Why Most Companies Are Getting Salesforce AI Wrong (And How to Fix It)

Most manufacturers evaluating Salesforce AI (artificial intelligence) and Agentforce are shopping for features before they’ve assessed operational readiness. The result is stalled pilots, disappointed executives, and AI tools that sit unused next to the spreadsheets they were supposed to replace. We believe the fix is not more features. It’s a process-first readiness assessment that identifies specific, measurable workflows before any AI tool gets configured.

Why Do Most Salesforce AI Rollouts Underdeliver?

Most Salesforce AI rollouts underdeliver because they start with a feature demo instead of a business-critical workflow, leaving teams with capability but no adoption.

Vendors and even some implementation partners lead with what Agentforce or Salesforce AI can technically do: autonomous case routing, predictive forecasting, generative quoting. That is the wrong starting point for a mid-market manufacturer. We think the right starting point is a specific operational problem: late order visibility, inconsistent forecast accuracy, a service team drowning in repetitive case triage. When AI gets bolted onto a workflow that was never mapped or cleaned up first, the tool inherits every existing inefficiency instead of fixing it.

This isn’t unique to Salesforce or to manufacturing. MIT’s 2025 research on enterprise AI found that 95% of generative AI pilots showed no measurable return, and traced the gap to integration and approach rather than model quality.

This is not a manufacturing-specific problem, but it is a manufacturing-amplified one. Mid-market manufacturers run on legacy systems, disconnected ERP (enterprise resource planning) systems, and processes that took decades to calcify. Layering AI on top of that without addressing the foundation first does not accelerate the business. It accelerates the chaos. We’ve written separately about the groundwork ERP and CRM integration requires before AI enters the picture at all, since the two problems tend to compound.

What Does “Process-First” Actually Mean for AI Readiness?

Process-first means assessing operational readiness, not technical readiness, before selecting or configuring any Salesforce AI capability.

Technical readiness asks whether your Salesforce org can technically support an AI feature. Operational readiness asks a harder question: does your organization actually know which workflow it wants to improve, and can it measure whether the improvement happened? We find most companies skip straight to the first question and never seriously answer the second.

“I believe AI readiness starts with operational readiness.”Jordan Joltes, CEO and Founder, TruSummit Solutions

That distinction matters because operational readiness is what determines whether AI produces a measurable result within a defined pilot window, or a stalled pilot that quietly gets deprioritized at the next budget review.

What Does Operational Readiness Look Like in Practice?

We break operational readiness into two sequential questions, and we’ve written a two-part series that walks through each one in depth. The first is assessing where your organization actually stands today: what your data quality looks like, where your workflows break down, and which teams are actually bought in. The second is mapping where AI can create real value once that baseline is honest, rather than starting from a vendor’s feature list.

Skipping straight to the second question without answering the first is exactly how companies end up with technically impressive pilots that nobody trusts enough to use.

How Should Manufacturers Choose Their First AI Use Case?

Manufacturers should choose their first AI use case based on measurability, not novelty, focusing on quantifiable functions like quoting, forecasting, or service optimization.

The instinct is to chase the most impressive-sounding capability. We recommend choosing the use case where success or failure can be measured in hard numbers within a short, defined window. Quote turnaround time. Forecast variance. Average case resolution time. These are functions where before-and-after comparisons are unambiguous, which makes them far better first bets than a broad, ambitious AI initiative with no clear success metric attached.

Forecast accuracy is a common starting point, and one we’ve covered in detail in our guide to AI-driven demand forecasting for manufacturers, since it’s a function most manufacturers already track closely enough to measure improvement against.

Order status visibility is another good example of an overlooked, high-leverage starting point. Giving sales and service teams immediate visibility into order status reduces customer hold times, cuts churn risk, and removes a constant source of manual back-and-forth, all without requiring a company to overhaul its entire data environment first. We go deeper on what this looks like operationally in our piece on manufacturing pipeline visibility in Salesforce.

What Does This Look Like With Agentforce Specifically?

