The pace at which changes from new technologies are occurring within the business world continues to increase at an astonishing rate. But narrowing it down to what some authors have identified as “disruptive technologies,” the one that keeps me awake at night is artificial intelligence (AI).
Harvard Business School professor Clayton M. Christensen presented the concept of disruptive technologies in his 1997 book “The Innovator’s Dilemma” when he separated technology into two categories: sustaining and disruptive.
In my interpretation, sustaining technology can be defined in simple terms as what the majority of us normally do and experience in our day-to-day: improve an already established technology. Disruptive technology, however, is defined as the type of technology that is not completely refined, sometimes might have performance issues because it is new, and could be appealing only to a limited audience as it is not “proven” yet.
With this definition in mind, we can look back through the history of mankind and find such types of disruptive technologies exemplified in the early days of our species. Examples include control of fire, writing, and the wheel. More examples closer to our current time are the PC, software operating systems, cloud computing, and social networking.
Even though the phrase “past performance does not guarantee future results” is often found in financial documents (especially the fine print), it does not seem to apply to disruptive technology in the same way. If there is one common factor that can be taken from all the examples of disruptive technology it is that its performance often earns it a place in the future as a current standard.
In other words, many types of disruptive technology that have emerged are here to stay. One prime example is artificial intelligence. Artificial intelligence has now arrived in all the fields in which NEBB companies and NEBB professionals deliver services, and the best way we can predict the future is to create it.
We have invited guest speakers to our yearly strategic session to talk to us about cloud computing, data mining, and artificial intelligence, amongst other topics, so that we can be a little bit more savvy about these topics.
Based on what we have learned, we wanted to share our simple definition of artificial intelligence for our company: Artificial Intelligence = Data x Knowledge
Armed with this simple definition, we have set our firm up to experience what we think AI is in our business segment and wanted to share two examples of (internally focused) strategic initiatives that involve AI:
- The business opportunity does not stop when becoming NEBB certified. AI is quite disruptive; therefore, NEBB members and firms have to keep learning, and probably the first experience we have had with AI in our business segments has been with the output of building monitoring systems (BMS) or building automation systems (BAS) and how it relates to the TAB, CPT, and CX disciplines, as well as facilities equipment in general. More often than before, we are receiving inquiries from our customers regarding issues about the conditions of their facilities and/or drifting in critical operation parameters. Upon review of these inquiries, we supported these customers by routinely requesting data trends from their BMS/BAS system to analyze the different scenarios and provide back a list of suggested corrective actions. This type of exercise certainly has a “cost” to us, as we need to invest engineering time in the analysis, but at the end of the day, the payback for that time invested is keeping the business of these customers. Sharing knowledge insights with customers helps to continue building and strengthening our relationship with them.
- Next comes the second area in which we have purposely entered AI in our TAB, CPT, and Cx segments: incorporating data analysis into the general services provided so that added value can be extracted from the data obtained by our firm. Two examples are:
- A customer had asked us to do TAB work in a warehouse space. Upon completing it and finding that the customer was entering the validation process of this storage facility right afterwards, we offered our support. Given that we were already on site, we could process the data obtained from the validation dataloggers they have installed (which is something Cx firms do anyway) and extend our value offer. We had to research a bit in order to understand what “mean kinetic temperature” was besides the regular calculations of maximum, minimum, and average, as well as dust off the statistical concepts of Cp (Process Capabilities)/Cpk (Process Capability Index) that we were taught in the University, but the most insightful learning was to correlate how the uniformity of supply airflow obtained during TAB reflected in the data graphs from the validation process, enabling the system perspective for our team.
- Another one of our customers was experiencing issues with viable particle spikes inside their cleanroom. Upon being contacted, we offered to perform a cross-analysis in two dimensions: time and spatial distribution of our testing data for the past three years (flows, non-viable particles, and differential pressures) and overlaying the HEPA filters layout on top of their manufacturing equipment layout. Upon completing this analysis, it was possible to offer the customer ideas regarding simple equipment re-layouts, increasing the air changes per hour without affecting the differential pressure level, and clearing “paths” to the return air intakes that have helped improve performance and enable a data-based proposal that was highly appreciated.
By no means do we pretend to be experts on the interaction of AI with NEBB disciplines, but if there is one thing we have clear on our strategic radar, it is that we need to get onto the AI bus and pay our ticket to get on board—otherwise, someone else will. For us, paying the ticket has meant:
- Take risks to do things we have not done in the past.
- Become better at data management and data processing.
- Become a “translator” of the “story” data is telling to actual actions to be performed by our customers.
- Exercise more and more deductive thinking: start with the big picture and end with the tiny details.
As with any other businesses, some of the experiences have worked, and some have not, but from both cases we are able to develop better ways of doing TAB, CPT, and Cx. And to us, that is, in essence, how disruptive AI has been to the NEBB disciplines we focus on.
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