Threading the Enterprise Needle with AIoT

An Enterprise is a matrix of horizontal and vertical functions, bound together by one common thread – Information. Information is a multi-facet dimension with elements of content, context, and time. These dimensions are tightly intertwined and cannot be treated in isolation. Content without context, content or context without a time element is flawed information since it is of no use or if used causes more harm than good.

• Content without depth or detail is mostly unusable information. Content has value if supplemented with depth of detail and clarity of function.
• Content without a context can cause misinterpretation, confusion or misuse of the information provided. Context provides perspective, baseline reference and a focal point.
• Time is a perishable item and therefore must be treated accordingly. Information that is dated or with lag has passed its expiry date and is therefore not useful.

Let us assess a Service Engineer Scenario. A Service Engineer is tasked with responding to a maintenance / service ticket of a critical product of an important client. A ticket arrives saying “Product not working”. This appears, of course, like a ticket raised manually.

The first reaction is a complete blank. The questions race across one’s mind – What is the product? What is not working? Where exactly is this product? What exactly happened? Who is impacted?

The second reaction is the pain one has to go through, the pain of asking the end client these same questions and face the brunt of the client’s frustration and anger.

The third reaction is agony. The work of analysing the issue, talking to multiple stakeholders, visiting the site, conducting investigations, connecting with the development teams, conducting root-cause-analyses, arriving at consensus with all parties, documenting and reporting the action plans.

The entire process could easily take a couple of weeks, maybe more. A very oiled machinery would still take a minimum of a week. This is operationally inefficient, manual intensive and the most impacted person is the customer, who is in the dark through the entire process.

AIoT potentially could short-circuit the entire process to a few seconds tops. Enabling the product with IoT technology and using AI to identify the patterns, assess potential risks and provide actionable insights automatically without any human involved creates a highly efficient workflow process.

IoT enablement provides content rich and contextually relevant time series data. AI is then taught to recognize patterns in real time. Therefore, it is a simple workflow to identify the issues, analyse root-causes, prepare the report, define actionable insights, suggest actions, and deliver the information to the customer in an instant. No manual intervention necessary.

An Example: Looking at the pattern of past service calls and resolutions, the AI engine could automatically provide a solution to the problem and trigger insights report back to product engineering about a worrying trend in a certain design element. This pushes continuous product development processes as well leading to instant threading of data from one end to an actionable topic at the other end.

• Begin with the experience you would want the customer to have and the value this experience brings.
• Assess the kind of AI and patterns that need to be developed to generate this value.
• Develop a design of the data and data threads necessary to generate this value and enable the patterns for AI.
• Finally design the product that will sense and generate this data

The essential rule, therefore, is to design a data enabled digital thread AIoT model around the customer delight business use-cases.

• It is important to develop the linkage of every business function in an enterprise.
• The linkage must be based on a digital thread of data supported by an AI engine for detection of clearly defined patterns.
• The pattern also is enabled with provision for efficient and speedy decision making.

This digital thread coupled with a data lake strategy and AI enablement for each discipline creates a high responsive architecture. This promotes organization process efficiency, analysis, and decision automation and eventually an AIoT driven decision-making culture.