By Jon Hansen | Procurement Insights | January 2026
A recent MIT Technology Review report, sponsored by Fivetran, surveyed 300 C-suite executives on AI readiness. The findings were predictable:
- 82% say scaling AI use cases is a top priority
- 83% have identified data sources they must integrate to enable AI initiatives
- 45% cite data integration and pipelines as their leading challenge
- 44% cite data governance and security
The report concludes that “a compelling data strategy is the foundation for technological success.”
It’s not wrong. It’s incomplete. And incomplete readiness frameworks are why the 80% failure rate persists.
This Isn’t What Satya Nadella Meant
When Satya Nadella took the helm of Microsoft in 2014, he talked about the importance of organizations creating a “data culture” so that everyone could “make better decisions based on quality data.”
Decisions. Not pipelines. Not infrastructure. Decisions.
The MIT report co-opts the language of data culture but strips out the human element. It reduces “culture” to attitudes about data management — not about decision governance.
I wrote about this distinction in 2021: Getting Beyond the Twilight Zone of Data Uncertainty. The key finding then — which remains true today — is that 95% of executives identify organizational and process challenges as the primary obstacles impeding big data and AI initiatives.
Not data quality. Not pipelines. Not integration.
People and process. Leadership. Governance.
The MIT report ignores this entirely.
The Missing Variable
The report defines readiness in purely technical terms: pipelines, integration, governance, infrastructure. It asks whether the plumbing is ready.
It never asks whether the organization is ready.
Not once does the report address:
- Decision rights: Who decides what “good” looks like before deployment?
- Process alignment: Are existing workflows designed to absorb new capabilities — or resist them?
- Behavioral readiness: Will people use the system as intended, or route around it?
- Success criteria: Has the organization defined measurable outcomes before selecting tools?
- Exception governance: What happens when the system doesn’t fit the edge cases that define real work?
These aren’t soft considerations. They’re the variables that determine whether technical readiness translates into organizational value — or expensive shelfware.
The Pattern
I’ve watched this pattern repeat for 27 years.
Organizations invest in infrastructure. They integrate data. They deploy platforms. And then:
- Adoption stalls at a fraction of capability
- Workarounds become standardized
- “Success” gets redefined downward
- The next platform promise arrives, and the cycle restarts
The MIT report captures the inputs executives are focused on. It doesn’t capture why those inputs consistently fail to produce the outputs they expect.
The reason is simple: technical readiness is necessary but not sufficient.
You can have perfect data pipelines feeding a system that no one uses correctly — or that everyone uses to replicate the dysfunction that existed before deployment.
Two Definitions of Readiness
Technical Readiness (MIT/Fivetran framing):
- Data integration complete
- Pipelines operational
- Governance policies documented
- Infrastructure scaled
Organizational Readiness (Hansen Method framing):
- Decision rights defined before tool selection
- Success criteria measurable and agreed
- Process alignment validated against real workflows
- Behavioral adoption designed, not assumed
- Exception handling governed, not improvised
The first gets you deployed. The second gets you value.
Surrender or Govern
The MIT report teaches executives to prepare for AI by surrendering to its requirements — build the infrastructure, integrate the data, comply with the platform.
It never teaches them to govern the decision before the system governs them.
That’s the difference between technical readiness and organizational readiness.
That’s the difference between the 82% who say AI is a priority and the 80% who will fail.
That’s the difference between surrender and governance.
The Question That Matters
Before your next AI initiative, ask:
Have we defined what “good” looks like — in measurable, governable terms — before selecting the technology that will deliver it?
If the answer is no, you’re not ready. No matter what your data infrastructure says.
The Hansen Fit Score measures organizational readiness — not technical capacity. It exists because the 80% failure rate isn’t a mystery. It’s a pattern. And patterns can be interrupted, but only if you measure the right variables.
Which mindset runs your C-Suite: Practitioner A or Practitioner B?
