AI and Automation: Lawrence Lerner Of LEADForward On How To Effectively Harness AI Technology In People Operations

An Interview With Rachel Kline

In large distributed companies, use it for employee development. The larger the company, the greater the need to keep employees engaged, trained, and relevant in their current or upcoming roles. AI can be used to identify trends, recommend training and improvements, and adjust to the many local and regionalized staff without the need for ever-growing HR staff. It is essential to note this should be done in collaboration with team leads and the employee.

With technological advancements, particularly in the AI space, an increasing number of tasks can be either fully or partially automated. In this series, we are talking to People experts about how they’re utilizing new technologies to make their jobs easier and provide greater strategic value. As a part of this series, we had the pleasure of interviewing Lawrence Lerner.

Lawrence is a wealth engineer. Companies he has worked for have earned nine-digits of revenue growth, ten best-in-class industry awards, and the recognition of analysts and peer companies. By taking big ideas and internal R&D, he creates viable products and services, seeing the interconnectedness of systems, data, and user needs in often unexpected ways aligning with a company’s mission and vision. He builds the right teams, go to market, and scaling strategies to become the market leader and box out the competition. How?

He took unattended Retail pioneer Avanti Markets and created eight-digits of growth, setting the company up for higher multiples at acquisition by 365 Retail Markets by adding the college and university market, broadening the forms of payment, and building a true digital wallet. He created six new hardware platforms, adding Machine Learning and computer vision to product selection, created a superior design and user experience allowing for new kiosk placement in public venues such as airports, and then found ways to monetize the transaction and product purchases (targeted advertising, partnerships with card brands) across 350 Operator licensees.

Before Cognizant was a mainstay in offshoring, he developed the Portfolio Analysis methodology. Skeptical clients needed to see systematic ways to identify risk factors, technical needs, and people processes to outsource the right systems, resulting in eight digits of growth over the first year.

He built out the European presence at UST and Cognizant, winning ten new logos in the first years and resulting in nine-digit businesses by reshaping services and processes for local markets. Lawrence opened the Latin America presence for 365 Retail in under two months by re-building payments technology and techniques.

Thank you so much for your time! I know that you are a very busy person. Before we drive in, our readers would love to “get to know you” a bit better. Can you tell us a bit about your ‘backstory’ and how you got started?

My love of technology and interconnected things started as a child. As a child, I would look through the local junkyard for bits of technology to take apart. I found and took apart an old-fashioned public phone. Seeing how things worked and putting them back together in new and different ways was a source of endless fascination. Back then, phones had magnets (what kid doesn’t love strong magnets?) in the receivers, and there was a blend of mechanical and electrical parts. Taking it apart and putting it back together in “working” order started me investigating the interconnectedness of devices and how people used them. Everything was new and different; I wanted to know why there was a rotary dial instead of buttons. Why was everything made out of metal? I thought about the annual birthday calls I was on with distant relatives. I held in my hands a device that I knew had a counterpart somewhere else in the US and that wires were running between those two devices.

It set me down the path of taking apart everything I could get my hands on and putting them back together, sometimes in “unique” ways. There were often many extra parts and conversations with adults about how and why I got my hands on devices. I found common elements in all the things and experimented with swapping parts. Those experiences left me with three needs.

• I needed to know why hardware and software were designed in specific ways. The interface, the tactile feel, and usability continue to be a source of tinkering.
• How do the insides work and make lights blink, sounds come out, or compute in specific ways.
• How does data (sounds, key press, information) go from one place to the next?

Fast forward to my sophomore year as a computer science student, One night I was “rewriting” one of the core operating system programs on the university’s network. I might have added some code that was unnecessary but interesting to me. I felt a hand on my shoulder; it was Ernie, the head of the university’s support team. “User e232, you clearly have too much time on your hands. Let’s talk.” Instead of a trip to the Dean, Ernie put me to work in the computer center and, over time, gave me a lot of responsibilities. In those days, only a handful of people on campus had regular computer access, and few knew what the “Internet” was. I helped students and faculty by answering questions, managing the university’s host file (in the days before you could type in a text domain name), helping people typeset their dissertations, and fixing printers (in those days, printers were great beasts that needed two people to move them). I learned how to talk to people who weren’t technical and often much older than me. I began to learn patience by working with computers.

It was those formative years that catapulted me into leadership roles. I was the tech guy who could bridge the gap between technology and business. I was able to speak plain English about complex and interconnected systems. By age 28, I was running an emerging technologies consulting practice.

It has been said that our mistakes can be our greatest teachers. Can you share a story about the funniest mistake you made when you were first starting? Can you tell us what lesson you learned from that?

