To be effective, modernizing responsive digital infrastructures and creating new value through creative business strategies both require AI. We talked with Paul Zikopoulos, an IBM Vice President on the domain of Big Data and AI, as well as a host of other topics. We’re convinced: what he shared with us will help anyone. Today, Paul uses his deep technology skills to lead the IBM Technology Unit’s (all IBM software and hardware) technical skills strategy. He tells us, “Technology years are like dog years, so the number one thing you can do as a skills leader is to instill a learning never ends culture; a curiosity that burns so deep, teams don’t just learn through the pedagogy, but perhaps even more so, on their own.” My job is “Simple to KPI, harder to do” he tells us. He describes his mission: set a new technical threshold for the whole IBM sales force (sellers, tech sellers, and its partner ecosystem), with technical depth that matches domain knowledge. He says, “Domain knowledge, and soft skills for that matter, are critical because today’s sellers need to be teaching clients, we need to know more than the client knows… even about their own business dynamics! I try to lead by example, and I never ask our sellers to do something I typically haven’t already done. I think leading from the front like that is super important. If I asked you to get more technical, you will see I got more technical before my ask. If I ask you to live demo, that’s because I’ve already been demoing live. I think people appreciate that about me. Or perhaps they think, ‘if he can do it, I can surely do it’ and I agree with that.”

Paul has been named to dozens of global “Experts to Follow” and “Influencer” lists, including Analytics Insight’s “Top 100 Global AI & Big Data Influencers.” Paul has written 21 books (including “The AI Ladder” and “Cloud without Compromise” with O’Reilly, and 3 “for Dummies” titles) and over 360 articles during his accidental 28-year career as a self proclaimed “data nerd”.

The Not So Typical Journey to Becoming Technically Proficient

Before starting his professional career, Paul was an Economics and Business major in university. Here’s the thing most people don’t know about him, and it shocked us when we looked at everything he’s done: Paul came to IBM having never taken a computer course. His IBM career has taken him through all kinds of roles including development, product management, and sales, but he’s always maintained deep roots into surrounding technical communities and clients. It’s clear to us he practices what he preaches when he talks about learning. Paul taught himself to program, though he admits, “I’m not very good at it, but StackOverflow hides a lot of that.” He got certified in all kinds of database technologies (IBM, Microsoft, and Oracle), and more. Paul notes, “Today, anyone can prepare to do almost any job in the world. You just need to decide to start learning about whatever it is you want to do. In the history of the world, it’s never been easier to reinvent yourself or pivot your career.”

Fast-forward all these years, he tells us “All those books and articles I’ve written, those thought leader awards I’ve been so honored to receive, becoming an executive, being consulted on the topic of Big Data by “60 Minutes” or my Women in Technology comments making the TV show “The View”, to becoming an award-winning speaker and writer… for whatever pothole or mountain of accomplishments anyone thinks I’ve had, it’s all because of this amazing employer/employee relationship I have at IBM. They invest in me — continuously. I invest in them with the number one commodity I have — gritty effort. It’s a really amazing place to work.”

Today’s IBM: A Hybrid Cloud and AI Company

IBM is at the forefront of two highly talked about technologies: artificial intelligence (AI) and hybrid cloud. Paul tells us how these anchor points serve as “DNA building blocks for IBM’s digital transformation platform by providing clients with five digital advantage levers: data-driven, automate, secure, modernize, and transform.” (IBM also creates intelligent hardware that turbocharges these levers.) Paul asks us to think about all the outcomes that can be produced with these hybrid cloud and AI-infused levers: sustainability, lives saved, more efficient supply chains, better risk underwriting, better customer engagement, and so much more.

From a data-driven perspective, IBM is putting critical investment dollars far beyond how accurately a model can be built. He gives us one (of many) examples and notes that “Accuracy in AI is no longer enough, explainability is crucial, especially in consideration of today’s social justice movements. This is all part of IBM’s AI mission. Think about it, a model starts to go stale the moment it’s deployed into production. With all the hype in AI, Day 2 operations are often overlooked — this is the space of MLOps.” Paul continues showcasing another problem IBM is solving, “Today AI is like those that own a Ferrari” — he assures us he doesn’t own one — “the experience is for the privileged few. IBM is intent on changing that… how do companies engage all of their employees such that everyone can participate in a data renAIssance. This requires innovation around no-code or low-code toolsets that democratize data for the many.”

When it comes to AI projects, Paul advises clients to, “categorize their AI initiatives and tools into 3 benefits: automation, prediction, and optimization” and drive the project with that end value in mind.

The data-driven aspect is obvious, but he’s quick to note how “AI plays a key role in all of IBM’s digital transformation levers.” For example, he told us how IBM AI makes companies more secure and can predict malware attacks on a system and alert the business because of shrinking storage compression rates. Why are compression rates shrinking? Because a data heist is starting, and one popular method is to encrypt data (which won’t compress well) and charge a ransom. Now the AI takes action — automation. IBM uses AI to observe threats and behaviors it sees on the network and dispositions them automatically so scarce analysts can stay focused on the tough stuff that needs investigation and some really tough manual interventions. Knowing that a specific air-gapped backup is free of any malware for recovery and using it to quickly restore data and ignore a hacker’s ransomware note — optimization.

