Thank you so much for joining us in this interview series. Before we dive into our discussion our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path?
During business school, I was mostly interested in Marketing. But I also took electives in forecasting and statistical analyses, so I guess I’ve always been interested in business with a quantitative bias. I’m better with numbers than the creative side of marketing. After some early jobs in Marketing, I was asked to lead a B2C Pricing team implementing a retail price, promotion, and markdown optimization SaaS solution. I fell in love with the area. Modeling market responses and using those insights to inform our business strategy was thrilling. I feel blessed to have had that experience.
I eventually came to work at a mid-sized software company, Vendavo, that was doing similar things for B2B. I soon realized that while advanced data science techniques were being utilized in retail and consumer goods, they were almost nowhere to be found in B2B — at least not in pricing or selling. Business operations and supply chains have had software systems and optimization algorithms for decades yet when it comes to setting or negotiating prices and contracts, many firms, even today, still rely on gut feel and spreadsheets.
What do you think makes your company stand out? Can you share a story?
Vendavo’s approach to solving client challenges makes us stand out. We firmly believe that success for our customers depends on the correct blend of improvements to people, processes, and of course, technology and data science. Some companies approach this from one aspect or another and act as if their solution is the silver bullet: just use this process or that form of machine learning and your business will be magically optimized. In reality, it never works that way.
A good example of this would be the case of a leading windows & doors manufacturer who had implemented a software solution that may have had good AI built in but, as their CEO put it, there was no change management for people or processes so the project was deemed a failure.
You are a successful business leader. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
- Humility. I’ve had some success in leading business teams, but I have also had my share of mistakes and learned lessons. It’s important to know that you don’t always have to have answers (because your teammates often do!) and when you do trip up, you learn from the experience. We’ve all seen examples of this play out many times… the overly confident coworker who charges ahead, makes a mistake, but then is too insecure to admit it and, as a result, doesn’t grow from that mistake.
- Curiosity. Curiosity is vital for staying motivated and determined to find the best solutions day in and day out. It helps to work in an area where you’re naturally curious because it makes hard work and determination easier to sustain. I used to manage a team of consultants in our organization, and strong curiosity was considered a top three trait. The reason is that they had to get to know a client’s business quickly, and then needed to look past the obvious things to identify the critical root causes for a successful project.
- Golden rule. Treat others how you’d like to be treated. This golden rule will enable you to experience many more positive and rewarding relationships with colleagues, partners, and employees. Early in my career, my employer brought on a “special assistant to the president.” This person was a whip-smart MBA from one of the top programs in the country. The issue was never his intelligence or business acumen, but that no one wanted to cooperate with him because of how he treated people. He could have had a greater impact in that organization if he had cultivated relationships and treated people with more respect and kindness. It doesn’t matter what line of business you’re in. If you don’t work well with others, your progress will be limited.
Let’s now move to the main point of our discussion about AI. Can you explain how AI is disrupting your industry? Is this disruption hurting or helping your bottom line?
There are common levers for improving efficiency and lowering costs within manufacturing and distribution and AI increasingly plays a part in many of them. We see AI having the most impact when companies leverage AI to inform commercial decisions like offering, quoting, and selling the right products to the right customers at the right prices, in their preferred experience. Why is this creating such disruption? It’s rooted in the size of the opportunity for all organizations. Consider a typical income statement. The largest number on the page is revenue. When you make small improvements in commercial execution with AI, you see outsized impacts on revenue.
A standard statistic in pricing says every 1% improvement made in price typically yields 10% or more in profit. AI is adding intelligence, automation, and scalability to these decisions and having a sizable impact where it matters most, on the bottom line.
Which specific AI technology has had the most significant impact on your industry?
Large manufacturers and distributors serve thousands or tens of thousands of customers across many geographies with extensive product portfolios and complex sales channels. Algorithms seek out patterns in the noise of all that data to find insights and patterns that individuals couldn’t possibly see on their own. It can predict outcomes and make recommendations on the largest of scales.
Can you share a pivotal moment when you recognized the profound impact AI would have on your sector?
Way back in 2013, we hosted a user conference, and one of our customers — a large, well-known manufacturer — presented on how their revenue optimization initiative had not only improved their revenue and margins through mathematically optimized pricing, but their resulting improved price accuracy had also led to a boost in customer experience. This increased their competitive win rate and sales volumes. The benefits at a large company like that were in the hundreds of millions — it was inspiring. Modern AI capabilities have added layers of insights to this intelligence, making it more accessible, and scalable.
How are you preparing your workforce for the integration of AI, and what skills do you believe will be most valuable in an AI-enhanced future?
Even non-quantitative employees like marketing, product, and sales managers are well-served to have a working knowledge of how different approaches or categories of AI work in various situations. Think of it like having a toolbox at your disposal. You wouldn’t want to use a hammer to drive in a screw for fear of the damage it could cause. Likewise, some AI methods are inappropriate in the wrong situation. Arthur C. Clarke famously wrote, “Any sufficiently advanced technology is indistinguishable from magic.” AI is far from magic and business managers may not understand AI and all its complexities but they should understand applicable use cases.
What are the biggest challenges in upskilling your workforce for an AI-centric future?
Maybe not a popular opinion but the current hype and treatment of AI as the silver bullet that can magically solve anything is hampering the upskilling of workforces. There is plenty of confusion around AI terms and methods. My suggestion is for higher education: universities should not just offer degrees in decision sciences like a Master’s in Data Science. Their business schools should treat AI as a skill set as they do with other business disciplines. I have never had a job in Accounting (hooray!) but as a business executive, I *am* expected to have a working knowledge of managerial accounting.
