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What we’re learning about AI use cases

Over the past few weeks, we’ve intentionally driven conversations with our customers towards the potential applications of AI within their organisation to drive business outcomes, productivity and efficiency. Throughout June, we met countless technology leaders through our events in partnership with the UK IT Business Leaders, The UK Contact Centre Forum and with the Contact Centre Management Association. We’ve also been working with our partners, old and new, to continue our own education on the AI technology market and understand where platforms and technologies can deliver against specific outcomes.

Companies are in AI education mode

The overwhelming consensus from the conversations throughout the past few months has been one of education. Many of the leaders we’ve spoken to have talked about the impact of ChatGPT on either their Private Equity leadership, their own board and CEO, or their Senior Executive team. The implications and huge hype in the media have filtered into these leadership conversations, which in turn is leading to a somewhat predictable outcome - that AI is going to have a profound impact on their businesses. In time, this may be true, but the impact of this expectation on department leaders can be overbearing for some. 

The reality is that whilst most are continuing to educate themselves on the benefits for their own specific goals, there is still a great amount of FUD (fear, uncertainty and doubt) about the impact in the short term. CEO’s of large businesses see AI as the potential to be both a huge competitive advantage but also a huge risk. FOMO seems to be playing a part, with senior leaders concerned that over a period of time their competition may gain an advantage by the successful use of AI at the expense of their own hesitancy.

Building AI use cases

We see our role as technology advisors as one of enablement around the practical use of AI within companies. Most interactions at this point are to try to understand where a mini use case can be implemented to prove value without too much interruption and upfront cost. The technology plays a part in this, however it also comes down to culture and process. Organisations with a technology backbone, for example cloud native or platform businesses, are naturally better placed from a culture and skills perspective to take a leap. For example, these organisations may choose to implement their own generative AI by working out how to build a secure LLM (Large Language Model) internally using either Open AI or another Open Source solution. Other, more traditional organisations could look to partners to provide a platform (potentially based on Azure or AWS) which will then host a model to train on their own data in Teams, Slack, ERP and the unstructured documents throughout the organisation.

Predominantly, the leaders we’ve spoken with are looking for help in trying to understand where mini use cases may be developed. We see this as a three stage process. Working to develop an objective, then planning what the approach to this might look like, followed by a full understanding of the impact and return on investment. Typically this may be completed across a number of departments, customer service, marketing, legal, finance to understand a set of use cases to prove various concepts. At this point a business case can be raised for any investment required to prove the concepts.

Data is the fuel

One of our partners has a great phrase… “AI is the rocket ship, but data is the fuel”. Without the data, there is no real application, so we’re working with areas of the business that produce enough data (structured or not) to enable an output to deliver value to the business. This naturally leads to customer experience (or more accurately the contact centre) where traditionally organisations have held enough data on their customer interactions to deliver value. It’s also an area where the use case can be quantified through productivity of the customer service teams or improving the level of service, sales conversion and ultimately brand loyalty (NPS or CSAT). 

A common misconception among leaders, and in fact a contentious conversation at one of our recent events, was the level of threat AI poses to jobs in these departments. In almost all cases, AI is the co-pilot to drive employees to be more productive in their work day-by-day. It’s not there to take their job. Not yet, anyway. In the customer service case – data needs to be at the fingertips of an agent in a call centre for them to offer a superior level of service. They become better at their job because of it.

Other industries where we see a great use case are those with plenty of documentation – legal and accounting are obvious cases. The ability for lawyers and accountants to use conversational AI to triage documentation is a relatively straightforward use case and can be implemented at speed. Larger enterprise organisations with more complex data structures, and in many cases data science teams, are working on building core AI platform capabilities alongside Machine Learning to drive outcomes like predictive maintenance in manufacturing. It’s worth remembering that AI and ML isn’t new, it’s just becoming more accessible to the market due to the consumerisation of the technology and the proliferation of new software/platforms on the market. Understanding which of these is relevant for department leaders who are not technologists is the next challenge.

AI Discovery Assessments

In the meantime, we continue to work with our customers to help them understand how the application of AI can help them, and as such we’re building an AI assessment to package up a set of deliverables to help department leaders build use cases to take to their senior teams. We’re really interested to hear from anyone who is investigating this area to see if we can help.