5 AI Strategies Small Manufacturers Can Implement Today

STRATEGIES: AI? Overview, 1) Smart Cash Flow modeling & forecasting, 2) AI-driven Pricing strategy, 3) Copilot & Digital Assistants for Scale, 4) AI for Legal, Compliance, & Procurement Workflows, 5) Engineering Anomaly Detection + + + Conclusion: Crawl, Walk, or Run, but Start now !!!

What is AI?

AI (Artificial intelligence) is the capability of computational systems to perform tasks typically associated with human intelligence, such as learning, reasoning, problem-solving, perception, and decision-making.

Overview: Look At a Glance

These targeted AI applications can help manufacturers start their digital transformation without massive investment or major overhauls.

Digital copilots could save engineering teams up to 10 hours weekly by automating repetitive documentation tasks.
AI can help compliance and procurement teams zero in on the sections of contracts that require oversight or negotiation.
Smart pricing strategies can use AI to track cost fluctuations and flag contract inconsistencies in real time.

After several conversations with manufacturing leaders, I’ve seen a consistent theme emerge: “I know we need to start using AI, but I don’t know where or how to start.” The urgency is clear, and the interest in modernizing operations is strong. What’s missing for small to mid-sized companies is a practical entry point that won’t overwhelm teams already stretched thin.

The good news is that getting started with AI doesn’t require a full or massive budget. Still, unlocking Return on Investment (ROI) takes more than buying licenses or generic tools. It demands leadership commitment, thoughtful use cases, and a willingness to train teams. AI isn’t a plug-and-play solution; it’s a business strategy that requires follow-thru.

For many, the right approach is to begin with small, targeted applications that address specific pain points. Processes such as cash flow forecasting, optimizing pricing, and contract management are all prime AI targets that can produce real results. These smaller wins also build the foundation for long-term AI success.

The following five (5) initiatives offer a practical starting point and are areas that I’ve seen firms like yours find success. These tools are readily accessible and capable of delivering value today while preparing businesses for more advanced AI adoption later.

1. Smart Cash Flow modeling & forecasting

Cash flow is central to any manufacturing operation, but many companies still rely on outdated Spreadsheets and disjointed systems that struggle to deliver accurate forecasts. Maintaining financial visibility has grown more difficult as manufacturers navigate supply chain disruptions, shifting demand, and rising input costs. Without a clear view into incoming and outgoing cash, leaders are left making ill-informed decisions and risking unexpected shortfalls.

That’s where AI-enabled cash flow forecasting comes in. By applying machine learning to 13-week models and transaction-level data, manufacturers can gain real-time insights that allow them to anticipate liquidity needs, run stress-tested financial scenarios, and respond proactively. For example, Armanino helped one multinational manufacturer build a customized, AI-powered 13-week cash flow model that unified reporting across business units, currencies, & banks. What once took weeks to manually categorize, over 32,000 transactions, was completed in minutes using AI. The result was a single source that enabled faster decision-making and better capital planning.

Manufacturers don’t need to overhaul their systems to benefit from AI-enabled forecasting. Models trained on historical transaction data and operational patterns become more accurate over time, allowing finance teams to continuously improve their projections. This kind of predictive visibility helps leaders stay ahead and make more informed financial decisions in a fast-changing environment.

2. AI-driven Pricing strategy

Pricing has become harder to manage amid logistics challenges, fluctuating raw material costs, and rising input expenses. The return of tariff-heavy trade policy has only added to the pressure. Many firms were forced to re-assess supplier relationships and cost structures with little notice. In these moments, companies without a defined pricing blueprint couldn’t respond fast enough.

Building a more flexible pricing strategy starts with the fundamentals: 1) organizing pricing data, 2) identifying key cost drivers, and 3) pinpointing where slow decision-making puts margin at risk. From there, AI can help guide teams during real-time market conditions by tracking cost fluctuations and flagging pricing inconsistencies in contracts. When manufacturers prioritize clear pricing data and specific roadblocks, they’re better equipped to react, especially those managing large product lines across regions.

3. Co-Pilot & Digital Assistants for Scale

AI CoPilots and ChatBots act as digital assistants, helping employees automate repetitive tasks, access information faster, and stay focused on high-value work. These tools can be embedded into everyday platforms like Email and Productivity apps, meeting employees where they work.

One global tech manufacturer recently rolled out an AI CoPilot program across its engineering, legal, finance, and operations teams. In six weeks, Armanino onboarded 50+ employees and identified where CoPilots saved time & $$$. The results were clear: the engineering team alone reported saving up to 10 hours per person each week, with the majority of users saying they were very likely to continue using the tools long term. Adoption rates were highest in tools like Outlook, Teams, and CoPilot Chat, where employees could quickly ask questions, summarize documents, or draft communications in a matter of seconds.

This showed that adoption matters, and that simply buying licenses isn’t enough. Users need training to unlock the full benefit. Treating CoPilots like any other transformation initiative, with intentional rollout and support, ensures the investment delivers lasting value to teams.

These tools aren’t designed to replace people. They extend human capabilities and give teams bandwidth to center on work that requires human judgment. As responsibilities grow, copilots help support daily tasks and make it easier to scale operations.

4. AI for Legal, Compliance, & Procurement Workflows

Contracts, compliance, and procurement involve some of the most detailed and resource-heavy processes in manufacturing. Contract review alone can involve hours of scanning dense language and cross-checking clauses for potential liability. AI-powered clause analyzers can scan documents, identify unusual or high-risk terms, and provide easy-to-understand summaries instantly. Rather than reviewing each line manually, teams can zero in on the sections that require oversight or negotiation. These tools also accelerate vendor on-boarding and shorten negotiation cycles.

Beyond contracts, AI can support procurement teams by automating repetitive processes like Invoice Approvals and Purchase Request routing. In one case, Armanino helped a global manufacturer deploy automation and AI to process more than 20,000 invoices and purchase requests annually. As a result, they reduced invoice processing time by 30 % and cut manual procurement steps by 70 %. Less paperwork means more time for strategic procurement decisions.

5. Engineering Anomaly Detection

Tight timelines and high testing volumes are constant challenges for engineering and product teams. Detecting flaws early in data is critical to avoid costly rework or delays later in the production cycle.

AI can help streamline this process. One tool known as a Test Data Watchdog Agent is designed to scan engineering test results, flag anomalies, and organize findings into structured, ready-to-review reports. Instead of sifting through raw test output, engineers receive a clear summary of what deviated from expectations and why it matters. This improves design accuracy and ultimately helps teams get products to market faster.

The key to success is embedding AI into the existing Engineering workflow. When tools like this are treated as part of the everyday process and not as a standalone initiative, they yield concrete value without disrupting how teams work. Staying competitive in today’s market depends on speed, precision, quality, & embedding AI directly helps teams deliver on all three.

+ Conclusion: Crawl, Walk, or Run, but Start now !!!

Implementing AI in manufacturing doesn’t require a full digital overhaul on day one. The best approach is to start small, using a strategy that surfaces real pain points and scales overtime. Before jumping into tools and platforms, manufacturers should identify high-impact use cases tied to existing gaps. This helps uncover opportunities that may not be immediately obvious and ensures teams move intentionally. Organizations that begin with a clear roadmap consistently see stronger results than those that rush straight to implementation.

AI technology is ready today, and the business case is clear. The next move depends on how leaders choose to act. Do it ASAP !!!