AI and Digital Feedback Loops
I am here in sunny…aka “hot” Las Vegs for the 2018 Microsoft Inspire and Ready conferences. Despite the heat I love this time of year for the opportunity to meet with many of Microsoft’s great partners to discuss Artificial Intelligence (AI) and the opportunities to work together in this area. Over the last year I’ve had hundreds of conversations with customers and partners on AI and what it can mean for their business/industry, which I documented in my last series of blog posts (see some of the themes here, here, and here). With the ongoing digitization of many line-of-business processes there is a growing wealth of structured and unstructured data that can be used to provide insights that improve systems and processes across core business functions.
While we have often taken an “infrastructure first” approach in the software space (e.g. client, client-service, Internet, etc.) with a new generation of technology building blocks emerging, we will see a transition to an “experiences” and “processes” first approach in the coming decade. The ability to combine core business data with a new generation of “observation-driven” data signals that will provide the starting point to drive intelligence into all of our business processes. This shift in combination with scalable cloud compute and powerful AI algorithms means organizations are better equipped than ever before to use their data to improve business outcomes, and I believe these data-powered ‘Digital Feedback Loops’ will fundamentally change how companies innovate business processes. When we couple this thinking with the AI Patterns we discussed before we can begin to see the alignment of AI with these core processes/loops.
In most cases, organizations start small by creating a feedback loop around a core business process. For example, an industrial manufacturer might begin using an instrumented manufacturing line to predict breakdowns and optimize production flow. Or a bank might begin aggregating its call center activity to engage customers with more personalized offers. However, the true power of this model stretches far beyond a single feedback loop. The beauty of our growing data and AI capabilities will be the opportunity to break down silos and synthesize data across product, customer, employee, and operational business processes. In this sense, the most powerful feedback loops will be the ones that are interconnected and that reinforce each other by bringing together disparate datasets.
Customer + Product
One of the most common examples of this interconnectedness is between the processes that support customer engagement and product development. Delivering good products and intelligently engaging customers are deeply linked and complementary. Customer understanding can always help a company better tailor a product for a given market/customer, if the feedback is woven into product planning and creation. In addition, this same customer feedback can be used to deliver more effective and personalized sales, marketing, and service.
When Progressive, one of the largest auto insurance companies in the USA wanted to drive deeper engagement with their customers they created Flo, a chatbot for helping customers with their questions. This is an area where we see the “Virtual Agents” pattern being one of the first implementations of AI in the customer feedback loop. Recognizing that many of their customers were using their mobile phones, Progressive used Azure Cognitive Services and Bot Framework to create the Flo Chatbot and made it available through Facebook Messenger. Now customers can get their questions answered, get quotes, and chit chat with their bot. As Progressive grows Flo’s knowledgebase over time across many customer interactions, it can use this data to improve Flo’s capabilities and even optimize the products (insurance offers) it delivers based on evolving customer needs.
”One of the great things about Bot Service is that, out of the box, we could use it to quickly put together the basic framework for our bot.“
Matt White: Marketing Manager, Personal Lines Acquisition Experience, Progressive Insurance
Developing useful bots can be challenging due to a combination of how hard it is to answer open domain questions and computers lacking conversational skills. Developing a chat bot like Flo, with its unique personality and the variety of questions that it needs to handle can be difficult, but with tools for language understanding (LUIS) and questions and answers (QnA Maker), it is easier to create innovative bot experiences quickly. Microsoft has also deeply researched social bots which provide great insights into how to build a bot that people feel comfortable interacting with. For example, Xiaoice in China with over 500M+ users is trained to have open domain conversations in a natural way, with one of the longest conversations lasting over 29 hours.
Where Progressive began adopting its feedback loop with a focus on customer engagement, Tetra-Pak has taken a more product-centric approach. One of Tetra-Pak’s key business lines provides complex machinery to help food companies safely package their food. Cows typically provide 6 gallons of milk per day that needs to be packaged and delivered to customers promptly. Providing reliable products is extremely important to Tetra-Pak as any problems with the packaging process or breakdowns in the packaging line can result in large amounts of spoilage.
To manage these risks, Tetra-Pak instrumented its products and started using Azure to capture the telemetry. By analyzing this data, Tetra-Pak can proactively predict product breakdowns and identify the repairs needed to prevent any issues. Not only does this help Tetra-Pak better understand its own products and opportunities to improve them, but it also allows Tetra-Pak to engage its customers more intelligently. Service engineers can be proactively dispatched and can be armed with all the right information and tools they will need to address the issue at hand. This not only drives better outcomes for the customer, but it provides them with a better experience as well.
Just as the product and customer feedback loops are linked, so too are the feedback loops companies build around their employees. For example, Tetra-Pak has taken the scenario above even further by extending its investments in product instrumentation to empower its employees. By using HoloLens, all the details on the customer and telemetry from the asset being worked on are available through the headset in real-time. Ambient Intelligence is the AI pattern where AI is applied to physical space and when telemetry is combined with this pattern it makes easier for the service engineer to help their customer. Not only does this help with lowering the time needed for a repair, but it reduces downtime and helps the service engineer gain more credibility with the customer through easy access to key product knowledge.
In the most compelling scenarios, companies are empowering their employees not just with the relevant data they need, but with AI to reason over that data and assist where needed. Publicis has 80,000 employees and like other large organizations, finding the person with the right skills that you need access to can be a difficult task. To help solve this problem, Publicis teamed up with Microsoft to develop Marcel, an AI powered platform for connecting Publicis’ employees. This is an example of the pattern of “AI assisting professionals” which Publicis will accomplish in 3 ways. First, AI will be used to identify the skills and connections of each employee and then make it easier to search for and find the person that has the skills needed to accomplish a specific task. Second, by having a complete knowledge graph of the skills and abilities available within the company, collections of people that show unique combinations of expertise can be surfaced for specific projects. Third, is the ability to create new and ad-hoc working groups will allow employees can now participate in projects beyond their immediate boundaries creating more organic opportunities for employee growth.
The interconnected power of the feedback loop also extends to operations. As companies build increasingly robust data assets around their products, customers, and people, opportunities quickly appear to deliver products & services more efficiently and reliably. Think of how data + AI has changed the Taxi industry, where street-side hailing is often replaced by apps that use location data and complex algorithms to optimize the matching of riders with drivers.
Jabil, one of the world’s leading design and manufacturing solution providers provides a powerful example of how the Digital Feedback Loop can also help companies optimize their operational processes. Using Azure Machine Learning, Jabil can analyze a given manufacturing process and identify high-risk areas where it will fail. By taking existing defect data and using ML techniques to create a custom “inspection model” Jabil has “seen at least an 80 percent accuracy rate in the prediction of machine processes that will slow down or fail, contributing to a scrap and rework savings of 17 percent,” said Clint Belinsky, vice president, Global Quality, Jabil. (source)
Using data from sensors to optimize and maintain the health of a system is one of the ways to use the autonomous systems AI pattern. First step is to instrument your system or process to get key health metrics and then to use that data to trigger an automated system to fix problems as they arise.
Creating the Digital Feedback Loop
Although I provided a couple examples of how customers are using Microsoft AI patterns and technologies to drive feedback loops across customers, employees, products, and operations; this just the start. At this point in time it is too early for AI to solve every problem, but it is too late not to get started. Whether you are attending Inspire in person or online, take some time to attend some of the AI sessions and think about how some of these AI technologies can be used for your digital feedback loops. I am looking forward to seeing what you build.