AiThority Interview Series with John Gentry, CTO at Virtual Instruments

Highly competitive industries such as banking and finance will also lead adoption because while they are traditionally very conservative industries, they need to maintain a competitive advantage and stay ahead of the curve.

Know My Company 

Tell us about your interaction with smart technologies like AI and Cloud-based Ops platforms.

 Through my experience with so-called “smart’ technologies, it’s become clear to me that AI is not the new easy button. It is actually just the new catch-all marketing buzzword, along with “machine learning” or ML. It seems like just about every tool out there claims to “do” AI or ML, but the problem is very few are actually defining what that means, or how exactly they are using or applying those technologies. They really do a better job of marketing than they do of delivery.

While some AI-based solutions, specifically in AIOps, have promised to make sense of the onslaught of data and alerts generated by the myriad of legacy tools by applying AI, they’re really just doing basic event correlation and deduplication with some rudimentary pattern matching. To me that isn’t AI. Just throwing math at the problem, without the right context or applied intelligence, isn’t the answer.

I think we should change AI to mean “Applied Intelligence” instead of “Artificial Intelligence” – that would make a lot more sense and eliminate a lot of the hype.

When it comes to cloud-based ops solutions, I’m curious how they are going to evolve and if they’re going to be able to compete in a hybrid and multi-cloud world. While these kinds of solutions are good for monitoring operations on a single vendor’s cloud, I expect that they will run into problems once they try to perform across multiple infrastructure environments.

How will they handle all different underlying infrastructure across legacy, private, and public multi-cloud environments? It’s a challenge of both scope and scale. I’ll be keeping my eye on these technologies to see how and if they change because they will have to evolve to survive.

How did you start in this space? What galvanized you to start at Virtual Instruments? 

I got involved in this space to solve customer’s problems, and I started working with Virtual Instruments because I saw that they had the best data, the best analytics and the best technology that could solve real problems in IT Operations, specifically from an infrastructure perspective. The problem Virtual Instruments was solving was a problem that companies I previously worked for had struggled to manage – their solution was to throw more hardware and more people at the issue, which only made it more complex and resulted in finger pointing across the organization.

What is Virtual Instruments and how does it leverage AI in its operations? 

Virtual Instruments is a hybrid IT infrastructure management company, with a focus on the applications and their workloads that depend on that infrastructure.

We offer solutions for everything from on-premise physical, virtual and private cloud infrastructure, to public and multi-cloud computing environments. Our goal is to make it possible for organizations to get an end-to-end view of their entire infrastructure and put that in the context of their mission-critical workloads and applications, regardless of the underlying infrastructure.

When it comes to our work with AI, we realized early on that it isn’t just about throwing various algorithms and data at the problem. To get to a truly value-added application of AI or ML, you have to really understand the problem and the context where you’re going to apply algorithmic intelligence – another phrase that I think makes more sense than “artificial”.

We’re applying hundreds of man-years of experience to our approach to AI, ML, and AIOps. By monitoring and managing the entire infrastructure, we’re able to generate our own unique data, as opposed to just pulling existing data sets from various external sources. In other words, we’re not just aggregating data – we’re generating and collecting it ourselves. This data is then used to educate the AI engine, allowing the intelligent algorithms to be fully informed and operational, rather than glorified pattern identifiers.

What is the state of AI for IT Infrastructure Management in 2019? How much has it evolved since the time you first started here? 

The technology and industry as a whole are in their adolescence, but officially out of the infant state. There have been two evolutions within the industry – one focused on the changing industry and market need, and one focused on the technology itself.

• The market

The evolution of DevOps and the increased rate of change in environments has created an IT environment that is so extremely complex that it’s become incomprehensible to humans. That’s put a focus on ways to apply AI/ML to solve the big data analytics problem, which has led to the evolution.

• The technology

The evolution of the tech itself – we’ve had enough focus on the problem and data science has been applied to it for long enough that we can start to see solutions that aren’t just interesting they’re relevant.

Tell us more about your vision into growing revenue opportunities by deploying IT Infrastructure Management.

I like to focus on the fact that the revenue opportunities associated with infrastructure management solutions will only materialize if the technology brings new capabilities that deliver true value to individual companies and the market as a whole. Infrastructure management and monitoring solutions grow the breadth of data that feeds into an AI engine, and each new integration is a new revenue opportunity that results in more available data. This also increases the value of what we can deliver as a result of an informed AI algorithm – remember, more data is good, but more analytics based on more data is even better.

Tell us more about your new approach to ending the “IT War Room”. How does Virtual Instruments deliver on its promises? 

War rooms are a symptom of a much bigger problem within IT organizations. Siloed monitoring tools lack a common context and have no inherent understanding of the applications or their importance business. These fragmented tools can’t provide end-to-end visibility across IT infrastructure and applications, and teams within each silo can’t identify and resolve the problem on their own. This leads to “IT war rooms” filled with unconstructive finger-pointing.

Our hybrid infrastructure management and AIOps solution eliminate these obstacles by holistically monitoring, analyzing and optimizing the health, utilization, capacity, and performance of IT infrastructure IN the context of the application. We apply real-time, AI-based analytics featuring machine learning, statistical analysis, heuristics and expert systems across the entire infrastructure, enabling organizations to proactively identify and resolve issues that impact business-critical applications, which in turn removes the need for the costly reactive firefighting that is the hallmark of traditional IT war rooms. 

How do you see the raging trend of including involving AI and Machine Learning in a modern CIO/CMO’s stack budget?

