solutions for customer service teams. This real-time automation unlocks the value of your center data to maximize productivity, engagement, and customer experience. Our patented AI-powered technology processes the massive amounts of data generated by your center – in real-time – and takes immediate action to support staff working in the center or remotely – every minute of every day. This intelligent technology has been built on the management best practices of dozens of the world’s leading brands, and it continues to become more powerful every day.
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