Eric Deltour

Executive summary

AI-Enabled Commercial Excellence

Where AI can create commercial value in industrial B2B, and where analytics, process or governance are the better answer.

The demonstrators behind this summary are built with synthetic data. They test the decision logic, operating model and value hypothesis; they are not production results. External figures are published research, cited with their source.

1 / 3

Four families of commercial problems. Only some of them need AI.

Growth

Key accounts erode slowly; innovation projects convert late

Share-of-wallet erosion, cross-sell, account priorities, sample-to-sales

  • Analytics
  • ML / optimization
  • Process & governance

Margin

Price increases and cost pass-through are only partly realized

Price realization, discount leakage, rebates, raw-material pass-through

  • Analytics
  • ML / optimization
  • GenAI / RAG

Sales execution

Reviews explain the gap; few end with an owned action

Performance reviews, forecasting, resource allocation, opportunity management

  • Process & governance
  • Analytics
  • ML / optimization

Productivity & knowledge

RFQs, specs and orders arrive as documents, re-keyed by hand

RFQs and tenders, quote-to-order, technical search, admin load

  • GenAI / RAG
  • Workflow
  • Analytics

Before any model: one customer hierarchy, invoice-level net prices, a named owner per action, incentives that reward margin.

84%of sales leaders say analytics influenced performance less than expected

Filled: main answer · Outlined: supporting · Source: Gartner survey of 303 sales leaders, published February 2024

2 / 3

Five opportunities stand out, but AI plays a different role in each

Prioritize business value first; use AI only where it materially improves the decision or workflow.

OpportunityBusiness valueRole of AIHowEvidence
Margin Bridge & Price RealizationProtect margin: price realization, pass-through, leakageAI-ENHANCEDPrice corridors · contract extractionESTABLISHED
Quote-to-Order & Technical RFQReduce response time and expert workloadAI-COREDocument understanding · RAG · draftingEMERGING
Key Account Share-of-Wallet ErosionRetain and grow strategic accountsAI-ENHANCEDErosion signals · prioritizationLIMITED
Commercial Performance CockpitMove faster from performance gap to owned actionANALYTICS-LEDExplanation · action prioritizationESTABLISHED*
Sample-to-Commercial ConversionConvert more innovation with scarce technical capacityAI-ENHANCEDProject scoring · brief structuringLIMITED

*Established for performance management; AI contribution less proven.

The strongest business case is not necessarily the one with the most AI.

Not prioritized as standalone demonstrators: Pipeline Coach · Distributor Performance Engine

3 / 3

Start with price realization: direct P&L impact, testable in a 90-day pilot

Build now · Margin

Margin Bridge & Price Realization Engine

  • Starts from the gap to budget: price, volume, mix
  • Runs on invoice data, the most reliable in the firm
  • AI suggests price corridors and reads contracts; people decide
  • Peer-reviewed B2B field experiment: +11% profit on treated quotes

Source: Marketing Science, 2024

Opens with the performance cockpit

Build next · Productivity / Growth

Quote-to-Order & Technical RFQ Copilot

The clearest GenAI case, measured in lead time and expert hours

Key Account Share-of-Wallet Erosion

Growth, and the change management that comes with KAMs

Sector-specific · Innovation

Sample-to-Commercial Conversion

For ingredients, F&F and specialty chemicals

Evidence still emerging

Same decision loop for every demonstrator

  1. Signal
  2. Diagnosis
  3. Recommendation
  4. Human decision
  5. Action
  6. Value tracking

Each demonstrator is judged on one test: can a commercial decision be traced from signal to measured value?

Download the three slides (PDF, 75 KB)

Let's talk about a role

I can walk through the demonstrators live. Tell me the context and what is at stake.