Artificial intelligence is widely touted as offering manufacturing and distribution organizations the agility and visibility that legacy systems no longer provide, but many deployments stall before reaching scale.

It’s tempting to buy a one-size-fits-all solution, marketed as a universal fit. But supply chains, while sharing broad functions like inbound logistics, plant scheduling and order fulfillment, remain deeply rooted in each company’s operational reality. Every manufacturer and distributor uses a unique blend of workflows, KPIs, business guardrails and tribal knowledge. Prebuilt platforms rarely honor these differences. That disconnect is at the heart of most AI failures.

Success rests on four positive factors — incremental transformation, non-disruptive architecture, deep knowledge-worker involvement, and a core foundation built on manufacturing know-how.

Incremental Deployment

Incremental deployment addresses real pain points, not abstract process maps. Leading manufacturers begin with their highest-impact bottlenecks — perhaps chronic inventory overruns from inaccurate demand forecasts, or rampant manual interventions that break order-promising workflows.

When one prominent manufacturer adopted a modular AI solution, their teams moved from spreadsheet-driven reconciliation to automated, real-time visibility within weeks. Instead of a sweeping overhaul, they fine-tuned forecast accuracy and automated replenishment logic, resulting in a dramatic reduction in inventory holding costs alongside improved customer fill rates.

Success stories unfold fastest when managers and executives sponsor deployments in their own real-world operational context and build momentum from measurable wins.

Non-disruptive Technology

Equally crucial is technology that augments existing systems rather than replacing them outright. Rip-and-replace projects have a notorious reputation in manufacturing circles for triggering extended downtime, resistance from users, and runaway investment. By contrast, modern AI platforms overlay intelligent automation on top of ERP, MES, and homegrown applications. This non-disruptive approach allows operations to continue uninterrupted, even as AI agents orchestrate procurement signals, sensor analytics and supply risk alerts, across siloed environments.

A leading distributor realized tangible results within a single fiscal quarter — not by uprooting their data architecture, but by unlocking actionable insights from fragmented order and fulfillment records. Their planners shifted from firefighting missed shipments to proactive customer commitments, all without pausing production.

Engage Knowledge Workers in Process

Engaging knowledge workers is critical for sustainable adoption. Manufacturing and distribution organizations aren’t run by dashboards; they’re run by the expertise, tacit skills and judgment of their people. AI must work alongside these teams, codifying best practices, surfacing root cause signals, and capturing the operational decisions that have always been the backbone of production and distribution. When platform deployments are driven from the ground up, tribal knowledge becomes an asset; machines learn the patterns and business rules embedded over years of experience, not just the explicit data inside an ERP.

A best-in-class allocation system deployed by a leading distributor didn’t just optimize product mix — it incorporated the nuances of regional price elasticity, channel cannibalization, and service-level commitments that spreadsheets missed. What emerged was a collaborative network with AI colleagues supporting, not replacing, operators and analysts.

Industry Smart Design

Technology designed specifically for manufacturing and distribution is the fourth pillar of successful implementation of AI. Industry leaders know their workflows aren’t generic; every compliance protocol, inventory segmentation rule and stakeholder reporting cadence is unique. Generic platforms force IT teams to spend endless cycles mapping product IDs and retrofitting standard fields, draining resources without extracting true business value. Instead, solutions developed by supply chain experts start with a deep understanding of industry context — embedding process optimization, changeover minimization, transportation modeling and demand irregularities native to the sector.

When a multi-market distributor sought to reengineer their pricing and allocation approach, off-the-shelf tools failed to capture the intricacies of cost-to-serve and regional capacity constraints. By shifting to a domain-oriented architecture, they rapidly calibrated their S&OP process, driving a multi-point uplift in both throughput and gross margin.

If AI is to become a growth engine for supply chain organizations, these four capabilities must converge. Deployments should be a series of pragmatic steps: solving business-critical operational challenges, not generic simulations. Platforms must be modular, working as intelligent overlays, never intruding on core transactional integrity. Success depends on elevating the judgment and expertise of supply chain professionals, not marginalizing them. Finally, tech providers need to demonstrate manufacturing and distribution DNA, offering architectures that operate within industry realities, not abstract technology templates.

AI pilots, when deployed outside a company’s day-to-day reality, inevitably hit roadblocks. They rarely scale or deliver sustained value. By contrast, incremental solutions grounded in real business pain are scalable, resilient and quickly embraced. When a global distributor leveraged supply chain AI to unify decision-making across production, inventory and field sales, their teams saw a measurable surge in revenue and profit margins. Likewise, a Top 10 manufacturer witnessed a transformative reduction in manual reconciliation hours, freeing up the operations team for strategic initiatives and shortening cash cycles.

Executives who prioritize these must-haves see AI evolve from a technology hype to a foundational driver of resilience, speed and margin optimization. Instead of endless IT projects, they build supply chain operations that can sense, decide, and act in real time — empowering every plant manager, buyer and planner to focus on what matters most. In the race to build resilient, scalable supply chains, these are the differentiators between those who lead and those who lag.

Manufacturers and distributors who ground their AI implementations in this framework won’t just keep pace; they’ll set the new standard for supply chain excellence.

Michael Romeri is CEO of A2go.ai.