Reprinted with Permission from Precision . Originally published June 5, 2026.

View the presentation: This article is based on Chris Hunsaker’s presentation at the 2026 Ag Equipment Intelligence Executive Summit. Readers can access the full slide deck .

Editor’s Note: Chris Hunsaker, founder, Acuitus Ag, led an autonomy presentation and ensuing discussion for manufacturers, dealers, distributors and suppliers at the inaugural Ag Equipment Intelligence Executive Summit on May 20, 2026, in Chicago. 

Is this the end of the Iron Era?

The business model of farm equipment is well understood. Sell machines, sell parts, sell service. Overall growth is generally dictated by the replacement cycle, which is well-documented to be about 4% annually.  Growth rates higher than 4% can be achieved, but it usually comes at the expense of some other value stream. New product launches might outperform this; but growth always reverts to the replacement cycle’s cap eventually.

OEM equipment gross margins generally fall in the 20-35% range — with some specialty crop implements approaching 50%. Sales channel gross margins are generally lower and vary depending on channel structure, geography and crops. Parts and service margins are higher, but still nowhere near what’s emerging.

And what’s emerging? Honestly, it’s fluid and far from settled. But what IS clear is that the NATURE of what’s emerging changes the game entirely.

Here’s the reframe. The customer used to amortize equipment cost over as many acres as possible, and the value of the job done by the equipment was well understood by the customer. The manufacturer would develop new machine features or new models based on whether or not they could hit the gross margin they required to keep their businesses in the black.  Distribution would mark up the equipment on a similar basis. New solutions are hired to do a job — the combined work of multiple machines, several humans and workflow steps the equipment seller doesn’t currently touch. The customer judges value by outcome, not by the asset. Suppliers (software, automation-as-a-service, data analytics, uptime guarantees) price as a function of that outcome, capturing a share of the efficiency, quality or throughput gains. Because those gains are so large, they can afford real innovation risk, and gross margins run 65-85%+. These solutions win on both sides of the economics equation — on price because more value is delivered to the customer and on cost because once software is written, the marginal cost to sell the next instance is near zero. Add subscriptions on top — the customer doesn’t pay everything up front, adoption gets cheaper, the developer stays incentivized to keep improving — and steady, predictable cashflows make innovation less risky to fund.

The old paradigm of software in ag equipment was that software was used to enable your machine. That paradigm is dead. The new paradigm is that your machine is the hardware that enables the intelligence of software. If you’re still the company only selling iron, you’re not just losing on margin. You’re losing on scope. Someone else is capturing value across a much wider piece of the operation than you ever did.

This leads to a critical question. In 2030, or even just in a couple of years, will your company still be selling iron or will it be selling outcomes?

The engine driving all of this is autonomy. Software pricing, outcome contracts, bundled value capture, none of it works without autonomy as the underlying mechanism.

There are four main forces shaping autonomy in ag:

Four Forces

The first force I’ll call the AI catalyst. This force makes everything else relevant.

Five years ago, autonomous perception required about $100,000 of LiDAR hardware and sensors. Today, AI is making a $500 camera see the same way that the $100,000 LiDAR stack could. Perception cost has collapsed, which removes a significant barrier to entry.

The second force is capital intensity. Building autonomous machines from the ground up takes huge amounts of capital and conviction. In the self-driving car business, for example, Tesla is the only startup that’s been profitable so far. One of its highest profile competitors, Waymo, is not profitable because the cost of its vehicle is significantly higher than Tesla’s. Even so, Tesla has only survived up to this point because Elon Musk was willing to stake his entire fortune at near death experience moments during the company’s history to get it to where it’s at.  

In ag, a Tesla-equivalent doesn’t exist yet, and even if it did, it would only solve half the problem. Self-driving cars are the tractor autonomy problem.  There is no automotive analog to the implement autonomy problem, and as such, application of the self-driving car playbook stops at the drawbar.  Incumbent OEMs in ag have a significant defensive moat around their businesses that’s underappreciated. They’ve already invested deeply in design, manufacturing and distribution. They have this moat, startups do not.

The third force is marginal vs. revolutionary innovation. Ag OEMs have built their businesses on true innovation. Much of that innovation came from upstart shortliners. However, what was innovation at some point has become incremental improvement, and that’s dictated by the economics of legacy machines that are in service, dealer networks and risk-averse corporate cultures.