Just because you can do something doesn’t mean you should, nor should you share with it with your boss. I was a team lead in developing a complex customer service system serving millions of customers. We were converting a legacy mainframe program to a new technology. In my spare time, I was tinkering with the user interface design. During my experimentation, I found a way to change the color of the scrollbars. Not a big feat by today’s standards, but then it was a lot of coding, and it required making changes in many places. The client project manager walked by my cube one day and saw the changes. I excitedly explained how I did it. He asked me if I could make them yellow, which were their brand colors. Eagerly I went into way too much detail about how to do it. He showed me the company’s brand colors and told me, and everyone else, to make the changes on every screen. It was a lot of work for programs that would only be seen by staff and took us off schedule by two weeks.

My colleagues were not happy, nor was our project manager. Lesson learned: keep client discussions on track and to the essentials. Don’t let the excitement of showing off technical agility distract from the primary goals. It was a profound lesson I took with me for working with startup companies.

None of us are able to achieve success without some help along the way. Is there a particular person who you are grateful for who helped get you to where you are? Can you share a story?

“Ernie” helped me focus my powers for good 😊 and set me on the path to bridging business and technology. It also taught me to look deeper into the motivations of others. I was an overly curious kid looking for lessons on how to connect data from one place to another. My professors weren’t teaching it, so I took my own initiative on interconnectedness. I wasn’t malicious, I didn’t have any mentors or guidance. Today, I’m constantly reminded to look deeper into the motivations and needs of others.

Can you please give us your favorite “Life Lesson Quote”? Can you share how that was relevant to you in your life?

I have a book full of them 😊 https://www.lawrenceilerner.com/gallery — Soon to be published.

“If you don’t ask, the answer is always no.” Unasked questions in business or life are a path to rejection. Too often, we assume that someone will reject a proposal before they’ve even had a chance to consider it.

When I was running the UST consulting business, I routinely asked CIOs for their budgets. How big was it, what were their priorities, and how was it allocated? It would shock some of the sales execs. I remember asking the CIO of a large CPG (Consumer Product Goods) company about his budget. “I cut a $500M check yearly to one of your competitors to run my IT department. You’re here so I’ll listen to your pitch.” I walked through our services. I had productized the way we did modernizing legacy mainframes and had just won a best-in-class award beating out much larger competitors. The CIO said, “[…] that’s the only service they don’t provide. Here are three systems I want to retire. Tell me what you can do for me.” That began a multi-year relationship.

Thinking back on your own career, what would you tell your younger self?

Take the time to let people and businesses experience you. I move quickly, absorb a lot of information, and produce new spins on old products very quickly. It doesn’t always give people time to catch up. I remember explaining to one of the industry analysts my plan to productize Business Intelligence as a Service. They literally laughed me off the briefing I was doing. Nine months later, everyone was writing about SaaS based Business Intelligence.

In the past, every few years, I would look for the next new technology and build new products. Companies have earned ten best-in-class awards due to that, and I want to illustrate the art of the possible in the market while challenging myself. Sometimes, that meant leaving a company or reinventing myself. Even in tech, that type of rapid change and turnover is unexpected. I’ve since learned to build deeper roots.

Let’s now move to the central part of our interview. How have recent technological advancements such as AI made your job easier?

A definition of AI and the different forms available is key to start. When people talk about AI, they have preconceived notions of what it means. Today’s types or definitions of AI seem to grow faster than the algorithms can make recommendations. Here are some standards for the readers.

1. General Purpose AI or Self-Aware — This is Mr. Data, Wall-E, Agent Smith, etc. they don’t exist. They are adaptable to many and perhaps every situation with intuitive and sentient responses — just like people.
2. Narrow Purpose AI — Algorithms and solutions focused on specific problem sets such as recommendation engines (which flavor are people most likely to buy) and chatbots. There are several flavors of this type of AI.

• Reactive Machines — this is the modern thermostat at the end of the day. It works through a decision algorithm(s) at a point in time based on inputs/sensors. It likely has little or no memory (perhaps “After 6 PM keep the temperature above 68 degrees”) and provides highly quantitative responses with a narrow range of functions. It cannot decide when to pre-heat the oven for you.
• Theory of Mind — AI can interact with humans on a relatable and emotional level. It does not exist in a practical form today. Bing got snarky last year, not quite relatable.
• Limited Memory — This is the current state of practical AI today.
• Limited Memory AI is state-of-the-art today. It is best understood as absorbing data based on one or more models or algorithms.
• The search pattern within the data improves over time. This is limited ability to decide between models. Humans are now more sophisticated at determining which model works best by analyzing results. This is where I’ve done most of my work with data scientists.
• There is the ability to store, retrieve, and build upon massive amounts of data to provide recommendations.
• Machine Learning and Recommendation Engines fit into these categories. These are the most common categories when the general public interacts with AI. The lack of sophistication in these models makes people think they are working with an ordinary program.

