How AI Is Transforming ERP Selection in 2026

The Old Way Was Broken Before AI Arrived

For decades, ERP selection followed a predictable and painful playbook. Hire a consultant. Spend three months gathering requirements in spreadsheets. Issue an RFP to six vendors. Sit through identical-looking demos. Argue in conference rooms about which system had the best user interface. Sign a contract. Regret it eighteen months later.

The process wasn't just slow — it was structurally biased toward vendors who were good at selling, not necessarily good at fitting your operations. Demos were choreographed theater. RFP responses were written by marketing teams, not engineers. And the consultant guiding your decision often had undisclosed relationships with the very vendors being evaluated.

AI is dismantling this model — and for manufacturers and distributors, the timing could not be better.

AI is dismantling this model — and for manufacturers and distributors, the timing could not be better.

What AI Actually Does in the Selection Process

The most impactful AI applications in ERP selection are happening in three areas: requirements intelligence, fit-gap analysis, and contract benchmarking.

Requirements intelligence means using AI to analyze your current systems, business processes, and operational data to surface requirements you didn't know you had. When we work with a small or mid-market manufacturer, we no longer rely solely on stakeholder interviews — which inevitably reflect what people think they want, not what the business actually needs. AI tools can analyze transaction logs, support tickets, workaround documentation, and process bottlenecks to generate a requirements profile grounded in operational reality.

Fit-gap analysis has historically required an army of consultants spending weeks mapping requirements to vendor capabilities. AI compresses this dramatically. By ingesting vendor documentation, community forums, implementation guides, and our own proprietary database of real-world deployment outcomes, we can score vendor fit against your specific requirements in days rather than weeks. More importantly, we can model fit not just against your current operations, but against where your business is heading in three to five years.

Contract benchmarking is where AI delivers some of its most immediate financial value. ERP contracts are notoriously opaque — vendors present pricing as though it were fixed, when in reality everything is negotiable. AI tools trained on hundreds of comparable contracts can identify where a vendor's proposal deviates from market norms, flag clauses that shift implementation risk to the buyer, and surface leverage points that most buyers never discover.

The Democratization of Enterprise-Grade Analysis

Here is the shift that matters most for small and mid-market manufacturers: AI is democratizing analytical capabilities that only the largest organizations could previously afford. A $75 million discrete manufacturer now has access to the same depth of requirements analysis and vendor benchmarking that a Fortune 500 company would commission with a seven-figure consulting engagement.

This levels the playing field during vendor negotiations. Vendors have always known more about their own pricing elasticity and contract risk than their customers. AI is closing that information gap.

What AI Cannot Replace

There are dimensions of ERP selection where human expertise remains irreplaceable — and where AI-only approaches fail.

AI cannot assess organizational culture fit. It cannot predict whether your operations team will embrace a new workflow or quietly revert to spreadsheets. It cannot evaluate a vendor's professional services team during a reference call, read the room when a vendor dodges a specific question, or structure the contractual language that protects you when an implementation goes sideways.

AI is extraordinarily good at processing structured information at scale. It is not good at judgment calls that require contextual understanding of your specific business, your competitive environment, and your organizational capacity for change.

The most effective ERP selection process combines AI-powered requirements and fit-gap analysis with experienced practitioners who know how to use those outputs — not consultants who use AI as a shortcut to reduce their own work.

Practical Recommendations

If you are beginning an ERP selection in 2026, here is how we recommend thinking about the role of AI in your process.

Use AI tools early, in requirements gathering. Do not begin with blank-sheet stakeholder interviews. Start by analyzing your existing operational data and letting the patterns surface requirements that human memory misses.

Require vendors to be scored against AI-generated criteria, not just the requirements your team hand-crafted. This surfaces blind spots and prevents vendors from gaming the RFP process.

Use AI contract benchmarking before you sign anything. The savings typically dwarf the cost of the analysis.

But invest in experienced practitioners to interpret the outputs. AI tells you what the data says. It does not tell you what to do about it.

The Bottom Line

ERP selection has historically been a process where the buyer is at a structural disadvantage — less information, less time, more to lose. AI is shifting that balance. But only for organizations that know how to deploy it as a tool rather than treat it as a substitute for operational expertise.

At Cherry Street, we have embedded AI into every phase of our selection methodology — not to replace our consultants, but to make their judgment sharper, faster, and more defensible. The result is selection processes that are more rigorous, more objective, and significantly more likely to produce implementations that actually deliver on their promise.