Related: Buyers Need Not Apply (2014)
-30-
BONUS: Nadella Culture, SaaS Death, and the Incomplete MIT Report
Nadella’s “data culture” was always about decisions; “SaaS is dead” simply removes the old UI guardrails — so if decision rights and exception governance aren’t defined first, AI doesn’t replace SaaS… it automates dysfunction.
When systems act faster than organizations can govern decisions, failure doesn’t disappear — it arrives sooner, with greater confidence.
Related: Why Did Satya Nadella Say That SaaS Is Dead?
Procurement Insights
This Is a Dangerous Report: Surrender to the System Versus Govern the System
Posted on January 21, 2026
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By Jon Hansen | Procurement Insights | January 2026
A recent MIT Technology Review report, sponsored by Fivetran, surveyed 300 C-suite executives on AI readiness. The findings were predictable:
The report concludes that “a compelling data strategy is the foundation for technological success.”
It’s not wrong. It’s incomplete. And incomplete readiness frameworks are why the 80% failure rate persists.
This Isn’t What Satya Nadella Meant
When Satya Nadella took the helm of Microsoft in 2014, he talked about the importance of organizations creating a “data culture” so that everyone could “make better decisions based on quality data.”
Decisions. Not pipelines. Not infrastructure. Decisions.
The MIT report co-opts the language of data culture but strips out the human element. It reduces “culture” to attitudes about data management — not about decision governance.
I wrote about this distinction in 2021: Getting Beyond the Twilight Zone of Data Uncertainty. The key finding then — which remains true today — is that 95% of executives identify organizational and process challenges as the primary obstacles impeding big data and AI initiatives.
Not data quality. Not pipelines. Not integration.
People and process. Leadership. Governance.
The MIT report ignores this entirely.
The Missing Variable
The report defines readiness in purely technical terms: pipelines, integration, governance, infrastructure. It asks whether the plumbing is ready.
It never asks whether the organization is ready.
Not once does the report address:
These aren’t soft considerations. They’re the variables that determine whether technical readiness translates into organizational value — or expensive shelfware.
The Pattern
I’ve watched this pattern repeat for 27 years.
Organizations invest in infrastructure. They integrate data. They deploy platforms. And then:
The MIT report captures the inputs executives are focused on. It doesn’t capture why those inputs consistently fail to produce the outputs they expect.
The reason is simple: technical readiness is necessary but not sufficient.
You can have perfect data pipelines feeding a system that no one uses correctly — or that everyone uses to replicate the dysfunction that existed before deployment.
Two Definitions of Readiness
Technical Readiness (MIT/Fivetran framing):
Organizational Readiness (Hansen Method framing):
The first gets you deployed. The second gets you value.
Surrender or Govern
The MIT report teaches executives to prepare for AI by surrendering to its requirements — build the infrastructure, integrate the data, comply with the platform.
It never teaches them to govern the decision before the system governs them.
That’s the difference between technical readiness and organizational readiness.
That’s the difference between the 82% who say AI is a priority and the 80% who will fail.
That’s the difference between surrender and governance.
The Question That Matters
Before your next AI initiative, ask:
Have we defined what “good” looks like — in measurable, governable terms — before selecting the technology that will deliver it?
If the answer is no, you’re not ready. No matter what your data infrastructure says.
The Hansen Fit Score measures organizational readiness — not technical capacity. It exists because the 80% failure rate isn’t a mystery. It’s a pattern. And patterns can be interrupted, but only if you measure the right variables.
Which mindset runs your C-Suite: Practitioner A or Practitioner B?
Related: Buyers Need Not Apply (2014)
-30-
BONUS: Nadella Culture, SaaS Death, and the Incomplete MIT Report
Nadella’s “data culture” was always about decisions; “SaaS is dead” simply removes the old UI guardrails — so if decision rights and exception governance aren’t defined first, AI doesn’t replace SaaS… it automates dysfunction.
When systems act faster than organizations can govern decisions, failure doesn’t disappear — it arrives sooner, with greater confidence.
Related: Why Did Satya Nadella Say That SaaS Is Dead?
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