# Agentic AI Enablement and Budget Ownership Meshup

Companion files: [HTML](../webpages/agentic-ai-enablement-budget-meshup-gpt5-1.html) | [RDF Turtle](../rdf/agentic-ai-enablement-budget-meshup-gpt5-1.ttl)

## Thesis

[Agentic AI](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23agentic-ai) programs will not scale through broad tool rollout or isolated flagship projects. Darlene Newman's post identifies the enterprise "stuck middle"; Derek du Preez's article adds the other half of the thesis: enterprise agentic AI has no clean budget owner when value, risk, data context, and transactional execution span data teams, IT, application owners, and business units.

The diginomica article is not just a funding footnote. It turns the enablement argument into an architecture and operating-model test: if agents need to pull context from a data platform, execute work in a transactional system, update another system, preserve approvals, and satisfy audit trails, then the enterprise must decide whether the control point sits with a data platform, CRM/ERP/workflow vendor, IT, or a cross-functional governance model. Vendor roadmaps do not answer that budget question by themselves.

The combined thesis is therefore: the stuck middle cannot be unstuck unless semantic foundations and federated capability are paired with explicit [budget ownership](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23budget-ownership), production-grade [vendor architecture](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23vendor-architecture) scrutiny, clear [systems of record](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23systems-of-record) boundaries, and a transparent model for [value accrual](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23value-accrual).

## Source Material

- [LinkedIn post: Three kinds of AI enablement programs](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23post) by [Darlene Newman](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fin%2Fdarlenenewman%2F%23this)
- [diginomica: Who pays for agentic AI?](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fdiginomica.com%2Fwho-pays-agentic-ai-enterprise-budget-problem-no-vendor-will-address%23article) by [Derek du Preez](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fdiginomica.com%2Fauthor%2Fddpreez%23this)

## Guidance

1. [Segment the portfolio by enablement condition](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23step1): do not collapse broad adoption, funded strategic projects, and stuck-middle domains into one KPI model.
2. [Fund semantic foundations as shared product infrastructure](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23step2): make semantic layers, process models, data contracts, and governance budgeted infrastructure.
3. [Assign ownership for cross-system agent workflows](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23step3): decide who pays, who owns risk, where approval lives, and how value is attributed.
4. [Build federated capability near the work](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23step4): equip teams to model, redesign, and build rather than only consume AI tools.
5. [Measure transformation by redesigned work](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23step5): track improved work outcomes, not just training completion or prompt activity.

## Notable Comments

The public comment layer strengthens the thesis in five ways: operating-model redesign, workflow-specific governance, a missing intake path for stuck-middle teams, data quality and legacy-system work, and budget ownership.

- [Kristine Krueger, PhD](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23comment-kristine-krueger) (LinkedIn): AI programs stall because organizations treat enablement as training and adoption while the harder gap is workflow, decision, governance, and operating-model redesign.
- [Paul M.](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23comment-paul-m-perspectives) (LinkedIn): The stuck middle needs a design-table role where work becomes AI-ready workflows, decision criteria, data context, semantic definitions, and governance requirements.
- [Bernardo Guinea](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23comment-bernardo-guinea) (LinkedIn): Broad Copilot rollout plus parallel experimentation with ChatGPT, Claude, internal platforms, agents, and governance created fragmentation rather than transformation.
- [Matt Cobby](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23comment-matt-cobby) (LinkedIn): 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.
- [Paul M.](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23comment-paul-m-workflow-governance) (LinkedIn): Workflow governance should define which governed resource buckets a workflow may use, which perspectives are valid, and which sources are authoritative for a decision.
- [Javier Angel Martinez Rodriguez](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23comment-javier-martinez) (LinkedIn): 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.
- [Jon Reed](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23comment-jon-reed-data-quality) (diginomica): Agentic AI buyers who expect results without breaking down silos, upgrading older systems, and improving data platforms and quality are headed for disappointment.
- [cliveb](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23comment-cliveb-budget) (diginomica): A concise budget-side challenge: ERP buyers may be using layoff expectations as a way to justify or fund agentic AI investment.
- [Jon Reed](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23comment-jon-reed-sor) (diginomica): The comment sharpens the article's ownership debate by challenging whether CRM vendors should be treated as systems of record for agentic enterprise workflows.

## FAQ

### [Why is tool adoption not enough for AI transformation?](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23q1)

Tool adoption proves access and activity. Transformation requires semantic context, redesigned workflows, governance, operating ownership, and local capability to change how work gets done.

### [What is the budget problem in agentic AI?](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23q2)

Agentic workflows cut across data platforms, systems of record, business teams, IT, security, and governance. Value may accrue in one place while cost and risk sit in another, so budget ownership must be explicit.

