Leadership Perspectives

Howard Pressman is a technology product executive with over 25 years of experience driving product strategy, operational efficiency, and business-focused IT solutions. At Bimbo Bakeries USA, he spearheaded groundbreaking advancements in forecasting, market execution, and frontline enablement, impacting iconic brands like Thomas’, Sara Lee, and Entenmann’s.

Howard is known for scaling impactful solutions and leading high-performance teams. He has held various product management roles at Elsevier, Radial, and Motorola. He earned his MS in Technology Management from the Wharton School of the University of Pennsylvania and his BS in Electrical Engineering from Drexel University.

Over the years, I’ve had the privilege to build and scale technology in industries that aren’t traditionally tech-centric, which means one thing: the tools have to work for the people using them. In sales tech, especially in the field, the key driver is always user motivation. Field reps aren’t there to marvel at dashboards; they’re focused on executing quickly, minimizing out-of-stocks, winning display space, and getting home on time.

So, the tech has to be intuitive, lightweight, and laser-focused on the why. Why does this tool help them sell more? Save time? Make their life easier? It’s critical to recognize that a rep’s motivation isn’t always the same as the company’s broader strategic goals. That’s why it’s essential to understand both perspectives and build products that align user behavior with business value.

To that end, I prioritize solutions that slot seamlessly into daily workflows and speak the language of results. It’s not just about technology; it’s about problem-solving in context. For example, can the system guide a rep to the right store with the right product at the right time? Can it alert them to missed opportunities or streamline how they prep for a promotion?

The secret is to align user intent with business outcomes through tools that are simple, smart, and deeply connected across the ecosystem. Asking “why” before “how” is what ensures you’re not just deploying tech, but delivering impact.

Technology success isn’t measured by features delivered; it’s measured by outcomes achieved. The real question is: did it move the needle? Whether that means driving sales, reducing waste, or improving operational efficiency, the metrics need to tie directly to business impact. To find if the tech is helping, you need to correlate the data and find the causes and leading indicators to measure.

For example, if the goal is sales growth, you don’t just look at topline numbers; you break it down. What actions did the technology enable that contributed to that outcome? Did it help get product on shelves faster? Improve forecasting accuracy? Streamline a rep’s workflow? Give them access to information that made their merchandising better? What are the measurable metrics that fed into the result and caused a better outcome?

Of course, outcomes are often influenced by variables outside of the tech itself: competitive activity, store conditions, or weather patterns. So, the key is isolating what’s within your control. You identify the high-signal metrics that correspond with behavior you want to influence, but be careful to only use ones that truly cause the result. Some of these might include:

● Delivery volumes and patterns across the week. Are they making lots of random changes, and are they delivering the right volumes on the right days?

● Promotional compliance: Are displays executed and materials accessed?

● User engagement: What tools are being used, when, and by whom? Which metrics or dashboards are being looked at, what materials are opened, and what actions are logged?

● Quality-of-life indicators, like Net Promoter Score (NPS), or even informal feedback on whether the tools are making people’s jobs easier.

Digital transformation, in particular, demands a before-and-after lens. Are people doing things differently? Is the organization learning faster, acting faster, adapting faster? You have to compare adoption behavior and performance metrics pre- and post-rollout, and go beyond dashboards. Spend time in the field. First-hand feedback is important if you want to be sure to hear the truth. Watch how the tech is being used or avoided. Learn from the extremes: who’s succeeding with it, who isn’t, and why? Look at the data outcomes of the different groups and correlate them to their interactions with the technology.

Transformation isn’t just about deploying new tools. It’s about shifting mindsets and workflows in a way that sticks. The moment you start seeing best practices scale and users advocating for the solution on their own, that’s when you know the change is real.

There are two real challenges in technology implementation from my perspective.

Challenge One: Integration
Building or buying the tech is just the start and the first half of the equation. Making it meaningful requires connecting it into your data ecosystem, workflows, and processes without slowing anything down. Smart data architecture is essential. You need real-time data where it matters and asynchronous updates where it doesn’t. Overengineer this, and you’ll burn performance. Underengineer it, and your solution becomes noise. Either way, bad integration kills credibility.

Challenge Two: Adoption
This isn’t a 50/50 split. I like to say user adoption is 75% of the challenge, and I’m only half joking. You can’t just drop tech and expect behavior to follow.

There are two strategies here for adoption, and you may find that a little of each is your best bet: pull and push.

Pull works when the value is obvious and immediate. You make users want to engage by showing them what’s in it for them. Get in the field. Listen. Find champions. Turn success stories into momentum. Peer pressure and FOMO are underrated adoption tools. And when you connect usage data to real business wins, you get buy-in from both users and leadership.

Push is needed when the value isn’t immediately obvious, or the tool supports compliance or standardization. In those cases, you need to hardwire it into the process. Make it the default. Monitor usage. And always close the feedback loop so users understand why it matters and how it’s helping them, not just the business. A great component here is to use the competitive nature of sales teams. Publish metrics and reward better outcomes on those causal metrics we talked about earlier. Everyone likes to be on the top of the list. But even more, no one likes to be viewed as last. Fair warning, though, if you decide to go this route, the capability you are pushing had better be easy to use, add value, and not block efforts, or the user feedback will be harsh.

