AI isn’t taking over the (business) world just yet – here’s why…
Cape Town, 1st August – Ryan Falkenberg, co-CEO & co-Founder, CLEVVA – AI has become, in a very short time, an established part of business. Much time and energy has been spent on evaluating the impact that this digital disruption will have on business and specific industry sectors. Significant sums have also been invested in testing various forms of AI – in most instances targeting improved operational efficiencies through robotic process automation or improved digital self-service through chatbots.
Few will have managed to move beyond the pilot phase into full scale production, however.
This is, for the most part, due to legacy infrastructure issues. Data continues to be stored in different places and in different formats. Many operating systems are not fully integrated. And many decisions and actions continue to be taken by staff, outside of systems. If key pieces of the puzzle are not connected, it becomes difficult to automate these. To connect all of said pieces usually requires significant changes to systems, processes and people. That is why disruptive businesses tend to be started from scratch rather than emerging from existing business models, unlocked by the application of AI.
The disruptive implications of AI remain daunting for most established businesses, which slows down adoption. Breaking down what has worked and replacing it with what should work takes courage, leadership, time, and money. In many cases, it is forced on companies as they scramble to respond to a disruptive market entrant that threatens their very survival.
So how do executives prepare their organisations for the digital era while driving improved performance via their existing business models? How do you migrate a legacy business model to a digital reality without running parallel business models or engaging with a transformational overhaul from day one?
Take the contact centre. It’s tempting to get rid of all your agents and ask your customers to self-serve via chatbots. The financials make sense. But, this means you have to build a chatbot capable of answering all customer queries in a way that satisfies the customer. Doing this in an Innovation Lab and hoping the digital team will get it production ready is wishful thinking. There are simply too many possibilities to capture using traditional decision tree coding techniques, and the amount of rich unstructured data is seldom enough to achieve acceptable predictable outcomes via machine learning. These projects struggle to get out of pilot phase simply because the risk of a poor customer experience is too high.
A more pragmatic approach is to stick with your existing human interfaces and augment them with digital intelligence. This gives you room to learn and make mistakes, because your staff can step in when your digital logic is found wanting. It means approaching AI from a both/and position, not an either/or one. In other words, including, not excluding staff in your digital mix. Only once you have perfected your digital logic do you then look to adopt purer forms of digital autonomy.
This approach can help realise that mythical call centre creature – the super agent, capable of handling any and every query, changing from business unit to business unit or product to product without missing a step.
Call navigation technologies specialise in the capture, maintenance and deployment of structured, expert logic that can navigate agents through real-time calls, ensuring that, based on the customer’s specific context, they ask the right questions to diagnose the right problems, and then identify the right solutions that result in them taking the right actions. In addition, this expert logic can also feed self-service bot interfaces, guiding a digital conversation in line with structured, consistent and compliant advisory logic.
This can also be used in sales, technical support and any number of other operational areas. By initially building digital experts that augment, not replace staff, it allows you to realise significant business benefits without being forced to transform the business model to make it work.
And not only will it enable staff to do more with less training, but you start building critical data that helps you shape and optimise your digital logic. As the logic gets more robust and accurate, you can look to adopt more digital interfaces where relevant. By that time, you will have empowered your staff to think and operate within a digital world; one where prescribed decision logic will increasingly be tackled by technology and where the human role shifts to perfecting the behaviours that help differentiate the customer experience.
Intelligence augmentation (IA) offers existing businesses a very pragmatic first step into the digital era. It focuses on getting the back-end logic or intelligence perfected before you try perfect the front end experience. It also allows staff to increasingly develop their EQ given that their IQ can increasingly be supplemented digitally.
2018 is the year where AI is moving out of the labs and into the mainstream. And intelligence augmentation solutions are one of the key ways existing businesses achieve this outcome.
Ryan Falkenberg is co-founder and CEO of CLEVVA, an augmented intelligence technology that allows non-coders to capture expert logic into Navigation Apps. Navigators remove the need for people to learn decision-making formula, guiding them through any required decision and action in real time. Termed ‘Artificial Intelligence for People’, CLEVVA gives people access to a digital brain so they can rather focus their learning and efforts on more differentiating and value-adding behaviours. www.clevva.com
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