Agentforce has raised the stakes on this problem rather than solved it. An autonomous agent configured against an unclear or undocumented process doesn’t just underperform, it can actively make decisions inside a workflow nobody fully understands. We’ve shared our broader thinking on where Agentforce genuinely fits for manufacturers in our take on Agentforce for manufacturing, and the short version is the same as everything above: agentic AI is not a shortcut around process clarity, it depends on it more than any prior generation of automation did.

What Happens When Companies Skip the Readiness Assessment?

Skipping the readiness assessment typically produces one of three outcomes: a stalled pilot, a tool nobody uses, or a data integrity problem that erodes trust in the system.

Without a readiness assessment, teams frequently discover mid-implementation that their data isn’t clean enough to trust the AI’s output, that the workflow the tool was built for doesn’t match how people actually work, or that leadership never agreed on what success looks like in the first place. Any one of these is enough to sink an otherwise well-funded initiative. All three together are common.

The deeper issue is that adding a new AI tool on top of an unaddressed foundational problem does not solve that problem. It just adds a more expensive layer on top of it.

How Does TruSummit Approach AI Readiness Differently?

We anchor every AI engagement to a specific business outcome first, then scale AI capability iteratively from that foundation.

Rather than starting with a feature checklist, we start with a target outcome, such as improving on-time delivery or forecast accuracy, and work backward to identify the minimum viable connections needed to make that outcome achievable. A pilot might sync sales quotes with inventory availability, or surface customer order history directly inside the CRM (customer relationship management) system. Each step is deliberately small enough to show impact quickly and build organizational confidence before the next phase begins.

“This agile, iterative approach reduces risk, builds confidence, and allows for stakeholder feedback, all while laying the foundation to scale.”Jordan Joltes, CEO and Founder, TruSummit Solutions

This is the same logic behind our Trail Mapping, Climb, and Summit methodology: establish the terrain and the destination before committing to the climb, rather than configuring technology first and hoping the business case catches up.

What Should Manufacturers Do Before Their Next AI Conversation With a Vendor?

Manufacturers should complete an internal readiness assessment, covering data quality, workflow clarity, and success metrics, before entering any vendor conversation about AI capability.

Walking into a vendor conversation without this groundwork puts the burden of defining success on the vendor, who has every incentive to sell capability rather than diagnose readiness. We’ve found that a short internal exercise, even a working session that identifies the single most measurable, business-critical workflow worth improving, changes the entire conversation. It turns a features discussion into an outcomes discussion, which is a much better negotiating position and a much better predictor of whether the resulting investment will actually pay off.

Our own AI readiness checklist for manufacturing CRM and the broader AI Adoption Playbook both walk through this exercise in more detail for teams who want a structured starting point.

Frequently Asked Questions

Is Salesforce AI Ready for Manufacturing Companies Right Now?

Salesforce AI capabilities like Agentforce are ready for manufacturing use cases today, but the manufacturer’s operational readiness, not the technology’s maturity, is usually the limiting factor.

How Long Should a First AI Pilot Take?

A first AI pilot should be scoped narrowly enough to show a measurable result within a single business quarter; broader timelines vary by organization and should be treated as illustrative rather than fixed.

Do We Need Clean Data Before Starting an AI Initiative?

Perfect data is not required to start an AI initiative, but the specific data set tied to the chosen use case needs to be reliable enough to trust the AI’s output.

Should IT or Operations Lead the AI Readiness Assessment?

The AI readiness assessment works best as a joint effort, since operations defines the business outcome and IT (information technology) confirms what is technically achievable against the current Salesforce architecture.

Does ERP Integration Need to Happen Before AI Readiness Work Starts?

Not necessarily before, but the two efforts are closely linked, since AI tools built on top of disconnected ERP data inherit the same inconsistencies. Our guide on CRM and ERP integration for manufacturers covers how to sequence the two.

Ready to find out where your organization actually stands before your next AI conversation? Talk to TruSummit about an AI readiness assessment.