Right now, tech is creating a new set of economics independent of the old economics, and this is a revolution that’s justifying innovation and risk taking for anyone who can see it.

The fourth force is institutional inertia. Ironically, the mass of investments already made by incumbent OEMs that give them a defensive moat also make strategic course changes really difficult. As the saying goes, it’s hard to turn a battleship around in a bathtub. Can OEMs adjust fast enough? That’s the question at the front of everybody’s minds as we see what’s emerging. Startups are lean, nimble and unencumbered by any of that institutional inertia.

The hidden asset that’s key to autonomy overall is the operational data of the machines that are currently in the field and being produced. If you don’t have that, automation is ridiculously harder to achieve. Startups without domain knowledge stall because they can’t reverse engineer years of experience with implements operating in the field. Each OEM holds the key to their own data, and many haven’t unlocked it yet. They’re not even capturing it yet. Startups that are coming to the market are capturing that data from day one. Given these forces, how does autonomy play out from here?

The Four Paths to Autonomy

Four distinct paths to autonomy are emerging and each one has different winners, timing and barriers.

1. Integrated Autonomy. An autonomous CNH tractor was announced at the Farm Progress Show 10 years ago and it got a lot of attention at the time, but it was also a little bit of a “getting over your skis moment” because it hasn’t been shown publicly since that year.

Ironically, CNH built the tractor, but they didn’t build the autonomy stack. That was built by a company in Logan, Utah, called Autonomous Solutions.

There’s a lesson here. The speed and complexity of this path are tricky. Incumbents struggle because cultures that are built to support marginal innovation and institutional inertia make it really hard to shift gears.

Several startups have tried fully autonomous tractors, and they’ve also struggled because of sky-high capital requirements and non-existent distribution. But even if OEMs and startups overcome these challenges, they’d still have an issue because again, tractor autonomy alone isn’t of much value if you can’t solve implement autonomy.

1. Retrofit Kits. John Deere has made attempts at ground-up integration of autonomy, but they seem to be leaning into the second path, which is retrofit kits. This path appears to be where a lot of the action is right now in the industry.

Deere’s second gen retrofit kit is supposed to be available this year, but there’s a lot of cost that goes into this. I look at Deere’s current offering as being like the Waymo of ag autonomy. It’s built on older technology because of risk aversion, and it’s what’s available in the supply chain.  The AGCO PTx Outrun Autonomous Grain Cart system is available, and I’ve seen that one in action. It’s impressive, and they’ve made some smart design choices in the architecture that kind of break out of this mold of just taking what’s in the standard supply chain and running with it.   

There are also autonomy startups like Carbon Robotics, Sabanto and Blue White with machines already in the field. The startups are moving faster with less complexity, and they have a lower price point. OEMs might lead today and they might have an advantage because of their manufacturing and distribution, but will that last?

1. Purpose Built Platforms. One example of this is the GUSS autonomous orchard sprayer, which is already being used in hundreds of fields.  

Startups with technical expertise and agility are owning this path right now because they’re coming up with novel solutions. But implement OEMs with the right domain expertise could absolutely play in this space as well. Interestingly, the major OEM reaction to GUSS was a Deere partnership that quickly turned into a full acquisition in the fall of 2025.

1. Humanoid Robots. This path might come at you out of left field. It’s a huge wild card. If humanoid robots crack general perception and manipulation, they retrofit any existing tractor implement without any redesign. They capture the high margin intelligence layer, and traditional OEMs are at risk of becoming contract manufacturers. I believe this threat is dramatically under-discussed in ag right now.

Regardless of the path forward, consider one other key insight. Using the Tesla/Waymo example again, Tesla has made a gigantic bet that simple perception with cheap hardware will be sufficient to solve full autonomy, while Waymo believes it can only be solved using significantly more expensive LiDAR.

Deere, in particular, might be leaning more toward the Tesla model as its Gen 2 tractor autonomy kit consists of 16 cameras (no LiDAR) that see 360 degrees around the tractor. It also seems to be a starting point to solve implement autonomy simultaneously as the cameras can also see the implement.

Who Wins Autonomy?

The answer to the question is whoever solves implement autonomy first.

The value created in autonomy is all hinged on getting the operator out of the field. That unlocks labor cost savings, training cost savings, higher quality/more consistent work, 24/7 capacity beyond human limits, reinforcement learning and continuous improvement at scale.

