How to Get Started with AI in Your ERP

Artificial intelligence is quickly becoming part of nearly every business technology conversation. For manufacturers and other ERP-driven organizations, the possibilities are easy to see. AI can help improve forecasting, identify inventory risks, automate repetitive tasks, surface important information faster, and support better decision-making across the business.

The harder question is not whether AI has potential. It is figuring out where to start.

Many organizations begin by looking at the technology itself. They ask which AI tools they should be using, what new platforms they need, or how they can add AI into their ERP system. But that approach can quickly lead to expensive experiments that never solve a meaningful business problem.

A better starting point is much simpler: Where is the business losing time, money, or visibility today?

That question shifts the focus away from AI for the sake of AI and toward practical business value.

Start With the Problem, Not the Technology

Before evaluating AI tools, organizations should first identify the problems they are actually trying to solve.

A manufacturer might be dealing with recurring inventory shortages, inaccurate forecasts, production delays, excessive manual data entry, or difficulty getting the right information out of the ERP system. Each of those problems could potentially become an AI use case, but the business problem needs to come first.

Instead of saying, “We want to use AI,” the goal should be more specific.

Maybe purchasing teams are spending hours each week manually reviewing materials and trying to identify potential shortages. Maybe planners are struggling to forecast demand accurately. Maybe employees are spending too much time searching through ERP screens and reports to answer basic questions.

Once the problem is clear, it becomes much easier to determine whether AI can help and what a successful outcome should look like.

This is similar to the way SolutionsX approaches broader ERP transformation. The process begins by understanding business objectives, value drivers, desired outcomes, and business processes before deciding what technology should support them.

AI should be approached the same way.

Your ERP Data Is Part of the Foundation

After identifying a strong use case, the next step is understanding the data behind it.

ERP systems often contain years of valuable operational information. Customer orders, inventory transactions, purchasing history, production records, supplier performance, financial data, quality information, and demand history can all potentially support AI-driven insights.

The challenge is that having a lot of data is not the same as having useful data.

If information is incomplete, inconsistent, outdated, or spread across multiple disconnected systems, an AI initiative may struggle before it even begins. A model can only work with the information available to it.

That makes data readiness an important part of the conversation.

Organizations should look at whether important ERP fields are consistently populated, whether processes are standardized across the business, and whether critical information is sitting outside the system in spreadsheets or other applications.

In many cases, companies do not necessarily need more data. They need to make better use of the data they already have.

SolutionsX already considers data quality, readiness, integrations, and business processes as part of broader ERP and cloud readiness initiatives. Those same areas become increasingly important when AI enters the picture.

Start Small and Prove the Value

One of the easiest ways to make an AI initiative unnecessarily complicated is trying to do too much at once.

Organizations do not need to transform the entire ERP environment with AI in their first project. A better approach is to identify one focused use case where the problem is clear, the necessary data is available, and the result can be measured.

For example, a manufacturer could begin by using AI to identify potential inventory shortages earlier. Another organization might focus on improving demand forecasts. Others may see more immediate value in automating repetitive administrative tasks, analyzing supplier performance, detecting unusual transactions, or allowing employees to ask questions about ERP information using natural language.

There is no universal first AI project.

The right use case depends on the organization and where the most valuable opportunities exist.

What matters is that the first initiative has a clear definition of success.

If purchasing employees currently spend 20 hours per week manually reviewing potential shortages, can AI reduce that workload? If forecast accuracy is currently 70 percent, can a new approach improve it? If employees are spending hours gathering information for reports, can that time be reduced?

Establishing a baseline gives the company something to compare against later.

Without measurable goals, it becomes difficult to determine whether an AI implementation actually improved the business or simply introduced another piece of technology.

Sometimes AI Readiness Starts With ERP Readiness

Organizations also need to consider whether their current ERP environment is prepared to support new capabilities.

Companies running older systems, highly customized environments, or disconnected applications may have additional challenges when they begin exploring AI.

Infor has noted that businesses using outdated software, or even cloud environments with extensive customizations and integrations, can struggle to adopt innovation at the speed the business requires.

That does not necessarily mean a company needs an entirely new ERP system before it can use AI. It does mean organizations should understand the limitations of the environment they already have.

Sometimes the most important first step is improving data quality. In other cases, it may be reducing unnecessary customization, improving integrations, standardizing a business process, or moving more capabilities to the cloud.

Organizations should also understand what is already available within their ERP ecosystem. As AI becomes increasingly embedded into enterprise software, businesses may discover that some capabilities can be introduced through technologies they already use rather than through a completely separate platform.

AI Cannot Be an IT-Only Initiative

Another important part of getting started is involving the right people.

AI initiatives often get treated as technology projects, but ERP touches nearly every area of the business. The employees who work inside those processes every day are often the people who best understand where the biggest problems exist.

Operations may see opportunities to improve production planning. Purchasing may understand where inventory and supplier problems occur. Finance can help determine whether a project is creating measurable value. IT can evaluate data, security, architecture, and integration requirements.

Bringing those perspectives together creates a much stronger starting point than asking a technology team to identify AI opportunities on its own.

SolutionsX’s ERP readiness methodology similarly emphasizes business-process ownership and cross-enterprise participation rather than treating transformation as an IT-only project.

Ultimately, the best AI initiatives are not just technology initiatives. They are business improvement initiatives supported by technology.

Build a Roadmap, Not a Collection of AI Tools

The AI market is moving quickly. New tools, models, copilots, and features are introduced constantly, and organizations can easily fall into the trap of trying to keep up with all of them.

A better strategy is to build a roadmap around business priorities.

A company might begin by improving data quality, then launch an inventory-focused AI pilot, measure the results, and later expand into demand forecasting or production optimization.

Another company may take a completely different path.

The important thing is that each project has a purpose and builds on what the organization learned from the previous one.

Companies do not need to know exactly what their AI environment will look like five years from now. They need to understand where they are today, where the strongest opportunity exists, and what the next practical step should be.

That makes AI much more manageable.

Where SolutionsX Fits

For many organizations, the biggest obstacle is not recognizing that AI could be valuable. It is determining which opportunities are realistic and where to begin.

That is where an ERP partner can help bring the conversation back to the business.

SolutionsX works with organizations to understand their business goals, evaluate their ERP environment, review processes and data readiness, and identify opportunities for improvement. When AI becomes part of that conversation, the focus is not simply on adding new technology.

It is about answering practical questions.

Is the ERP environment ready? Is the data reliable enough? Where is the biggest business opportunity? Which use cases should come first? Are there existing processes that should be improved before they are automated? And how will the organization know whether the investment was successful?

Companies do not need to have all of those answers before they begin exploring AI.

Finding those answers is part of getting started.

AI will continue to change how organizations use ERP systems and how employees interact with business information. But companies do not need to implement AI everywhere at once to benefit from it.

They simply need to start in the right place.

Instead of asking, “How do we add AI to our ERP?”

Ask, “Where is our business losing time, money, or visibility today, and how can our ERP data help us solve it?”

That question creates a much clearer path from AI hype to measurable business value.

If your organization is trying to determine where AI fits into its ERP strategy, SolutionsX can help evaluate your current environment, identify practical opportunities, and build a roadmap for what comes next.