As you might have guessed, I am a glutton for data. Limited Memory or LLM (Large Language Model) technology is the state-of-art for massive data repositories. It is very good at sorting through unrelated and large unstructured data sources (ten 500-page written documents), making recommendations, and coming to limited conclusions.

LLMs are very good at sorting through a lot of information without needing code integrations or a lot of setup. One points the model at the data sources and writes a prompt. Prompt writing is its own art and science.

In which processes do you utilize automation the most?

As mentioned above, it’s sorting and organizing large amounts of unstructured and unrelated data. “Data is good, information is better, but stories are relevant.” Using LLM models to create stories that share deep insights is an excellent investigative resource. The speed to pull together sources is unparalleled. However, the output from an AI or LLM is rarely a finished product. It’s one step in the pipeline of building your conclusion.

What should people bear in mind when automating processes?

1. Trust but verify. Modern AI models are known to “hallucinate,” that is, provide responses that are false or not cross-checked against multiple sources. The concept of peer-reviewed results doesn’t exist today. The time trusted “garbage in, garbage out” bears remembering. Additionally, if you cannot provide accurate and verifiable input data, your results are not to be trusted.
2. Establishing the process you are automating is still relevant and not a set of legacy objectives. Processes, especially in highly regulated environments (E.g., Credit Unions) evolve over time.
3. Train your people on the new process. Software releases fail when the people using them don’t understand the changes made. With AI, the key will be excellent prompt writing and the right data sources.

What are your “Top Five Tips For Harnessing AI Technology to Propel People Operations”?

1 . New employee onboarding. Everyone has a story and their way of learning and navigating new situations. AI models can be used to develop a one-pager in collaboration with a new hire, the hiring manager, and the team. It’s a good starting point to help people be successful and learn about new colleagues (maybe find some old connections — , a colleague reached out to me because we just had hired an old friend that he had lost track of more than 20 years before. It was only through coincidence that he found out we brought them on board.) Through AI it might be possible to identify connections. We want to set people up for success on Day One.

2 . Use AI to sanitize resume screening and improve diversity hiring. AI is good a tracking and rewriting documents. Use the AI to sanitize resumes and screening information to put everyone on equal footing.

3 . In large distributed companies, use it for employee development. The larger the company, the greater the need to keep employees engaged, trained, and relevant in their current or upcoming roles. AI can be used to identify trends, recommend training and improvements, and adjust to the many local and regionalized staff without the need for ever-growing HR staff. It is essential to note this should be done in collaboration with team leads and the employee.

4 . Use predictive analytics to find attrition risks. Similar to the way credit card companies use adaptive scoring to find anomalies in credit cards or other transactions. Patterns or environmental forces can be identified to find flight risks. During the “Great Resignation,” it may have been possible to identify changes that triggered employees to move on. There are opportunities to make broad policy changes to retain key players.

5 . Compliance Monitoring. Every municipality has its own set of rules; in regulated industries (Pharmaceuticals, Banking, Healthcare), it’s possible to get ahead of changes in practices and regulations. This lowers stress for employees and improves the compliance posture of the company, potentially avoiding issues and additional costs.

What are your favorite “I couldn’t live without these” tools?

Grammerly. I’m often under deadline to produce many documents at once. Grammerly reminds me to do proper proofreading consistently and every time.

For fun, Dall-e. I’m a frequent traveler and a big fan of the artistic styles of Van Gogh and Klimt. I have the AI create landscapes of some of my favorite places in the style of those artists.

How do you see technology impacting the HR space in the future?

1. As we’ve discussed at length, AI has a big impact and has the opportunity to make people’s stories more relevant and relatable.
2. Remote work. Better tools to create the interconnectedness that allow true collaboration, think virtual whiteboards, that allow all users to draw and see in realtime.
3. Blockchain. I’ve worked with a local state government to put licenses and certifications on the blockchain. On average, it takes 90 days to onboard a new doctor with advanced certifications. The doctor is being paid even though they cannot start without verification. Any license or degree can be applied to this MIT has done this for several years with their degrees.

We are very blessed to have some of the biggest names in Business, VC funding, Sports, and Entertainment read this column. Is there a person in the world whom you would love to have a private lunch with, and why? He or she might just see this.

Indra Nooyi. We share a passion for creating business innovation and integrating our spiritual values into our full lives. Hearing about her 25-year journey at Pepsico and beyond would be fantastic.

How can our readers further follow your work?

I publish most consistently on LinkedIn https://www.linkedin.com/in/lawrencelerner/

Thank you so much for sharing these important insights. We wish you continued success and good health!