An experienced operator might cost $30 or more per hour, and if you can’t get them out of the field, the math doesn’t math and most of the value remains untapped.

Tractor autonomy with manual implements is driver assist. It’s not autonomy, and it already exists.

It’s important to note that implement automation doesn’t have to be all or nothing. It can be done in steps. Start with a function where customer value is high or pain is sharp, like the following:

• Controls (architecture is future proof)
• Sensors (cameras, vision)
• Edge compute (data capture, inference and RL)
• Connectivity (remote monitoring, data transfer to cloud)
• Cloud architecture (analyze, coordinate and learn).

Build the stack once for one function, and it compounds across every function thereafter.  There’s some urgency in this. Inaction isn’t a neutral stance. Value is transferring to whoever owns the intelligence layer of the machines in the field and ultimately the autonomy. If implement OEMs don’t automate, there’s a risk that someone else’s autonomy will catch a large chunk of the implement’s value, leaving it nothing more than a commodity. If implement autonomy is the key to who wins, what does equipment look like when it’s solved?

Bigger Isn’t Always Better

There’s a startup called Aigen Robotics that deploys a solar-powered, lightweight autonomous weeder. Nothing about it looks like a traditional implement, and that’s kind of the point.

We can immediately start thinking about things in a different way when there’s no operator in the cab. One way is to think smaller.

If I have one machine that has a capacity of 100 acres per day and a 10% probability of breaking down, when it does break down, I lose 100% of my throughput. If I have 10 small machines each with a capacity of 10 acres per day and the same probability of downtime, when one goes down, I still have 90% throughput online.

Technology can also allow for more precision, which is in turn enabled by more compute power. As compute power gets cheaper, more form factors get tried. As more form factors get tried, more compute power gets deployed. This is an economic idea known as Jevons Paradox, which I first heard described in the context of explaining why building better and higher capacity roads never seems to reduce traffic. When better roads are available, people drive more and they’ll keep driving more until the traffic pain offsets the benefits of driving. The same thing is happening here. The more compute that is available for a cheaper cost, the more it will be deployed, which in turn is what enables more exotic and innovative precision automation.

Bigger built this industry over the course of decades. Smaller might be something that rebuilds it, but there’s still one wildcard that could change everything.

The Autonomy Wild Card

A startup called Figure AI, one of the leading humanoid robot companies, has raised $2.5 billion to date and currently has its humanoids deployed in BMW manufacturing facilities. In early May 2026, it had a live feed of one of its humanoids sorting packages in a warehouse. The humanoid was tasked with figuring out which side of the package had the shipping label on it and orienting it face-down on the conveyor. I checked the feed one morning, and the humanoid robot had sorted 204,000 packages in just under 164 hours. No breaks, no workers’ comp claims, no managerial issues. The humanoid was doing roughly one package every 3 seconds. They ran it head-to-head against a human intern, and the intern barely beat it by a couple dozen packages over 12 hours. These robots are getting smarter because of recursive and reinforcement learning. Tesla, Figure AI, Apptronik, Hyundai and many others are pouring money into this. The total global investment in the space to date is estimated to be between $30-$50 billion and a third of that is attributed to pure startups. If any of these companies solve general perception and manipulation in unstructured environments first, every form factor argument that I made earlier breaks. Existing tractors and implements become autonomous without any further redesign. This is the main justification behind the massive investment in these products. I believe these humanoids are already good enough to manipulate tractor controls. The question is whether you can train the humanoid to watch the implement and operating environment like a human. The short answer to that question is if you can see it with your eyes, you can train a camera to see it, too. OEMs’ unique access to machine data in their specific domain can become a real strategic asset in this endeavor as this training plays out. It’s something they’re closest to and have more access to than anyone else does — if they’re capturing it. Even if half of this is right, an OEM’s strategy probably needs to consider a humanoid element, and there’s value in capturing existing machines’ operational data regardless of how autonomy shakes out. I think this is an OEM’s call option on the future.

Opportunities & Threats

Everyone in the industry has a specific opportunity and threat when it comes to autonomy.

Dealers’ business depends on iron volume and parts and service right now. As outcomes get sold, that backstop weakens. Dealers have a local presence, customer trust and operational knowledge that nobody else in the chain has. The opportunity is to stop only selling iron, lean into technology and become the deployment monitoring and uptime partner for whatever runs in their territory. If they don’t, somebody else probably will.

