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B2B software company · AIR

Many AI pilots, little value: how a software company prioritized use cases and built governance

Fourteen AI initiatives running in parallel, none in production with a value metric, and customers asking for data-use guarantees.

51 → 65Overall index
RegularFormat
131Respondents (82% response rate)
Context

B2B management software company with about 160 employees and a customer base in services and manufacturing. The technical team was strong, and use of generative AI tools had grown without coordination.

Challenge

Fourteen AI initiatives were spread across teams with no prioritization criteria and no usage policy. Two larger customers began requiring contractual information on how their data was handled by AI models.

Instrument applied

AI Readiness (AIR)

Why this instrument. AIR assesses AI readiness beyond technology: strategy, data, people, governance and value capture, which is where the gaps were.

Format: Regular · Respondents: 131 of 160 (82%)

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First application

Where the organization stood

Overall index
51 Band: Emerging 0100
  • Stagnant
  • Reactive
  • Emerging
  • Structured
  • Advanced
  • Benchmark
Results by pillar
Strategy and Use Cases 47 Data for AI 44 Technology and Platforms 66 People, Skills and Culture 58 Governance, Ethics and Risk 38 Operations, Adoption and Value 50
Plan executed

From results to action, in three waves

  1. 90 days

    Clear usage rules and priority use cases chosen.

  2. 6 months

    First use cases in production and teams trained.

  3. 12 months

    AI embedded in management, with measured gains and a new reading.

Reapplication

Before and after, pillar by pillar

Reapplied after 9 months: 51 (Emerging) → 65 (Structured).

BeforeAfter Strategy and Use Cases 47 63 (+16) Data for AI 44 58 (+14) Technology and Platforms 66 72 (+6) People, Skills and Culture 58 67 (+9) Governance, Ethics and Risk 38 61 (+23) Operations, Adoption and Value 50 66 (+16)
Business outcomes

What changed in operations

  • Overall index moved from 51 (Emerging) to 65 (Structured).
  • Average first response time on tickets fell about 22%.
  • Customer security questionnaires answered in days, not weeks.
  • Spending on duplicate AI tools reduced after license consolidation.
Lessons

What this case teaches

  • Mature technology does not make up for weak governance; governance is what got sales moving.
  • Cutting initiatives was harder and more useful than starting new ones.
  • Non-technical teams needed clear rules the most, not bans.

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Cases are presented without identifying clients. Sectors, sizes and figures are examples built to show the method.

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