Hybrid infrastructures are continuing to grow in complexity and capacity, but the operations staff supporting those environments isn’t growing with it. Traditional tooling is something that every organization is trying to get away from, and AI offers a unique solution to that problem. Every company has a strategy for rationalizing every tool they currently use or plan to purchase, and every company is trying to find a next-generation approach to making buying decisions that will help consolidate the monitoring landscape and alleviate “tool-fatigue”.

They are also looking for solutions that can off-load low level and remedial tasks from the limited staff to free them up for more strategic initiatives. There is already a palpable demand for AI-based solutions to address both these requirements, and the budget is there to support that demand.

Customer Success means making sure that the underlying infrastructure is running the way it should be and monitoring for issues that can be addressed before they really become a problem.

How do you differentiate between technologies for Customer Success vs. Customer Support in IT Infrastructure Management? Who are you competing with in this landscape? 

The difference between technologies that focus on Customer Success versus Customer Support really comes down to the difference between being proactive versus reactive.

Customer Success means making sure that the underlying infrastructure is running the way it should be and monitoring for issues that can be addressed before they really become a problem. Customer Support is about getting your customers out of trouble when something does go wrong. Both are important, and you should be capable of being reactive when there is an issue, but the primary goal should always be to avoid issues in the first place.

Our biggest competitors in the space are those companies that are only looking at the Customer Support side of the equation, which really makes them the status quo. And while there may be an overlap in marketing messaging between us and our competitors, that’s not the reality when it comes to capabilities.

What is the biggest challenge to Digital Transformation in 2019? How does Virtual Instruments contribute to a successful digital transformation? 

Honestly, the biggest challenge of going through a digital transformation is keeping the lights on as you’re going through it. You need to sustain the business throughout the entire process, but how can you maintain the required level of service while at the same time trying to modernize the core applications and infrastructure?

We offer unique solutions that help companies accelerate their new business application and infrastructure deployments, all while de-risking their digital transformation initiatives. Our customers can determine which applications they should migrate to the Cloud, evaluate new infrastructure products through performance testing, all while assuring the performance of the existing infrastructure that supports their mission-critical applications.

Where do you see AI/Machine learning and other smart technologies heading beyond 2020?

Just like any nascent industry, smart technologies like AI and machine learning are going to continue to grow in their application and thus become harder to define until they then slowly but surely consolidate. The technology itself will also move beyond just making sense of the data and informing humans to make better decisions, and ultimately it will enable proactive automation.

The Good, Bad and Ugly about AI that you have heard or predict? 

The benefits of AI really come down to improving efficiency, reducing time and energy spent on menial tasks, deploying key resources beyond tactical needs, and being able to put more energy towards problem-solving and strategic tasks.

The negatives of AI are tied to overpromising on the results and resultant benefits of the technology. AI alone without the proper application is not going to solve world hunger (or the IT War Room), at least not by itself. Having those expectations will just result in disappointment and misplaced faith, as well as wasted time and resources.

As with any kind of new and important technology, the ugly side of AI will rear its head if the technology is unleashed without appropriate standards and governance. Companies who try to get ahead by using this technology irresponsibly will ultimately threaten innovation and sabotage the industry as a whole.

What is your opinion on “Weaponization of AI/Machine Learning”? How do you promote your ideas?

One threat presented by “smart technologies” like AI and ML connects back to the focus on the “artificial” nature of these intelligent algorithms, rather than applying real data to AI engines that are wholly informed and capable of making decisions based on experience and observed results. This kind of applied intelligence will then result in accurate automated responses. An uninformed AI solution, or one that’s informed by bad data, can only deliver uninformed automated decisions, which in turn could unintentionally result in catastrophe.

The Crystal Gaze 

What Cloud Analytics and SaaS start-ups and labs are you keenly following? 

AppDynamics is doing some interesting things with their Cognition Engine which uses machine learning to automate anomaly detection and correlate software and business performance.

What technologies within AI/NLP and Cloud Analytics are you interested in? 

I have a personal interest in cognitive computing, which really just takes AI/ML to the next level.

As a tech leader, what industries you think would be fastest to adopting Analytics and AI/ML with smooth efficiency? What are the new emerging markets for these technology markets?

As it is to be expected, companies that grew up online – specifically SaaS companies – will lead in the adoption and effective use of AI technologies. Highly competitive industries such as banking and finance will also lead adoption because while they are traditionally very conservative industries, they need to maintain a competitive advantage and stay ahead of the curve. The insurance industry is another relatively traditional industry that is fast to adopt up-and-coming technologies. There’s a real opportunity for insurance companies to take advantage of AI technology as their rates and returns are strongly based on identifying patterns and predicting future results based on those patterns.

What’s your smartest work-related shortcut or productivity hack? 

Use voice memos – I’m always on the go, and I’m always thinking, so when an idea strikes me I don’t always have the opportunity to write it down or apply it to what I’m doing at that moment.  Voice memos allow me to capture my thoughts quickly and go back to it when I have more time to dedicate to that idea. I also ignore all calls from unknown numbers and all emails from people I don’t know but understand that might not be a possibility for most people.

Tag the one person in the industry whose answers to these questions you would love to read: 

Microsoft CEO Satya Nadella – I truly believe that there will be entire Harvard course dedicated to how he changed the culture and strategy behind Microsoft and is thoughts on Stanford professor Carol Dweck’s research on growth mindset.

Thank you, John! That was fun and hope to see you back on AiThority soon.