Tractor OEMs have the brand, the channel and the balance sheet. What they don’t have yet is integration completely past the drawbar in all cases. But tractor autonomy alone is a little bit of a cap. The win is to own the implement plus tractor system as one integrated outcome. Either build the autonomy in-house or partner deeply and quickly with the people who already have the domain expertise. The Deere acquisition of GUSS in 2025 is one template. AGCO’s partnership with Trimble is another.

Implement OEMs have the domain expertise that others don’t, and they have access to operational data if they’re capturing it. They’re potentially the difference maker in all of the autonomy paths, regardless of how they play out, and that opportunity is enormous. But the threat is equally enormous. If implement OEMs don’t own autonomy for their space, their implement becomes a contract-manufactured commodity bolted onto someone else’s autonomy stack. Forgive me for being blunt, but there may not be a second chance to rectify that.

Startups have the shortest distance to travel on the autonomy path, but they lack scale, and that’s a big hurdle.

Every segment in the industry can choose their path, but there’s no neutral position on the autonomy spectrum. Standing still is still potentially moving backward. 

Five Strategic Questions

At the end of the day, there are five big questions to consider:

1. In 2030, what percentage of your revenue comes from autonomy?
2. Are you positioned to own implement autonomy — or lose it?
3. What’s your machine data strategy — do you have one?
4. Who’s your partner for what you can’t build alone?
5. How does your business change if humanoid robots arrive in five years instead of 15?

Ultimately, I don’t know who wins in ag autonomy. But I’m sure of this — the winners will be the ones asking these questions out loud inside their companies before the answer gets forced on them.

Chris Hunsaker is Co-founder and CEO of Acuitus Ag, a software company engaged in improving the efficiencies of the world’s agricultural operations.

| Member since 2023

by Jeremy Groeteke, Senior Global Leader at Syngenta and speaker at the 2026 Supply Summit & Showcase

When I spoke at the FEMA Supply Summit, I opened with the original “See & Spray” — a young entrepreneur with a shortliner Bean Buggy, deciding plant by plant what needed attention. What’s old is new again. The difference today is that the intelligence we once carried in our own heads is moving into the iron itself.

I’ve spent 25 years at the intersection of agriculture and technology — as a farmer, agronomist, scientist, and now a technologist. From every one of those seats, the same conclusion holds: agriculture is naturally AI-compatible. AI thrives in complex, uncertain, data-rich environments, and there are few environments more complex than a field. What has changed is that you no longer need to know how to perform a task yourself in order to automate it. AI lets us automate the indescribable — and that puts entirely new targets on the table for the machines you build.

Consider the scale. The U.S. plants roughly 90 million acres of corn a year — about 2.9 trillion plants. A single petaflop of compute could analyze that entire crop 370 times every second. As I discussed the compute capacity as gone from one microscopic cell to one stalk, took about 60 years of compute science; to go from that single stalk to compute capable of “replanting” America’s entire corn crop 1,700 times took us about 13. The curve isn’t just steep. It’s accelerating, and the slope is the point.

Here’s what equipment makers need to internalize. The Internet changed distribution. The cloud changed scalability. AI brings automated intelligence directly into our products. The merger of iron and AI — robotics, new sensors, computer vision, and large language models working together — is no longer a lab demo. It’s becoming the spec sheet. The machine that perceives, decides, and acts in the row is the machine our customers will increasingly expect.

The industry already feels it. In recent research, 91% of agricultural leaders said AI is moderately to very important for staying competitive — ahead of traditional levers like capital and operational excellence. They expect yield gains, better margins, and sharper decisions. But the most cited barrier is unproven ROI, and nearly 80% believe AI’s impact will come down to quality of execution, not the technology alone. That tension is the whole story: the same leaders who see AI as their biggest opportunity also name it as their biggest risk.

So where should we focus? My advice is to treat efficiency as the entry point, not the prize. Trimming cost with a smarter sprayer or an automated parts lookup is real, but the durable value is in growth — new products, new services, and business models that didn’t exist before the intelligence was in the iron.

