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Objection Handling

Six objections you'll hear.
Each one is a door,
not a wall.

Every objection is a signal that the prospect is thinking about it seriously. These are the six you'll hear most — with the reframe, the proof, and the question that moves the conversation forward.

01 Technicians won't use it 02 Our data isn't ready 03 Is it accurate? 04 We're not on IFS Cloud 05 Not the right time 06 Budget and ROI
01

"Our technicians won't adopt another tool."

Change fatigue vs usability
Why you hear this

They've been here before. They rolled out a mobile tool. It took months. Adoption is still patchy. The operations director doesn't want to fight that battle again.

The adoption problem isn't change fatigue — it's that previous tools asked technicians to do data entry on top of their real work. Resolve inverts this. The AI does the admin. The technician focuses on the repair. Natural language. Works offline. No training manual needed.

Reframe
They won't adopt another form. They will adopt something that makes their job easier.
Proof
William Grant & Sons: 99% completion rate in production testing. Operators adopted Resolve on weekends without being asked. No training programme. The AI guided them through the workflow conversationally.
Ask the prospect
"What's your current mobile tool adoption rate? What would it mean if the new tool had 99% completion — because it didn't feel like a tool?"
02

"Our data isn't in good shape — we're not ready for AI."

Data quality vs data creation
Why you hear this

Every AI pitch assumes clean, structured, well-governed data. The prospect knows their reality: incomplete asset records, inconsistent fault descriptions, years of paper-based history that never made it into a system. They feel they need a data transformation programme before they can benefit from AI.

They're not wrong about their data. They're wrong about the prerequisite. The question isn't whether the data is ready — it's whether waiting makes it any better.

Reframe
Resolve doesn't need perfect data. It creates better data from day one.
Proof
William Grant & Sons: Resolve ingested existing asset history and generated diagnostic recommendations from imperfect data. Every completed job improved the data set. 2,618 tool calls, zero failures — built on real-world data, not a clean room.
Ask the prospect
"How is your data getting better today? If every job your technicians complete doesn't improve the data, waiting won't help. Resolve makes every job a data capture event."
03

"How do we know it'll be accurate in our environment?"

Generic AI vs domain-specific accuracy
Why you hear this

They've seen generic AI tools hallucinate. They've seen ChatGPT make things up. A wrong diagnosis on critical equipment isn't a minor inconvenience — it's a safety risk, a cost, a compliance issue. The scepticism is earned.

The fear is that AI in their environment means general-purpose guesswork applied to safety-critical decisions. The distinction that matters is where the knowledge comes from — and whether the technician can verify it before acting.

Reframe
It's accurate because it reads your P&IDs, your OEM manuals, and your fault history — not the internet.
Proof
William Grant & Sons: 59/59 test scenarios, 99% completion, zero failures. Resolve correctly identified water addition settings as the root cause of a fault — not the mechanical failure the initial report suggested. The AI matched the judgment of a 25-year veteran.
Ask the prospect
"What would give you confidence? We can run a proof of value on your data, with your assets, in your environment. The accuracy question gets answered with evidence, not promises."
04

"We're not on IFS Cloud / we're on a competitor platform."

Platform dependency vs system-agnostic
Why you hear this

They assume Resolve requires IFS Cloud. Or they're on Salesforce, SAP, Dynamics, or something else entirely and think they're disqualified. This is one of the most common misconceptions — and one of the easiest to clear up.

IFS Cloud is not a dependency. Resolve works alongside whatever systems the customer already uses. It reads from and writes to existing systems via API — Salesforce, SAP, Dynamics, Maximo, Sage, or IFS Applications 10. Resolve is the AI layer at the point of work, not a platform replacement.

Reframe
Resolve works with your existing systems. IFS Cloud is not required.
Proof
Resolve is available to both new and existing IFS customers — and non-IFS customers. It integrates via API with Salesforce, SAP, Dynamics, Maximo, Sage, and others. Ecolab's proof of value unified five disconnected systems including Salesforce and SAP.
Ask the prospect
"What systems do your technicians interact with today? Resolve sits as a layer over all of them — the AI handles the integration complexity so the technician doesn't have to."
05

"We can't undertake this right now."

Timing vs cost of waiting
Why you hear this

They have other priorities. A digital transformation in flight. A restructure. Budget cycles. It's not that they don't see the value — the timing feels wrong.

The question isn't whether they have capacity for a major programme. Resolve isn't one. First value is delivered in weeks, not quarters — with ROI within months and in-year savings. A proof of value runs on a single site, a single team, with minimal disruption. The cost of waiting isn't zero — every month without Resolve is another month of return visits, lost knowledge, and reactive maintenance eating margin.

Reframe
This isn't a transformation programme. First value in weeks, ROI within months, in-year savings.
Proof
William Grant & Sons: Production deployment in approximately three months. Ecolab: Working prototype built in three days. Resolve is designed for fast deployment — not a multi-year implementation.
Ask the prospect
"What if this wasn't a six-month project? What if you could see results with one team on one site in a matter of weeks — with no disruption to everything else you're doing?"
06

"What's the cost? How do we justify the ROI?"

Cost justification vs value realisation
Why you hear this

They need to build a business case. The CFO wants numbers. They've been burned by AI vendors who promise ROI but can't quantify it in their specific context.

The ROI case for Resolve isn't theoretical. It's built from measurable operational levers the prospect already tracks: first-time fix rate, reactive-to-planned ratio, technician utilisation, parts waste, compliance cost, and revenue captured per visit. We quantify the case using their data, not industry averages.

Reframe
The ROI isn't theoretical. It's built from metrics you already track.
Proof
Early deployments have demonstrated 8–18× value-to-cost ratios. The value came from measurable operational improvements — not projections. We build the business case with the prospect, using their numbers, before they commit.
Ask the prospect
"What does a single return visit cost you, fully loaded? What's your first-time fix rate? We can model the ROI from your actual numbers — it takes one conversation."
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The guesswork goes.

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