It seems that every day we hear of a new AI use case. Could AI automate financial advice, write music, diagnose disease, or my favourite “Could AI Spot Obesity from Space”? If you base your views just on reading the news, you could be forgiven for thinking AI is some sort of omnipotent panacea that will solve all of your business problems and maybe even some of our society’s as a whole. Now, as someone who follows the old adage “if it sounds too good to be true, it probably is” I suggest reading these stories with a healthy pinch of salt. Of course, AI projects aren’t a panacea and while I agree they will be disruptive and transformational, I think the hype risks, to use Gartner’s famous terminology, pushing all AI technology into the ‘peak and inflated expectations’ and directly to the ‘trough of disillusionment’. So instead of talking about what AI MIGHT be able to do, let’s instead talk about what value successful AI projects are bringing to customers today.
AI Projects: Opening up New Markets
Successful AI projects expand capacity and open up new markets for businesses. As an example, Natural Language Generation (NLG) software is playing a major role in disrupting the fund management space and allowing these businesses to enter new and previously underserved markets. In Europe, when a fund manager sells a fund, by law their company must write a quarterly analysis explaining how the fund preformed, why it did well/badly, and the outlook. This requirement stops companies from entering the small or bespoke fund market because if they build a bespoke fund for a unique customer, then they must somehow write an analysis with the same level of expertise they offer on the bigger off the shelf funds. Despite this the bespoke fund market is taking off in other parts of the world and fund management companies in Europe want to tap this new market.
For one Yseop customer, Yseop’s platform connects to the fund performance data and automatically writes the fund reports on a monthly basis. Because the tool is automating the process, it is able to write the reports for pre-packaged funds as well as 100% individual funds and all in English and German (a requirement for this customer). This opens up a new bespoke fund market for the customer and also allows them to service smaller investors with the same level of personalization, opening up yet another new (and underserved) market for our customer.
AI Projects: Realizing the Promise of Big Data
Not Unlike “Artificial Intelligence” the term “Big Data” is firmly entrenched in what Gartner calls the “Trough of Disillusionment” with some going so far as to write Big Data’s obituary. With Big Data we see a huge disparity between what we hope to achieve than what is possible. While a Big Data project is meant to use the vast swathes of data we collect to make smarter, faster decisions, often the results are hard to make practical use of, not understandable to end users, or simply deliver too much data to comprehend. For a deeper dive into Big Data, check out our blog post “Why Big Data Projects Fail”.
However, using Big Data in combination with AI could be the solution, taking complex findings and turning it into actionable intelligence. But how are AI Projects adding value? Here we are seeing a mix of AI technologies with conventional analytics tools sometimes using Machine Learning and pairing with Natural Language Generation. The work flow is simple but the added value is immense. First the software analyses the vast amounts of data, identifies patterns, before making suppositions and then explain those suppositions in plain English.
AI Projects: Your Salespeople on AI
In general successful AI projects connect humans and machines allowing each to do together what they couldn’t alone. One of my customers, a Private Banking company, connected our platform to their CRM system and their customer transaction data in an effort to make use of their existing data. The challenge the bank faced was the need to enter new markets without adding new advisors and all while offering an even more personalised service.
The Yseop Platform runs at the point of customer interaction, writing a pre meeting memo, prompting the banker to ask for relevant data, then using the new data, existing data and product catalogue, to build (on the fly) a proposal for the customer. Yseop also automates the follow up after the meeting and writes the proposal for review. Yseop saves the customer time and money, opens up new markets and puts existing data to work allowing for unprecedented levels of service.
If you separate the truth from the hype, you can uncover successful AI projects that are in production today and delivering immense value for business. Generally these projects augment human capacity, open new markets for the business and leverage existing data reserves.
