AI Consultant · Dallas, TX

Hire an AI consultant in Dallas.

Six departments at a Dallas enterprise pilot six different AI tools with no shared evaluation criteria, no security review, and no data governance. AT&T, Texas Instruments, Southwest Airlines, and Tenet Healthcare have all seen this pattern. The result: AI sprawl that costs more than it saves and creates compliance exposure nobody authorized.

An independent consultant audits the current state, identifies which pilots to continue and which to shut down, and designs the governance structure that prevents the next round of AI investment from producing the same result. For Tenet Healthcare, that includes HIPAA compliance review for any clinical AI pilots. For AT&T, that includes a cybersecurity assessment for tools that process customer data.

Fixed-scope engagement. Five to eight weeks. Written deliverables yours to keep.

How we work

Tell us about your AI governance challenge.

The AI sprawl problem.

AI sprawl starts with good intentions. Individual business units identify real problems that AI tools can address. They evaluate tools independently, sign up for trials, and sometimes reach procurement without a cross-functional review. The result is a landscape where the enterprise is paying for a dozen overlapping tools with no shared understanding of what each one does with data.

The compliance exposure in this landscape is not hypothetical. An employee at a Tenet Healthcare facility using an AI clinical documentation tool that was not reviewed for HIPAA compliance before deployment creates a breach risk. AT&T business units evaluating AI tools that process customer telecommunications data without a CPNI review create regulatory exposure under federal communications law.

The cost problem is as significant as the compliance problem. A typical enterprise AI sprawl scenario involves three to five tools that address overlapping use cases, each with a separate vendor contract, a separate integration, and separate staff time to manage. An audit that identifies the overlap and recommends consolidation to one well-chosen tool often pays for itself in the first year of reduced licensing costs alone.

Texas Instruments and Southwest Airlines face a version of this problem at scale: AI tools adopted by engineering and operations teams that were never evaluated for data security or export compliance. For a semiconductor manufacturer, an AI tool that processes proprietary chip design data on a cloud API with unclear data retention policies is a trade secret risk. The governance structure prevents that risk from reappearing in the next round of AI adoption.

What an audit produces.

Current state inventory

A complete map of every AI tool in use across the enterprise, including shadow IT deployments that IT does not have visibility into. Most enterprises find tools in the inventory they did not know about.

Evaluation against defined criteria

Each pilot evaluated on measurable business outcome, data governance compliance, total cost, and strategic fit. The criteria are agreed on with leadership before evaluation begins.

Recommendations: continue, consolidate, or shut down

A written recommendation for each AI tool with the specific reasoning behind it. Pilots that meet the criteria get a continuation plan. Pilots that do not get a shutdown plan with timeline.

Compliance gap findings

Any tools with incomplete security review, missing HIPAA BAAs, unreviewed data residency, or other compliance gaps flagged explicitly. This section is the one that gets shared with legal and compliance.

Governance structure for ongoing AI decisions.

The audit finds and fixes the current state. The governance deliverable prevents the same problem from recurring in the next round of AI adoption.

The governance package includes an AI acceptable use policy, an AI tool intake and evaluation process with defined approval roles, a vendor security assessment checklist, a data classification guide for AI tools, and a minimum quarterly review process for tools in production. For Dallas healthcare companies, it also includes a HIPAA-specific addendum covering BAA requirements and data residency standards for AI vendors.

The governance structure is designed to be operated by your existing IT and legal teams without requiring dedicated AI governance staff. The process adds one to two weeks to any new AI tool adoption, which is the right trade-off for an enterprise that has already experienced the cost of skipping it.

How we work.

1
Discovery call
One hour. We map the scale of the AI sprawl, the business units involved, and the compliance constraints that apply. We scope the engagement and give you a fixed price.
2
Structured interviews and inventory
Sessions with IT, legal, and a sample of business unit leads. We build the current state inventory and review any existing vendor contracts and security assessments.
3
Evaluation and governance design
We apply the agreed criteria to every tool in the inventory, produce the audit findings, and draft the governance package. Draft delivered for your review before finalization.
4
Review and handoff
Final session with IT, legal, and relevant business stakeholders. Deliverables are yours in full. We are available for follow-up questions for 30 days after handoff.

Questions about AI consulting in Dallas.

