Source Material
Frames the hype path, funded strategic projects, and the stuck middle.
Frames agentic AI as a budget, ownership, architecture, and governance problem.
Thesis
Newman
Adoption is not transformation
Training completion, Copilot access, and usage telemetry do not prove that teams can redesign work or operate governed agent workflows.
Newman
Funded exceptions do not scale
Strategic projects succeed because they have sponsorship, platform investment, semantic work, and cleared authority. Those conditions must become repeatable.
diginomica
The budget owner is unresolved
Agentic AI crosses data platforms, transactional systems, IT, security, and business units. Cost, risk, governance, and value do not land in the same place.
diginomica
Vendor architecture is not an operating model
Roadmaps for agents in CRM, workflow, ERP, or data-cloud platforms do not settle who controls systems of record, approvals, audit trails, and cross-system execution.
Agentic AI requires budget ownership, semantic layer investment, vendor architecture scrutiny, systems of record boundaries, value accrual clarity, governance, and federated capability.
Guidance
- Segment the portfolio by enablement condition: separate broad adoption, funded strategic projects, and stuck-middle domains.
- Fund semantic foundations as shared product infrastructure: budget reusable semantic layers, process models, data contracts, and governance.
- Assign ownership for cross-system agent workflows: decide who pays, who owns risk, where approvals live, and how value is attributed.
- Build federated capability near the work: equip teams to model, redesign, and build with governed platforms.
- Measure transformation by redesigned work: use work outcome metrics, not only adoption telemetry.
Notable Comments
The comment sections add field-level pressure to the thesis: adoption without an operating model creates fragmentation; governance must be workflow-specific; the stuck middle needs an intake route to funded capacity; and agentic AI still depends on data quality and system modernization.
LinkedIn comment
Kristine Krueger, PhD
AI programs stall because organizations treat enablement as training and adoption while the harder gap is workflow, decision, governance, and operating-model redesign.
Theme: Operating Model. Source comment
LinkedIn comment
Paul M.
The stuck middle needs a design-table role where work becomes AI-ready workflows, decision criteria, data context, semantic definitions, and governance requirements.
Theme: Semantic Layer. Source comment
LinkedIn comment
Bernardo Guinea
Broad Copilot rollout plus parallel experimentation with ChatGPT, Claude, internal platforms, agents, and governance created fragmentation rather than transformation.
Theme: Operating Model. Source comment
LinkedIn comment
Matt Cobby
Some teams are already working through the hard post-rollout yards by experimenting in context, even though there is no general solution for the stuck middle yet.
Theme: Federated Capability. Source comment
LinkedIn comment
Paul M.
Workflow governance should define which governed resource buckets a workflow may use, which perspectives are valid, and which sources are authoritative for a decision.
Theme: Governance. Source comment
LinkedIn comment
Javier Angel Martinez Rodriguez
The missing middle mechanism is an intake path that moves a team's low-value work opportunity to the funded center and returns as usable capability.
Theme: Intake Path. Source comment
diginomica comment
Jon Reed
Agentic AI buyers who expect results without breaking down silos, upgrading older systems, and improving data platforms and quality are headed for disappointment.
Theme: Data Quality. Source comment
diginomica comment
cliveb
A concise budget-side challenge: ERP buyers may be using layoff expectations as a way to justify or fund agentic AI investment.
Theme: Budget Ownership. Source comment
diginomica comment
Jon Reed
The comment sharpens the article's ownership debate by challenging whether CRM vendors should be treated as systems of record for agentic enterprise workflows.
Theme: Budget Ownership. Source comment
Knowledge Graph Explorer
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FAQ
Why is tool adoption not enough for AI transformation?
Tool adoption proves access and activity. Transformation requires semantic context, redesigned workflows, governance, ownership, and local capability.
What is the budget problem in agentic AI?
Agentic workflows cross data platforms, applications, business teams, IT, security, and governance. Cost, risk, and value are distributed, so ownership must be explicit.
How should enterprises unblock the stuck middle?
Fund semantic foundations, create enablement pods, provide governed platforms, and measure outcomes around redesigned work.