Oracle jumped into the AI and machine learning space for its CX products (a.k.a. CRM) and other applications like HCM at OpenWorld with an interesting difference—a huge data store to help educate the algorithms that work for you. Now we’re waiting for products to be delivered this year.
Machine learning depends on data about prior situations that the learning algorithms can use to get smart about a situation. Ten examples are good, 100 are better and generally the more samples there are the more refined a recommendation can be. That’s why machine learning never really ends. Like a great player or team, the learning and practice never stop and neither does the improvement. But it’s worth understanding that improvement beyond a point of basic competency will slow down regardless of what you’re modeling.
When you’re a kid you can make great strides in almost any sport but as you progress those strides become smaller and they’re harder won. Consider Olympic swimming or track and field where athletes try to shave fractions of seconds from world records. Often the difference between gold and silver can be an arcane difference in technique.
In business and machine learning, algorithms don’t stop learning for a very good reason—every new bit of data suggesting some fractional difference could be the harbinger of an evolving trend and the only way to stay abreast of that evolution is to stay current with the data. So you can quickly see that data is critical to the success of machine learning and that’s a big deal because few organizations possess all of the data they would ideally need to feed the algorithms that drive decisions.
Moreover, data quality is also a major issue and while a business might hold a great deal of customer data, its quality or lack—the duplications, misspellings, ambiguous designations, and incompletions—have, for years been the bane of data scientists and analytics users wanting to get information from their data.
Data quality is one thing that will distinguish Oracle’s Adaptive Intelligent Applications. Scheduled for delivery soon, Adaptive Intelligent Applications will work with customer data as well as Oracle’s Data Cloud, a collection of more than 5 billion consumer and business profiles, with over 45,000 attributes. The combination of a business’s specific customer data combined with this third party data will yield important insights that are unique to a business and its customers.
Businesses have always sought out fine differentiators like these solutions can provide to separate them and their rivals. Depending on the stage of market evolution that could mean product differentiation, value added services, product line extension—almost anything. The problem with all of these approaches is that they’re superficial. It’s all vendor, brand, or product centric because that’s all that a business could control prior to the development of very powerful computing and modern analytics and machine learning. If you wanted to peer into the mind of your customers you had to rely on gut feel—usually that of an executive who’d been involved in the industry for a long time.
The trouble with gut instinct is that it’s often wrong. The research that led to a Nobel Economics Prize for Daniel Kahneman—see Michael Lewis’s new book, “The Undoing Project”—shows that the rules of thumb or heuristics that we use in every day fast decision-making are often wrong or reveal a bias. Interestingly, since machine learning is definitely not human, it can avoid heuristics and biases and work the way we work when we concentrate and work slow and perhaps use pencil and paper. But the point of machine learning is to have the benefits of thinking slow and with a pencil but without having to do the work. In the process, machine learning is able to reach more users and prevent more incorrect assumptions from coloring business decisions.
To be clear this does not amount to a one size fits all approach to analytics. The Adaptive Intelligent Applications that Oracle has built also come with supervisory controls that enable users to fine tune their analyses to the specifics of a business’ needs. So the power of Oracle’s Adaptive Intelligent Applications will come from its well-crafted algorithms but also its Data Cloud. But the fact that it might prevent users from using an estimate or rule of thumb might turn out to be just as valuable.
Artificial Intelligence and Machine Learning (AI and ML) have taken the industry by storm with some saying they will usher in a new age of better business processes and customer orientation while others fret that automation will kill jobs. Both might be true.
There will definitely be jobs that no longer make sense for humans to do thanks to automation. Generally they’re entry level and not much fun but this begs the question of what, then, becomes an entry-level job. I took a briefing last week with, Conversica, a company that uses AI to do general-purpose triage for early stage sales leads. I am not affiliated with Conversica and I am simply reporting, but the technology seems pretty cool.
Conversica is essentially a bot that responds to things like email with appropriate information and then follows up. When a customer indicates a need to interact with a human, the bot makes the transfer. The bot is tireless and can work 24/7 so there’s a lot to like. This is a good example of automation replacing a human but I am told the bot simply makes it possible to redeploy the human to another job that requires real human thinking. I am sure it can.
Often when I see something like this it’s a new technology applied to an old problem and truth be told, that’s generally how new technology gets disseminated. For example, it has taken a long time for social media to establish a niche different from being a cheaper email platform. Some people haven’t made the leap in understanding but generally we’re there. So I fully understand employing modern AI technology to support the sales effort; sales is one of the first stops for new technologies.
But I’d like to divert your attention for a moment to some business processes that are nearly neglected where AI and ML could make real contributions. They would likely not replace anyone because a job isn’t being done in many cases. Customer loyalty is an area that could use some help, even the help of bots. Loyalty has always been a passive thing in which customers are expected to do something that demonstrates loyalty in the moment, rather than an active thing that businesses pursue.
We expect customers to do something loyal, usually making another purchase, for which we then give out rewards. That’s okay but it misses the point. Rewards are by their nature backward looking and loyalty ought to be something of the present and future. Say I advocate on behalf of a favorite product that may drive future business for the company. Often that’s not part of a business’ idea of loyalty and so it goes unrewarded even though it is a good loyalty indicator. Too bad.
Now imagine if you tuned some of your AI muscles toward loyalty. What if you had a bot that caught customers doing good things for which you rewarded that behavior? The reward, not associated with a purchase, might inspire more loyal conduct and all this could be done without human intervention. So this little exercise just 1) invented a job for a bot, 2) replaced no humans, but it did 3) improve business performance.
My point is that there are lots of incremental improvements like this example waiting to be discovered as we contemplate using AI. Many of AI’s early deployments will be like this, not very sexy but useful. That’s what happens on the far side of the hype cycle, after everyone’s tried applying the hot new technology to the oldest of problems. It’s maybe even after a lot of people have become mildly disenchanted with the failed promises that the new category couldn’t deliver.
I’ve seen this hype cycle movie before and I am wondering if we might be able to speed up the process. It’s really nothing more than imagining new processes rather than being happy pursuing a well-worn path.