Winning will be as much organizational as technical. The companies pulling ahead operate on what I call geek norms: clear ownership, openness to pivots, decisions settled by evidence, and a bias toward speed — plan a little, iterate a lot, and always know the ROI of your AI. As I compared SpaceX and Boeing on NASA’s crew program; the younger, faster, evidence-driven organization is now the routine provider while the legacy player is still closing out certification. The lesson for our industry is blunt.

Don’t show up to fight yesterday’s war. The iron isn’t going away — but the intelligence riding on it is about to define who leads. Start now, start specific, and build the foundation before your competitors do.

Jeremy Groeteke is Global Head of IT & Digital Strategy for Syngenta Group’s Vegetable and Flowers business, where he leads AI, data, and digital agronomy initiatives that drive profitability and sustainability. With more than 20 years of agribusiness leadership experience, he has led multimillion-dollar product launches, built global teams, and forged key industry partnerships.

The šűśł´ŤĂ˝ (FEMA) Technology Council is seeking industry thought leaders to share insights, experiences, and topic suggestions for our special Technology Track at the Supply Summit & Showcase on Thurs., March 26, 2026 in San Antonio TX.

The annual FEMA Supply Summit & Showcase on March 24-26 is an opportunity for suppliers and potential suppliers to network with FEMA members, discuss relevant industry topics, and showcase products and services.

The goal of the Technology Track is to educate FEMA membership about new or emerging technologies that have a direct impact on their products and services. The rapid advance of technology has our industry questioning many things, including the following:

• What do we need to do to be prepared for an autonomous future?
• How do we begin to integrate more technology into our equipment?
• How can software help me control the costs of automation?
• What role does AI play in automation and equipment design?
• Will our end customers pay for a more technologically advanced piece of equipment?
• How will I service and support my machines in the future if they have more tech?

Presentations will be grouped into two subject areas:

• Technology as it applies to member products
  (technology integrated into equipment and the features/functionality offered to end customers)
• Technology as it applies to member production/internal processes
  (technology used within manufacturing operations, engineering, design, and internal business systems)

This track will focus on topics that address these and other relevant questions surrounding the use of technology in farm equipment. We also aim to inspire FEMA member engineers to engage more deeply with our Association by providing more relevant, technical convention content, and fostering valuable networking opportunities.

Who should consider presenting?

• Technology suppliers (hardware, software, engineering services, etc.)
• Academics or thought leaders with interest/expertise/research relevant to the questions stated above.
• Universities and research institutions with relevant research, projects, or learning opportunities in the two subject areas above and project collaboration opportunities related to Farm Equipment technology.

Why participate?
This is your chance to:

• Showcase your expertise and innovative ideas.
• Connect with industry leaders.
• Raise your company’s/institution’s profile.

Who is your audience?

• Executives and technical/product leaders of farm equipment manufacturers, their suppliers, and their distribution partners.

To be considered, submit the following:

• A 250-word summary of your topic, demonstrating its relevance to the stated Technology Track goal above.
• A brief biography, highlighting your expertise and speaking experience.
• No more than five bullet points summarizing what the audience will learn from your presentation.

General presentation guidelines to keep in mind as you prepare your submission:

• Presentations should be about products, services, or topics relevant to farm equipment manufacturers and their use of technology in their own businesses (either internally or as part of their product and service offerings to their dealers/customers).
• Presentations should be engaging and educational—not simply sales pitches. We want our members to learn something about technology they hadn’t considered before.
• Use of slides is encouraged, as are references to case studies, research, etc. that support your topic of discussion.
• Speakers will be given up to a 45-minute timeslot—35 minutes to present with approximately 10 minutes of Q&A with the audience at the end.

Learn more and apply here.

For questions on submission content, email Chris@Acuitusag.com or call the FEMA office at 314-878-2304.

Submit your proposal to Gina@FarmEquip.org by Friday, January 23, 32026 for a chance to present at this can’t miss industry event!

by Gene Marks, 2025 Marketing & Distribution General Session Speaker. This article was originally published in .

A recent survey from Goldman Sachs found that about 68% of small-business owners are using AI — specifically generative AI “assistants” — and another 9% plan to begin using it within the next year. Whether it’s ChatGPT, Microsoft Copilot, Google Gemini, Claude, Grok, Perplexity, or other popular platforms, AI can make a significant difference to your business.

Here are six business uses that will help you increase the productivity of both yourself and your team.

