Improving AI using Kaizen Methodologies Kaizen 2.0
Have you ever tried a foolproof diet on its promise of overnight success and the 10 pounds you’ll drop right away? After a number of conversations regarding customer retention, NPS, and ultimately increasing bottom line revenue, key decision makers only had one thing to say in regards to how their organizations would reach their goals, “we’re going to implement AI”. But when asked what their plan was to do so, each seemed to think by moving quick and diving in deep, usually into pockets, this “AI” thing would figure itself out. AI in the business world today is seen as a foolproof diet.
AI is easier to harness than ever before, but it remains incredibly complex. Properly utilizing AI requires an investment in skillsets. Instead of attempting drastic changes in the hopes of achieving immediate positive results from fancy new tech, your organization might find it best to step back and peruse a Japanese concept known as “Kaizen”, or Lean Six Sigma. Lean Six Sigma is a methodology that relies on a collaborative team effort to improve performance by systematically removing waste.
How do these concepts Apply to AI?
An intelligent assistant can present in realtime the most potent images, copy, and product information for a personalized shopping experience. Companies can conduct A/B testing automatically by feeding the IA webpage click rates and statistics, varied copy and images. Soon, this will also include customer’s spatial and eye tracking information. An intelligent assistant driven E-Commerce site not only personalizes itself to customers, it discovers improved means to market.
Though the human brain’s ability to learn is incredible, the process can be quite messy. We all had to fall down a few times before learning to safely ride a bike. Each failure and success is recorded in our memories and the experiences built upon as we learn. Machine learning follows a similar trial and error process, and while useful for improving AI over time, the process can be messy and prone to errors.
Human learners are keen to adapt to variances in data. We can learn to ride a bike on the sidewalk in front of the house, at the park, or in a local parking lot. Where human learners lag behind machines is processing large amounts of numerical data. Machine learning algorithms can parse through numbers incredibly fast, even simulating thousands of scenarios in parallel.
Unfortunately, sometimes the smallest variances can throw off a machine learning algorithm. Machines have yet to master the art of adaptation without intervention by their human creators. An AI when applied to a specific problem can be powerful, but requires human-in-the-loop interfaces to help it adapt to variances. Lucky for us, established industries have been wrestling with variance for a while now.
Lean Six Sigma
Lean Six Sigma, in particular, aims to improve computing process performance by reducing variance. Reducing variance helps manage your process targets, and ensures you reach your goals. Lean Six Sigma methodologies strive to deliver hard, accurate data on which to base decisions.
The ideal machine should be an efficient one, after all, and reducing variances in data with methodologies like Lean Six Sigma can help drive improvements in the AI field. For today’s AI and machine learning, accurate, reliable, and valid data is the best kind, and it makes your machine learning algorithms more effective and smarter for your business.
The data cleansing phase alone is not sufficient to ensure the accuracy of machine learning.When noise and bias exist in input data, even a relatively smart AI can find itself on the wrong path. Lean six sigma variance reduction can improve the accuracy of machine learning results. Companies need to review underlying business processes and reduce input variance to ensure their AI is accurate, reliable, and effective for achieving business objectives.
Like an effective diet and exercise plan, businesses can succeed with AI by improving 1% each day. Small-scale improvements compound on the previous day’s accomplishment. At first, the changes seem inconsequential as AI aides with simple data crunching tasks. Gradually, you’ll start to notice improvements as inefficiencies are erased. Over time, there will be profound positive changes as your business enters the third wave with employees augmented by AI using Human-in-the-Loop systems.
We invite you to meet Hoover, our Intelligent Assistant learn more about swivl, our Kaizen based Human-in-the-Loop AI customer success solution, and how AI can empower your team and your business.
Scale your Customer Success with Artificial Intelligence.