What ethical considerations does AI introduce into your industry, and how are you tackling these concerns?
In seeking revenue optimization and commercial excellence, pricing can introduce risks for bias or unfair treatment of people or organizations. Plenty has been said recently about the fast food chain, Wendy’s when they tried piloting dynamic prices to rise and fall with time of day demand. I’m not saying that Wendy’s was right or wrong, but one has to at least ask ethical questions in these types of situations. It makes sense from a microeconomic standpoint to vary pricing based on supply and demand, but could this unfairly affect some groups? Within the B2B part of the value chain, however, there’s very little risk of racial or socioeconomic bias in a predictive model for manufacturers and distributors. Those factors simply aren’t part of modeling. But, new developments could introduce risks, so it’s important to stay vigilant and keep the “Responsible” in front of the “AI.”
What are your “Five Things You Need To Do, If AI Is Disrupting Your Industry”?
1 . See the opportunity. In a nod to the great Stoics, you can’t control much of what happens to you in life, but you can always control if and how you react. Recognize the disruption AI is creating and treat it as an opportunity. It may not be an opportunity for you yet, but that’s what you need to examine: who will this benefit and how? How might this change the dynamics in our industry?
2 . Know the capabilities. Become proficient in *applied* AI. I don’t mean to know the nuances of building complex models or data science techniques yourself. Instead, understand the capabilities — and the limits — of AI and machine learning. Don’t be the one that’s proud of your hammer as you bang away at a Phillips-head screw. This doesn’t mean going back to college. It could be free, virtual courses from LinkedIn or other free or nearly free sources. There are a lot of them out there.
3 . Experiment. Try out the widely accessible forms of AI, especially the easy-to-interact Generative AI solutions like ChatGPT, Copilot, and Gemini. Mere interaction and play can lead to “aha” moments for people who are not data scientists or even quants. Experiment with the many tools available to see what’s possible and use your imagination.
4 . Collaborate in Community. Join or set up an affinity group within your organization around AI. You can do this easily with MS Teams, Slack, etc. Crowdsource skill acquisition and lean into group discussion to accelerate your (and others’) knowledge. Where I work, we also created sub-groups based on different aspects of AI including the possibilities and the risks.
5. Consider (and reconsider) your business model. Study how AI might change the fundamental business models in your industry. Could new insights change how customers purchase goods and services or alter the competitive landscape? Will the supply chain be fundamentally altered? Data insights could fuel new value-added services and revenue opportunities. Are there other ways your business can stay relevant and competitive by leaning into AI?
What are the most common misconceptions about AI within your industry, and how do you address them?
- All AI is created equal for my use case. Certain types of AI are good for some things, and really, really bad for others. Many people don’t know the difference and hope that by sprinkling a little AI magic on the problem, they’ll end up in a better place. An efficient misuse of AI could actually deliver bad results faster. A simple example today would be that an LLM (Large Language Model) like ChatGPT, is amazingly good at understanding language and creating natural language responses, but they’re not appropriate tools for quantitative modeling or forecasting. As one of my mentors once said to a client when asked if you could use AI to do the wrong things, he quipped “Well, there’s ultimately no substitute for knowing what you’re doing.” Instead, get to know the varied types of AI.
- My data must be perfect. Many manufacturers and distributors say they can’t leverage AI because their data isn’t clean. The saying ‘don’t let perfection be the enemy of better’ applies here. Computing power and software toolchains used for developing and operating AI applications have gotten so much better in recent years. While the concept of garbage-in, garbage-out is still a good maxim, you will never have perfect data. In most cases, you can leverage the data you do have to gain better insights and results than you have today. As is often quoted, “A journey of a thousand miles begins with a single step.”
Can you please give us your favorite “Life Lesson Quote”? Do you have a story about how that was relevant in your life?
Ferris Bueller, “Life moves pretty fast. You don’t stop and look around once in a while, you could miss it.” Or a similar one… “The days are long, but the years are short.” In my professional, personal, and family life (married 26 years soon, and 3 kids in college) I can think of many situations where I can get bogged down trying to grind out a solution to something or work through a challenge. When I look up, things seem to have flown by. So take the time frequently to pause and enjoy the ride while it lasts.
Off-topic, but I’m curious. As someone steering the ship, what thoughts or concerns often keep you awake at night? How do those thoughts influence your daily decision-making process?
Leaders have plenty of options for what to worry about. I am mostly concerned about whether our organization can continue to adapt and evolve as people in our collective enterprise. I worry less about tools or processes. Mindset changes and behavioral changes are difficult to accomplish for any organization. How can we collectively change for the better? Whether that means changing a business practice, learning a new domain or skill, or just embracing change itself. How do I need to play a role in that?
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people, what would that be? You never know what your idea can trigger.
So many choices here! Because it’s an election year in the U.S., I will highlight a civic movement. I would love to see steps toward a new standard of civility in public discourse. I worry that the public discourse has become so toxic that in our democratic society, the best ideas are not even being heard. We will never all agree on much of anything but if we can’t even discuss the issues in public forums like political “debates” (quotes used for sarcasm) then we’re in trouble. Perhaps we should have major political debates utilize the Oxford-style debate — it ensures that each point of view is actually heard and debated in a civil manner.
How can our readers further follow you online?
Through LinkedIn at www.linkedin.com/in/alexhoff/ or some of our blogs on www.vendavo.com/blog
Thank you for the time you spent sharing these fantastic insights. We wish you only continued success in your great work!
Thank you!