Bottom Line: A great tool that no one uses isn’t a great tool. Success is when the tech fades into the background and the behavior becomes the norm. Bonus Tip: Sometimes the answer isn’t a technology solution and that’s okay. Especially as your business digests new technology at its own pace.

AI and machine learning are no longer buzzwords; they’re becoming the operational brains behind smarter selling and have been in use for years. In the CPG world, where every store, shelf, and shopper is different, these tools help us move from generic assumptions and random “best practices” to precise actions.

Forecasting is the first obvious use case. Using sales data and hosts of other variables to algorithmically predict what will sell is, in itself, a powerful tool. And the number of data elements being added into the mix makes it very complex for people to layer them all in to modify algorithms and user interfaces. The real power is when systems can connect the dots between demand patterns, store-level variables, and on-the-ground execution, with these new data elements coming into play that weren’t previously available. You’re not just predicting what should sell, you’re guiding reps on exactly what to stock, when, and how.

What makes this powerful isn’t just accuracy; it’s actionability. Front-line teams don’t want to analyze data. Back to those motivations specific to the user: They want answers. Ideally, from a system they can trust more than anything. How many units do I order? Where do I place them? What’s the highest-impact action I can take right now?

That’s where AI shines. It translates complex, multi-variable data into clear, prioritized decisions. It doesn’t just forecast, it focuses people.

As adoption grows, we are seeing a shift from reactive to predictive to prescriptive action. That’s the future: systems that not only flag issues but solve them in real time, driving sales growth while reducing waste and effort.

Image recognition is one of the most exciting breakthroughs in CPG tech, because it finally solves a decades-old problem: how to understand what’s actually happening in each specific store, at scale, in real time.

Manual data entry has always been the bottleneck. It’s slow, it’s incomplete, and let’s face it: no one wants to do it. I’ve seen that firsthand in my experiences with front-line teams. But snapping a picture? That’s fast. That’s easy. The challenge with pictures has always been that someone has to look at them and discern the information from them. Or else, the front line knows they are ignored, and it becomes a check-the-box with not much value added. Now, with image recognition, that photo becomes structured, actionable data: facing counts, out-of-stocks, share of shelf, planogram compliance, shelf tag data collection, as examples, all become possible.

That unlocks three big wins:

1. Speed and Simplicity: Reps can capture store conditions in seconds. No typing. No checklists. Just point, shoot, and go. With shelf-stitching, a rep can walk down the aisle snapping the entire thing in seconds, and the system can stitch it together.

2. Smarter Execution: When AI sees what’s on the shelf, it can immediately guide next steps. Restock here. Fix that display. Reprioritize the planogram. It’s real-time course correction that was never possible before.

3. Behavioral Reinforcement: This one’s underrated. When reps know their work is visible and measurable, quality naturally improves. Use it for positive reinforcement, not punitive reaction. The tech becomes the coach, not the critic. It builds accountability and pride without heavy-handed management.

And the best part? You’re leveraging the devices and people you already have. It’s low friction, high return. Image recognition doesn’t just collect data—it closes the feedback loop and accelerates action. It’s a game-changer.

This is a fantastic question. Number one, of course, is if the vendor has the technology you need in order to achieve the business outcomes you want. Are they looking to the future and incorporating capabilities that you will want now, a year from now, and longer? That’s table stakes, obviously. But picking the right tech partner is about more than product features; it’s about long-term alignment and trust. This is a relationship, not a transaction. The real test isn’t how things go when everything’s smooth. It’s how your partner shows up when things get tough.

Here’s what I look for:

1. Roadmap Reliability: Do they ship what they say they will, when they say they will? Is there evidence of continuous improvement? Ask their customers. Look beyond the sales pitch. When was the last time they did platform upgrades? Are they deploying small enhancements or big capabilities? And how has the quality been on those releases?

2. Vision and Domain Expertise: Are they bringing you fresh ideas and market insight? Or are you constantly educating them on your space? Do they understand your questions, or do you have to explain things multiple times? Do they anticipate your needs and next questions? Great partners help shape your vision, not just follow your specs. If you do an RFP, you can see this in the responses pretty quickly.

3. Flexibility and Collaboration: Can they adapt as your needs evolve? Are they willing to customize, configure, or prioritize enhancements to support your goals? Rigid vendors break under pressure. Agile partners grow with you. There will be points where you will need a specific “something” that none of their other customers will. How will it work for you to get it? Better to know the answer from the beginning.

4. Data Integration Strength: Can they plug into your systems, pull the right data, and feed your insights engine without creating chaos? Do they have open APIs, and are they willing to extend them?

The truth is that implementation is expensive, and switching costs are high. So, choose someone who’s in it with you. Not just to sell software or services, but to co-create success. To fit into your business goals, model, and operational methods.

When things go sideways, and they always do, you want a partner who answers the phone, rolls up their sleeves, and solves the problem with you as another member of your team. That’s the difference between a vendor and a value multiplier.

Thanks so much. I really enjoy creating sales growth and operational efficiency, and doing it in a way that hopefully makes the lives of associates better in the process. It has been a pleasure working with StayinFront to build that type of solution. Please feel free to reach out via LinkedIn.