He shares examples of business automation, AIOps, cloud-native modernization with the Red Hat Hybrid Cloud platform, and more. We can’t detail all the great stuff we talked about here, but he left us with a summary that really impressed us with what IBM is up to and we’re convinced: IBM has a flexible, secure, open platform at its core that provides scale up solutions for enterprise digital transformation.

Is Every Company a Technology Company?

Paul believes the world has reached a stage where there are no more technology companies, especially post-pandemic; he notes how, “Every company is now a data and technology company”, and if they aren’t, he is unsure how long they will exist. He talks about an inflection point, “A lift, shift, rift, or cliff for companies — two of these paths means you’re likely to survive, the other two suggest you won’t in the long run.”

Technology, according to Paul, is a fundamental advantage, but businesses must learn how to implement it at scale. Thrivers and newcomers to marketplaces do this very well, but divers do not. He uses the legendary Kodak corporation as an example of a diver and tells us how, “They thought they were in the film business, but they were actually in the memories business. Kodak missed the technology curve that changed the modality of storing memories from film to digital.” He contrasts this with Garmin’s journey and asks us if we remember plugging an external GPS into our car for driving directions. He confidently asserts, “No one really does that anymore, but Garmin is still around. Why? Garmin knew they were in the data mapping business. So, they used technology to expand the modality of mapping.” He goes on to note how Garmin delivers innovative GPS technology across diverse markets, including aviation, marine, fitness, outdoor recreation, tracking, and mobile apps. What’s more, vibrant communities that collaborate around better mapping, training plans, and more bring people together with common interests — many creating face-to-face experiences drawn together from a common technology platform. He gives more examples of everyday companies that have technology at the forefront of their success and names Sephora (the beauty cosmetics company) with amazing technology and how AirBnB can predict if a guest will hang up their towels after a stay.

The VP of IBM Skills Vitality and Enablement

Paul oversees developing a learning strategy and upskilling agenda that continually drives technical skills across the IBM Technology groups’ sellers and partner ecosystem. In today’s labor market, that’s bound to include people from various walks of life, from those who were like Paul when he first arrived at IBM (no computer background) to seasoned technology specialists. Within that framework, Paul’s organization has perfected a recipe that takes people from, “Zero to hero, regardless of where they are on their skills journey.” He adds “This journey is very similar to the one I took, so I know it works.”

Paul goes on to explain how this journey is more than the bits and bytes of technology and includes the ability to tell a story, articulate value, and other soft skills that synergize with technical skills. It’s about comprehending the issues and learning how to teach clients about topics or points of view they may not have previously considered, or even getting them to think differently about themselves and the types of businesses they want to be. He shares a couple of examples, “I talk about how accuracy is no longer enough when it comes to AI. Explainability matters. This is a corner many aren’t looking around. What about the cloud and agile development? Great for developers, horrible for site reliability engineers (SREs). We don’t want our sellers to lead with product. So, we make all sellers go through this, technical or not. But behind these teaching moments are deep technical skills… that is the magic. Give me anyone willing to put in effort — grit — and I’ll get them skilled.”

His team is made up of dozens of highly skilled and client-focused professionals (many of whom are published authors, award-winning speakers, and patent filers) whose sole purpose is to accelerate IBM’s mission of providing deep technical expertise to IBM’s clients as they transform their own businesses to AI and hybrid cloud enterprises.

Major Data Challenges

Over the course of his work, Paul has spoken with thousands of clients. He finds it astounding how much data companies collect that goes untouched or gets forgotten; as a side effect, most companies experience two phenomena he refers to as “guilty of not knowing what you could already know” and “enterprise amnesia”. He tells us how the majority of a company’s data corpus is like a thirsty sailor in a lifeboat adrift at sea: water is all around but there is nothing to drink (because it’s salt water). He says,“If I were to plot any company’s available data curve using the amount collected over time, it would be a super steep curve.” He further adds, “If I then plotted a understood data curve, it would be much flatter, trending flatter as more data gets collected and as time goes on. The space between these two curves is a difference maker for a company—I call it the “Cost of Not Knowing”. The “Cost of Not Knowing” could be lives saved, profits, supply chain efficiencies, employee retention, sustainability, and more.” According to Paul, firms should work on raising the data understanding curve to shrink the cost of not knowing.