How do you evaluate which existing AI pilots are worth continuing versus which to shut down?: The evaluation uses four criteria. First, measurable business outcome: does the pilot have a defined success metric and baseline, and is the current result better than the baseline? Pilots without defined success metrics are impossible to evaluate and should be shut down unless you can retroactively establish the measurement. Second, data governance compliance: was a security and privacy review completed before deployment? An AI pilot that processes customer PII without a completed privacy impact assessment is a liability regardless of its technical performance. Third, total cost of ownership including staff time: many AI pilots look cost-effective until you add the hours your team spends managing them. Fourth, strategic fit: does this use case align with where the business is investing in the next two years? A technically successful pilot in a business unit being divested is not worth continuing.
What does an AI acceptable use policy cover, and what is typically missing?: A baseline AI acceptable use policy covers: which AI tools employees are permitted to use, what categories of data cannot be input into those tools, what the approval process is for using new AI tools, and how violations are handled. What is typically missing: a subprocessor clause for AI tools that send data to third-party APIs, a data residency requirement for tools processing regulated data, and a process for evaluating AI tools that employees are already using without formal approval. Most Dallas enterprises have the first category covered and are missing the last three. The subprocessor gap is particularly acute for healthcare companies, where an employee using an AI writing tool that sends PHI to an uncontrolled API creates HIPAA breach exposure that the acceptable use policy does not address.
How do you handle the politics of telling a business unit that their AI project needs to be shut down?: The recommendation is delivered in writing with the specific criteria that drove it, and the criteria are agreed on by leadership before the audit begins. The most important step is getting enterprise leadership to establish the evaluation framework before the audit produces findings, not after. When the criteria are set in advance and applied consistently across all business units, the recommendation to shut down a specific pilot is a consequence of the agreed criteria rather than a judgment about the business unit's performance. The consultant's role in that conversation is to present the analysis, not to manage the interpersonal dynamics. That said, the written deliverable should be specific enough that a business unit leader can understand exactly why their pilot did not meet the criteria and what they would need to change to get a different result.
What cybersecurity assessment should happen before a large enterprise deploys an AI tool that processes internal data?: At minimum: data classification to confirm what categories of data the tool will process, a vendor security review covering the vendor's SOC 2 Type II report and penetration testing history, a data flow diagram showing where data goes including any third-party subprocessors, a data residency confirmation for regulated data categories, and an access control review to confirm the principle of least privilege is applied to the integration. For AT&T, which processes both customer telecommunications data and enterprise customer data, the relevant regulatory requirements include CPNI protection under 47 U.S.C. 222 in addition to standard enterprise data security requirements. An AI tool deployment that skips this assessment creates audit findings and potentially regulatory exposure. The assessment takes two to four weeks for a typical enterprise integration.

Want to know what an AI audit would find in your organization?

Hire an AI consultant in Dallas.

Fixed-scope engagement. Five to eight weeks. Written deliverables yours to keep.

The AI sprawl problem.

What an audit produces.

Current state inventory

Evaluation against defined criteria

Each pilot evaluated on measurable business outcome, data governance compliance, total cost, and strategic fit. The criteria are agreed on with leadership before evaluation begins.

Recommendations: continue, consolidate, or shut down

A written recommendation for each AI tool with the specific reasoning behind it. Pilots that meet the criteria get a continuation plan. Pilots that do not get a shutdown plan with timeline.

Compliance gap findings

Governance structure for ongoing AI decisions.

The audit finds and fixes the current state. The governance deliverable prevents the same problem from recurring in the next round of AI adoption.

How we work.

Discovery call

One hour. We map the scale of the AI sprawl, the business units involved, and the compliance constraints that apply. We scope the engagement and give you a fixed price.

Structured interviews and inventory

Sessions with IT, legal, and a sample of business unit leads. We build the current state inventory and review any existing vendor contracts and security assessments.

Evaluation and governance design

We apply the agreed criteria to every tool in the inventory, produce the audit findings, and draft the governance package. Draft delivered for your review before finalization.

Review and handoff

Final session with IT, legal, and relevant business stakeholders. Deliverables are yours in full. We are available for follow-up questions for 30 days after handoff.

Questions about AI consulting in Dallas.

How do you evaluate which existing AI pilots are worth continuing versus which to shut down?

The evaluation uses four criteria. First, measurable business outcome: does the pilot have a defined success metric and baseline, and is the current result better than the baseline? Pilots without defined success metrics are impossible to evaluate and should be shut down unless you can retroactively establish the measurement. Second, data governance compliance: was a security and privacy review completed before deployment? An AI pilot that processes customer PII without a completed privacy impact assessment is a liability regardless of its technical performance. Third, total cost of ownership including staff time: many AI pilots look cost-effective until you add the hours your team spends managing them. Fourth, strategic fit: does this use case align with where the business is investing in the next two years? A technically successful pilot in a business unit being divested is not worth continuing.

What does an AI acceptable use policy cover, and what is typically missing?

A baseline AI acceptable use policy covers: which AI tools employees are permitted to use, what categories of data cannot be input into those tools, what the approval process is for using new AI tools, and how violations are handled. What is typically missing: a subprocessor clause for AI tools that send data to third-party APIs, a data residency requirement for tools processing regulated data, and a process for evaluating AI tools that employees are already using without formal approval. Most Dallas enterprises have the first category covered and are missing the last three. The subprocessor gap is particularly acute for healthcare companies, where an employee using an AI writing tool that sends PHI to an uncontrolled API creates HIPAA breach exposure that the acceptable use policy does not address.

How do you handle the politics of telling a business unit that their AI project needs to be shut down?

The recommendation is delivered in writing with the specific criteria that drove it, and the criteria are agreed on by leadership before the audit begins. The most important step is getting enterprise leadership to establish the evaluation framework before the audit produces findings, not after. When the criteria are set in advance and applied consistently across all business units, the recommendation to shut down a specific pilot is a consequence of the agreed criteria rather than a judgment about the business unit's performance. The consultant's role in that conversation is to present the analysis, not to manage the interpersonal dynamics. That said, the written deliverable should be specific enough that a business unit leader can understand exactly why their pilot did not meet the criteria and what they would need to change to get a different result.

What cybersecurity assessment should happen before a large enterprise deploys an AI tool that processes internal data?

At minimum: data classification to confirm what categories of data the tool will process, a vendor security review covering the vendor's SOC 2 Type II report and penetration testing history, a data flow diagram showing where data goes including any third-party subprocessors, a data residency confirmation for regulated data categories, and an access control review to confirm the principle of least privilege is applied to the integration. For AT&T, which processes both customer telecommunications data and enterprise customer data, the relevant regulatory requirements include CPNI protection under 47 U.S.C. 222 in addition to standard enterprise data security requirements. An AI tool deployment that skips this assessment creates audit findings and potentially regulatory exposure. The assessment takes two to four weeks for a typical enterprise integration.

Want to know what an AI audit would find in your organization?