We read a lot about the great potential of Artificial Intelligence and Machine Learning (AI/ML)*. But there’s not a lot written about the pitfalls of dumb software.
Businesses use software to operationalize and scale their decision-making processes. This can range from internal operations or customer management platforms to external product recommendation engines for shoppers. Ideally, the software perfectly optimizes each task and each user to drive the most value for the business. Sounds pretty simple.
One problem for many businesses is that their decision-making software is relying on basic decision trees, A/B testing, case statements, and conditional logic tricks. The decision-making isn’t coded to learn and react to changing conditions. It operates under the assumption that if a change is needed, a human process will take over to analyze the data, evaluate that need, design a plan for a change, test out that plan for change and then put that change into the software. But in today’s world change doesn’t wait for a two-week sprint to complete (it’s too slow). The software we think of tried and true just can’t keep up. It’s tired. It’s true.
Rewire Tired Software
Enter AI/ML with its power to continuously sift through data, find patterns, and constantly optimize decisions in changing conditions for individual customers. Amazing, right? It’s no wonder that 83% of executives cite AI as a strategic priority. So why is not every business using AI/ML to make better decisions, faster? Well, some are – certainly, we know about the massive investments in AI by Google, IBM, Amazon, Tesla. These investments have created market-moving value and growth in their businesses. But few businesses have the time, agile cycles, and resources to throw data scientists and developers at every potential use case. They read articles about AI/ML focusing on the complex, the massive, or the bizarre and think, “No, thank you.” AI has been thoroughly defined as costly, complicated, and time-consuming. But that’s about to change.
The Time is Now
The best product teams and leaders are masters at doing more with less, regardless of the size of their teams or business. Same with the next great advancement in AI/ML resources. We have reached an inflection point, where we can do a lot more with less. The costs of crunching data are rapidly decreasing, thanks in part to marketplaces that assist in data aggregation, and decisioning models become increasingly standardized. Making better decisions for every single use case isn’t a promise for tomorrow; it’s a reality today.
An e-commerce shop or fintech firm doesn’t need the AI technology used to detect interstate money laundering activities to determine the best promo offer or payment plan to present to each user. A content creator doesn’t need to go deep on Tensor Flow to present personalized and relevant videos to their audience. What everyday business leaders need is the ability to optimize each pivotal moment in a path to ensure the ideal outcome. In other words, to make better decisions faster, every time.
All executives need for the next iteration of AI/ML technology is to know their ultimate business goals. They don’t need data scientists, data pools, large budgets, large teams, or MLOps. Soon businesses will be able to add AI in an afternoon. By the next day, the software will have started to learn. (If “soon” isn’t soon enough, how about now instead?). Software that can learn turbocharges capabilities by empowering businesses to offer increasingly targeted, personalized, streamlined options; ultimately driving growth and increasing value. Who doesn’t want that?
You don’t need a sledgehammer to drive in a single nail. You don’t buy training time on a TPU to decide the best time to present a ‘buy more lives’ offer to a mobile gamer. You just need a little AI.
*I use AI and ML interchangeably, and I am sure some of you may rightfully quibble with that. Technically AI is the umbrella under which ML sits. Meaning AI refers to machines acting “smart” and ML is a technique or application of AI where the machine learns and adapts to experience. There are even more definitional nuances, if you ever want to go down a rabbit hole.