Governance is another ‘needs fixing’ issue that Paul finds everywhere he goes. Most firms’ data governance plans have hardened into a “least effort to comply approach,” he says. But he’s super clear how governance initiatives done right provide dividends that can be used to speed up an analytics agenda. This is a crucial component that is sometimes missed. It’s vital to be able to use AI to categorize and appropriately label incoming data (such as finding a credit card and automatically obfuscating it based on company-wide business policies that comply with regulators), understand its origin, and what’s been done to it. Explainability is the last obstacle. As Paul said earlier, explaining not only the decision made by the analytics but also the provenance of the data used to train it and why the AI reached the decision it did is crucial.

Data Fabric is a popular subject right now, and it’s the strategy required to overcome the issues raised by Paul. IBM’s data fabric is a way for businesses to build a culture and architecture that allows them to connect data throughout the enterprise for seamless access and insight.

We ask him for a final insight on this topic before we move on, and he concludes this part of the discussion by saying, “Don’t box yourself in with a governance for compliance approach — you have a chance to broaden the aperture: think governance for insights and the compliance stuff will follow.”

Best Recognition as a Professional

Over the years, Paul has received a lot of accolades and named to dozens of “Thought Leaders” and “Experts to Follow” lists. He has won awards for his speaking and writing, too. But he thinks the best recognition he ever received was when he became the first male to win the “IBM Canada Women in Technology Ally of  the Year” award. He proudly states, “A great thing about working at IBM is that they are leaders in the social justice spaces,

and women in technology is no exception.” He goes on to comment about the rich set of programs and leeway IBM gives its employees to go out into the world beyond the walls of IBM and make a difference in the social causes they personally believe in. He tells us how he implemented a ‘Culture of Good’ program in his organization where employees get an extra day off every six months to do something good for the world — they just have to share the experience on Slack.

Advice to Emerging Tech Leaders and Enthusiasts

According to Paul, the mindset of grit and learning never ends are critical success factors for any role; after all, he says, “Real learning happens in the struggle, that’s why you need both.” He talks about “making learning a habit” and how “what you do every day matters more than what you do every once in a while.” In the end, we’re convinced. We agree with him that, “In the long run, when it comes to technology, someone that relentlessly focusses on learning never ends will overcome any knowledge gaps with those formally trained who stand still with their learning, even surpass them in knowledge over time.”

He advises young leaders to surround themselves with people that are diverse and smarter than you, and ensure they all get a voice. Earlier in his career, Paul really upset a senior executive when he responded to their accusation that he was acting like he thought he was smartest person in the room by saying, “I am the smartest person in the room.” For a moment, his response left the exec astonished and speechless. But before they could say another word, Paul explained why he said that and how he was half joking (though they didn’t take it as a joke). Paul told this exec, “I surround myself with people much smarter than me. I don’t make decisions without hearing their opinions on things we need to make decisions on. If I get 5 or 10 opinions on something, the feedback will either match my conclusion, fine-tune it, or perhaps I had it all wrong and I need to pivot. I’m going to come across as super confident and knowledgeable because I likely did more homework than most and my position is a representation of a lot of people I trust completely.” As it turned out, what that executive initially thought of Paul was the opposite of the truth. He points out the irony of it all. “I’m not smart enough to make a big decision with just my opinion, so I invoke the wisdom of a crowd to make big decisions.” Paul reminds us that having smarter people surround you, empowered with a voice, is a superpower for emerging leaders in the tech space. He also notes that despite this superpower being so readily available and so accessible, he’s not so sure all leaders use it as much as they should.

Sharing Success Principles

People inside and outside of IBM, those at the start or tail end of their career (or somewhere in between), people looking for a different job or advice on how to climb the corporate ladder, and others who just aren’t happy with their job… whoever they are and whatever their career dilemma, whenever Paul gives career advice, he tells us it always starts with this list:

• Work/Life Balance
• Love what you do
• Career Growth
• Money
• Title (it’s different than career growth)

He gives anyone that asks for his advice a homework assignment. Paul says, “You can’t just come to me for an opinion; you have to put some work into our plan.” Then he tells them to go and put the above list in order of importance and this becomes their success criteria list. He says, “If you look at the first two or three and you’re super happy, you’re on a great path to success!”

He tells us this list will change over time; in fact, it’ll change multiple times over the course of a career. He recalls, “When I was younger, money was likely at the top of the list—I was willing to work jobs and do stuff I didn’t like to put myself in a position to make more money. I didn’t have kids; I wasn’t married, so work-life balance didn’t matter; I was also 23 and just out of school which is code  for broke,” he jokes. He then shares with us how his list has changed five or six times over the course of his career.

He suggests writing your list down on good old-fashioned paper and then pulling it out and looking at it when you’re frustrated with your career, a perceived lack of success, or unsure what to do next. He states, “I’ve had both experiences. I was getting upset at something in my head that took over my definition of success and I forgot what success truly meant to me — documented in my list. I got lost in misguided envy. I made a mistake and it cost me. Other times, I looked at the list and had to change it because it didn’t reflect my life anymore… like when my daughter was first born.”  He assures us that making and following this list will serve one well for their entire professional career — we’re working on ours already.

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