Chatbots for your website: There are a number of customer service platforms targeted at small businesses like Talkdesk, Zendesk, Salesforce, and Zoho that enable their customers to easily deploy chatbots on their site that can automatically answer visitors’ questions or pass on the customer to a human agent for further help.

“As these tools become more advanced, they are taking on a bigger role in customer service processes,” said Charanya Kannan, chief product, engineering, and customer officer at Talkdesk. “As today’s economy drives labor and skills shortages that hinder customer experience, it’s more important than ever that companies not only invest in automated tools.”

Restaurant Reservations: Sydney Grims, who is the director of business development at Fearless Restaurants, a regional restaurant chain with 14 locations including popular spots like White Dog Cafe and Moshulu, uses an AI platform called Slang.ai to take reservations and answer questions 24/7. Staff can now focus on serving their customers without phone calls distracting them.

“This is not to reduce head count,” she said. “We cannot get rid of greeters. Our goal is to provide easy answers to guests quickly. We had over 250 coinciding calls at one restaurant in a week … that’s about a thousand covers we would just lose without this platform.”

Improving communications: AI assistants have become good at transcribing and summarizing online meetings as well as polishing emails, writing memos, and tightening other written communications.

“Writing emails is a lifeline, but is often time-consuming when running and scaling a business,” said Kevin McLaughlin, a partner at Philadelphia-based accounting and advisory firm Centri. “We use ChatGPT to assist by quickly drafting emails that capture the right tone and message, which the owner can then personalize in minutes.”

Company policies: I always tell my clients to never allow a single contract, agreement, quote, or bid to leave their office without uploading to their AI assistant for comments. These platforms have become well versed in spotting problems, identifying issues, and suggesting ways to not only improve your corporate documentation but to identify risks and protect you against potential liabilities.

Neha Arnold, CEO of Sedna Consulting Group, a technology consulting firm based in Edison, N.J., agrees. She frequently has her team use an AI assistant to help with corporate documentation by double-checking outputs, and acting as both a creative and analytical assistant. But she’s also careful to not rely on the platform entirely.

“It’s important to understand AI’s limits,” she said. “Recent studies show that AI models can falter with complex reasoning. It’s best used to support — not replace — human insight.”

Marketing materials and blogs: Howard McCabe, founder and creative director of Dream Syndicate, a brand specialist based in Philadelphia, leans heavily on AI to write rough scripts, create graphic styles, come up with content ideas, design and generate voice-overs and custom music.

“When shooting a commercial if we miss a line of copy, or the talent mispronounces something, we can train the AI on the particular actors’ nuances and then the AI can generate a new scene with the talent saying the missing line or correcting the mispronunciation,” he said.

“In addition, now we are able to have the talent speak in a different language … so if we produce a commercial in English, we can then very quickly generate that same commercial in a different language.”

Not everyone’s in the content creation business, but with image and video creation platforms like Sora, Google’s Veo, and others, you can publish professional content that can help set your company apart from its competitors.

Market research and competitive analysis: Your AI assistant is a trained adviser who has the entire internet at its immediate disposal and can discuss — just like a human — any business problem, issue, or challenge. I have clients that are leaning into AI assistants for help researching markets, products and competitors, and brainstorming ways to stay ahead.

Arnold said he uses his AI assist to provide critical feedback by prompting questions like “Why do you recommend this approach?” or “What are the alternatives?”

Other business owners, like Russ Napolitano, a partner at the Philadelphia marketing agency Xhilarate, uses an AI assistant for client discovery and meeting preparation.

“Before meeting with a prospect or client, we use ChatGPT to conduct quick discovery — researching their industry, identifying potential challenges, and surfacing relevant trends,” he said. “It’s like having a research assistant who can brief us in minutes.”

AI cannot be relied upon 100%. But what adviser can?
Even in its current infancy there are plenty of ways for today’s AI chatbots to help in your professional life.

“We’ve been utilizing AI across our operations for quite some time, and it has truly transformed the way we work,” Arnold said. “Personally, AI has allowed me to save an average of 60–75 minutes every single day, time that I can reinvest directly into accelerating business growth, deepening client relationships, and exploring new opportunities.”

Don’t miss Gene’s presentation on Navigating 2025 and Beyond: Strategic Moves for Manufacturers on Wednesday, October 29 at the Marketing & Distribution Convention in Las Vegas. Learn more at .