AI in Manufacturing & Distribution: Solve the Right Problems Before Choosing the Solution

One of six exclusive executive roundtables at Community Summit North America 2026, “AI in Manufacturing & Distribution for Business Central“, underwritten by Cavallo, brought together executives for an intimate discussion with Aleksandar Totovic, Principal Product Manager at Microsoft; Dona Sarkar, Chief Troublemaker – Microsoft Enterprise AI Advocacy, Microsoft; and Dewain Robinson, VP of AI Analysis & Training at Dynamic Communities. Scott Vaughan, Chief Market Officer, Dynamic Communities Consultant, moderated the session.

Attendees included vice presidents, IT leaders, financial officers, and global supply chain executives from manufacturing and distribution companies spanning electronics, import and export, industrial manufacturing, vegetable growing and distribution, oil and gas, and more.

A common theme emerged: Organizations are moving away from legacy systems and looking for ways to leverage AI effectively.

Vaughan asked, “What are the friction points in moving to AI integration?”

Common Challenges

Attendees highlighted the complexity of managing disparate systems and how it led to issues with data cleanup. They agreed that establishing a single source of truth — reliable, accurate data in the right place — is essential to making meaningful progress with AI.

A clear strategy is equally critical: Organizations must define their goals, identify the problems they want to solve, and measure ROI. With employees using different tools, it can be challenging to understand which activities deliver meaningful results. Ultimately, success depends not just on adopting AI, but also on identifying where it delivers genuine business value.

Robinson emphasized the importance of clear objectives and adequate training. Without them, organizations risk creating bigger problems, repeating tasks, and producing inconsistent results. Successful adoption requires focus, preparation, and meaningful outcomes.

Solve the Problem Before Choosing the Solution

Attendees were certainly in consensus when it came to the importance of understanding a problem before introducing AI. Defining the outcome first helps ensure AI addresses genuine business needs, rather than becoming a solution in search of a problem.

Sarkar challenged conventional thinking about AI adoption: “We are just trying to solve the problem, but faster. But now we should be trying to build things that have never been built before.”

She noted that organizations must bring together not only their data but also their context. AI needs to understand the business, its processes, and the outcomes people want to achieve.

Is It Too Late to Get Started with AI?

Nearly half of attendees felt their organizations were already behind in AI adoption. Sarkar offered a different perspective: Organizations that skipped the early chatbot era and AI-generated slide decks may be well positioned for the next phase.

AI is increasingly capable of taking action and performing tasks, rather than simply responding to prompts or generating content.

Her message was encouraging: Now is a good time to start. Organizations should focus on solving the right problems, bringing together data and context, training employees, and delivering measurable business value.