To assure IT service quality, it's important to apply AI to Operations. This allows teams to focus on developing better services.
This is the first in a series of blog posts by Adam Frank examining the role of AIOps in delivering better customer experiences.
We here at Moogsoft believe that — in order to understand our users, our industry, and the future of technology — first we need to LISTEN. It’s very important to stay in touch with our users, future users, industry practitioners, like-minded companies providing complementary solutions, and people who are passionate not just about the future of technology, but the future of humankind.
All this input leads to great services… that deliver great customer experiences.
One of the best ways to do this is to get out there and travel to meet people face-to-face. I travel a fair amount, which always involves a flight, a ride to/from home and the airport, and a place to stay. I book and hire all of the flights, rides and accommodations using my trusty smartphone, which funny enough I rarely ever use as a phone. My experiences arranging travel — my digital experiences — are usually quite good. My last trip was exceptional. I traveled from my home to the airport, went through security, boarded my flight, disembarked the plane, traveled to my hotel, opened the hotel room door — all digitally, without talking to a single human.
These digital experiences are amazing customer experiences because they are always available and on-demand: when and how I want them. This is the world we live in. Great companies foster loyalty by providing amazing customer experiences.
Most things we do now are digital. This means that more of our experiences with companies or brands are becoming digital. The online customer receives a digital experience, good or bad. If that experience isn’t what they expect, they will move to a competitor, looking for a better experience. And they can move within seconds, with a touch of the screen.
Great Customer Experiences Depend on Managing Complexity & Change
Customer experiences demand change. They always have (see Figure 1).
In the 1970s, people wanted to know the features and functions of the product As the Western world transitioned to a service economy in the 80s, customer service became more important to buyers. As computer technology and telephony matured thru the 90s, alienated customers wanted to feel centricity from the humans at the other end of the line. The immediacy of Web 1.0 and later, social media, made personal relationships with products and brands more important. Today’s experience involves all these trends, delivered in an integrated way (i.e. an experience), consistently over time (i.e. loyalty).
Digital transformation demands customer experiences. Successful transformations deliver great ones. Companies that don’t change, die. It’s that simple.
According to a 2018 study published by growth strategy consulting firm Innosight, 50% of the fortune 500 will be replaced over the next 10 years. The average tenure of a company on the S&P 500 used to be 33 years back in 1965. That’s forecast to shrink to a mere 14 years by 2026. Stagnant companies are being more quickly replaced by new agile companies that deliver better digital customer experiences.
For Site Reliability Engineers, DevOps and IT Operations, managing the great services that deliver great customer experiences comes down to two critical factors. One, you have to manage SCALE: the scale of always-on, the scale of global, the scale of logistics, and the scale of Big Data generated by transactional systems. Second, you have to manage COMPLEXITY: the complexity of orchestrating mobile and cloud and virtual and ephemeral micro-services. Failing to keep pace with the complexity and rate of change in today’s on-demand and 100% available world threatens support for digital customer experiences.
The pace of change has never been this fast, and it will never be this slow again.
– Justin Trudeau, Prime Minister of Canada, Davos World Economic Forum 2018
In light of these twin challenges, how can we humans keep up and manage?
A Not-So-Short History of Artificial Intelligence
As this rather humorous evolutionary timeline shows (Figure 2), it only took us humans just shy of six million years to stand upright and learn to use tools, fire, speech, farming and the written word. Then, Artificial Intelligence arrived! A bunch of stuff happened between the dawn of civilization and now, but most importantly we decided it would be better if machines did the thinking for us (at least the tedious, manual and routine). Research and development into AI began.
We are now in another technology revolution (Figure 3). AI is a cognitive revolution, one which began in the 20th century, breaking the barriers and limitations of our intellect. Think about going to work or the market in a horse-drawn cart for a minute. The point is you don’t have to, because today we have self-driving cars and awkward conversations with Siri, Google & Alexa.
For a comprehensive history of AI, check out the AIOps Manifesto report, courtesy of AIOps Exchange.
We’re currently entering a stage of AI known as General Intelligence (Figure 4). AI is divided broadly into three stages: artificial narrow intelligence (ANI), artificial general intelligence (AGI), and artificial super intelligence (ASI).
- ANI is limited in scope and essentially on par with a newborn’s intellect
- AGI covers things like reasoning, problem solving and abstract thinking, which means machines are on par with us adults. This is a very powerful stage that is enabling us well beyond static thresholds, manual correlation, and causality identification in the world of service assurance.
- ASI, the final stage of the intelligence eruption, will surpass the intelligence we mere humans possess.
The point is, AI is still embryonic in its overall lifespan. But its potential has captivated minds beyond the data science and sci-fi communities, to minds around the globe. AI is undoubtedly unleashing a torrent of financial opportunities and unparalleled technological power for all types of businesses.
The Value of The Human Cognitive Processes
All this brings us to AIOps, a term coined by Gartner in 2016 for the application of AI diagnostics to operating IT. To understand the significance and correlate observability data into an actionable incident provides SRE’s, ITOps and DevOps teams with the diagnostics, context and causality to restore services and assure the customer’s digital experience. In order to apply AI, observe normal behavior, and analyse the deeper operations of a service, we need to triangulate our own cognitive process, make inferences, and understand the judgement we as humans alone possess.
In my next post in this series, we’ll examine three essential truths on the road to delivering great customer experiences.
Moogsoft Senior Product Manager Adam Frank recently presented on this topic at Sensu Summit 2019. To learn more, view the full talk:
About the author
Adam Frank is a product and technology leader with more than 15 years of AI and IT Operations experience. His imagination and passion for creating AIOps solutions are helping DevOps and SREs around the world. As Moogsoft’s VP of Product & Design, he's focused on delivering products and strategies that help businesses to digitally transform, carry out organizational change, and attain continuous service assurance.