### [How should enterprises unblock the stuck middle?](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23q3)

Treat the stuck middle as a portfolio of work redesign opportunities. Fund semantic foundations, create enablement pods, provide governed platforms, and measure outcomes around work changed rather than tools used.

## Glossary

- [Semantic layer](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23semantic-layer): shared meaning layer that gives AI systems governed business context.
- [Stuck middle](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23stuck-middle): motivated teams without the investment and support needed to build.
- [Budget ownership](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23budget-ownership): funding and accountability model for cross-functional AI capability.
- [Federated capability](https://linkeddata.uriburner.com/describe/?url=https%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23federated-capability): AI capability close to work and connected to central governance.

## SPARQL Recipes

### Entity type summary

[Run live query](https://linkeddata.uriburner.com/sparql?query=PREFIX%20rdf%3A%20%3Chttp%3A%2F%2Fwww.w3.org%2F1999%2F02%2F22-rdf-syntax-ns%23%3E%0APREFIX%20rdfs%3A%20%3Chttp%3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdf-schema%23%3E%0ASELECT%20%3Ftype%20(SAMPLE(%3Fs)%20AS%20%3FsampleEntity)%20(SAMPLE(%3Flabel)%20AS%20%3FsampleLabel)%20(COUNT(%3Fs)%20AS%20%3FentityCount)%0AWHERE%20%7B%0A%20%20GRAPH%20%3Chttps%3A%2F%2Flinkeddata.uriburner.com%2FDAV%2Fdemos%2Fdaas%2Fagentic-ai-enablement-budget-meshup-gpt5-1.ttl%3E%20%7B%0A%20%20%20%20%3Fs%20rdf%3Atype%20%3Ftype%20.%0A%20%20%20%20OPTIONAL%20%7B%20%3Fs%20rdfs%3Alabel%20%3Flabel%20%7D%0A%20%20%7D%0A%7D%0AGROUP%20BY%20%3Ftype%0AORDER%20BY%20DESC(%3FentityCount)&format=text%2Fx-html%2Btr&timeout=0&debug=on&run=+Run+Query+)

```sparql
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
SELECT ?type (SAMPLE(?s) AS ?sampleEntity) (SAMPLE(?label) AS ?sampleLabel) (COUNT(?s) AS ?entityCount)
WHERE {
  GRAPH <https://linkeddata.uriburner.com/DAV/demos/daas/agentic-ai-enablement-budget-meshup-gpt5-1.ttl> {
    ?s rdf:type ?type .
    OPTIONAL { ?s rdfs:label ?label }
  }
}
GROUP BY ?type
ORDER BY DESC(?entityCount)
```

### Construct meshup spine

[Run live query](https://linkeddata.uriburner.com/sparql?query=PREFIX%20schema%3A%20%3Chttp%3A%2F%2Fschema.org%2F%3E%0ACONSTRUCT%20%7B%0A%20%20%3Chttps%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23meshup%3E%20schema%3Aabout%20%3Fconcept%20%3B%0A%20%20%20%20schema%3AhasPart%20%3Fpart%20.%0A%7D%0AWHERE%20%7B%0A%20%20GRAPH%20%3Chttps%3A%2F%2Flinkeddata.uriburner.com%2FDAV%2Fdemos%2Fdaas%2Fagentic-ai-enablement-budget-meshup-gpt5-1.ttl%3E%20%7B%0A%20%20%20%20OPTIONAL%20%7B%20%3Chttps%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23meshup%3E%20schema%3Aabout%20%3Fconcept%20%7D%0A%20%20%20%20OPTIONAL%20%7B%20%3Chttps%3A%2F%2Fwww.linkedin.com%2Fposts%2Fdarlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM%2F%23meshup%3E%20schema%3AhasPart%20%3Fpart%20%7D%0A%20%20%7D%0A%7D&format=text%2Fx-html-nice-turtle&timeout=0&debug=on&run=+Run+Query+)

```sparql
PREFIX schema: <http://schema.org/>
CONSTRUCT {
  <https://www.linkedin.com/posts/darlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM/#meshup> schema:about ?concept ;
    schema:hasPart ?part .
}
WHERE {
  GRAPH <https://linkeddata.uriburner.com/DAV/demos/daas/agentic-ai-enablement-budget-meshup-gpt5-1.ttl> {
    OPTIONAL { <https://www.linkedin.com/posts/darlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM/#meshup> schema:about ?concept }
    OPTIONAL { <https://www.linkedin.com/posts/darlenenewman_there-are-three-kinds-of-ai-enablement-programs-share-7465379435911847937-HBbM/#meshup> schema:hasPart ?part }
  }
}
```

## Provenance

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