What size enterprise do you typically advise on AI?

Our enterprise AI engagements run from mid-cap (revenue $1B+) through Fortune 100. We are most useful where the operating environment is regulated, where the existing technology estate is non-trivial, and where AI decisions need to clear a board, a compliance committee, or an audit function.

Do you build models or only advise?

We are a strategy and advisory firm. We design AI programs, evaluate models and vendors, write governance policy, and gate pilots. The build is delivered by the client's data science team, by the chosen vendor, or by a delivery partner. We are happy to coordinate with all three.

What governance frameworks do you use?

We start from NIST AI Risk Management Framework and adapt to the client's regulatory environment (HIPAA, SOC 2, FedRAMP, GLBA, GDPR, state-specific privacy law). We produce policy that an internal audit function can actually use, not aspirational documents.

Can you help with model evaluation?

Yes. We design evaluation matrices that score candidate models on accuracy, hallucination rate against domain-specific eval sets, latency, cost per inference, governance posture, and total cost of ownership. We help clients run these as ongoing eval pipelines, not one-time bake-offs.

What is the most common failure mode you see?

Skipping governance and starting with a chatbot. Most enterprise AI failures are not technical, they are organizational: the program lacks executive sponsorship, the legal team has not signed off on data flows, and the workforce has not been prepared for the change. Our engagements address governance and change management first.

Practice Area / Enterprise AI

Enterprise AI Strategy

Q: How do you handle the build vs buy decision?

With a written framework, not a vibe. We score candidate use cases against criteria including total cost of ownership over three years, exposure to vendor lock-in, defensibility under audit, and the data sensitivity of the inputs. The output is a documented decision with rationale, not a slide deck recommendation.

The enterprises that will benefit most from generative and applied AI in the next five years are the ones that take governance seriously, take the build-versus-buy question seriously, and resist the urge to launch chatbots before the legal team and the audit function have weighed in. Johnson Intelligence Group's enterprise AI practice exists for organizations that want adult counsel through that program: Fortune 500 enterprises, regulated mid-caps, and the federal contractors who serve them.

Where we add value

Our engagements are concentrated in five high-leverage areas. Most clients pull on multiple, but every program is anchored on at least one.

Enterprise AI roadmap

Twelve to thirty-six month roadmap that sequences AI investments by expected value, risk profile, organizational readiness, and regulatory exposure. The roadmap names the use cases, the gating questions for each, the people accountable, and the signals that mean "stop, this isn't working." We do not produce roadmaps that fit on a single slide.

Build vs buy frameworks

For each candidate use case, a documented framework that scores build, buy, and hybrid options against total cost of ownership over three years, exposure to vendor lock-in, defensibility under audit, data sensitivity of the inputs, and time-to-value. The output is a written decision document, not a recommendation slide. Internal stakeholders can audit the rationale months later when the operating reality has changed.

Model evaluation

Evaluation matrices that score candidate foundation models, fine-tuned variants, and retrieval-augmented systems against domain-specific eval sets the client owns. We help clients build eval pipelines that run continuously, not one-time bake-offs that go stale the moment a new model release lands. We are explicit about hallucination rate, latency tail, cost per million tokens, and governance posture.

Governance policy

Written governance policy that aligns to NIST AI Risk Management Framework and to the client's regulatory environment (HIPAA, SOC 2, FedRAMP, GLBA, GDPR, CCPA, state-specific AI laws). Policy that is short enough that a human will actually read it, specific enough that a compliance officer can enforce it, and structured enough that an internal auditor can verify it.

Change management

The hardest part of enterprise AI in 2026 is not the technology, it is the workforce. We help clients design role transitions, training programs, communications, and union or works-council engagement (where applicable) that get the workforce on board with the program rather than against it. Most failed enterprise AI programs failed at this step.

Risks we help manage

Modern AI introduces a category of operational risk that does not map cleanly to existing enterprise risk frameworks. Our engagements explicitly inventory and manage the following:

Data leakage. Sensitive data flowing into a foundation-model provider's training set, into a vendor's logs, or into a third-party processor's environment without a defensible legal basis. We help clients audit data flows, structure DPAs and BAAs, and select architectures that minimize exposure.

Vendor lock-in. Architectures that bind a critical workflow to a single vendor's API, pricing, or roadmap with no realistic exit path. We help clients design abstraction layers, multi-vendor fallbacks, and contractual exit provisions that preserve optionality.

Hallucination liability. AI-generated content that is wrong in ways the enterprise is liable for. We help clients identify which use cases require human-in-the-loop, which require deterministic guardrails, and which require human-out-of-the-loop only with explicit governance review.

Compliance fit. SOC 2 and HIPAA both impose obligations on AI subprocessors. State AI laws (Colorado, California, Illinois) impose disclosure and consumer-rights obligations. EU AI Act obligations apply if the enterprise touches EU data subjects. We help clients map AI use cases to regulatory obligations and structure programs that satisfy them.

Adversarial risk. Prompt injection, data exfiltration via model behavior, model-context-protocol misuse. We help clients design threat models for AI-integrated systems and pen-test against them.

Deliverables

Engagements produce specific written artifacts that survive the engagement and remain useful long after we have left.

AI roadmap document. 12 to 36 month phasing, written rationale, gate criteria, and explicit kill signals.
Governance policy. One short document an executive will sign, plus appendices the compliance team can enforce.
Pilot program design. Defined scope, success criteria, and gating questions for each pilot.
Model evaluation matrices. Scored across accuracy, hallucination rate, latency, cost, governance posture.
Vendor evaluation report. When build vs buy lands on buy, a written comparison of viable vendors with pricing, security posture, contractual terms, and exit options.
Risk inventory. Mapped to the enterprise risk framework, including residual risk after each control.
Change management plan. Roles affected, training requirements, communications cadence, success metrics.

Industries we work in most often

Financial services (banks, insurers, asset managers), healthcare and health insurance, regulated manufacturing, federal contractors, large state and municipal governments, and higher education. The common thread is regulatory exposure: enterprises where the audit function and legal team have a real stake in how the AI program runs.

How a typical engagement runs

Most enterprise AI engagements run through three stages. We tailor the depth of each to the client's existing program maturity.

1. AI maturity assessment (4–6 weeks). Stakeholder interviews, technology estate review, regulatory mapping, candidate use-case inventory. Output: written assessment with current-state findings and prioritized roadmap.

2. Roadmap and governance (6–10 weeks). Detailed roadmap, written governance policy, vendor evaluation framework, build vs buy framework, change management approach. Output: roadmap document, governance policy, and an executive briefing pack.

3. Advisory retainer (3–12 months). Sustained advisory through pilot launches and program rollout. Weekly check-ins, vendor evaluation support, governance policy maintenance, on-call advisory for executive escalations.

What we don't do

We do not build models in-house. We do not staff augment a client's data science team. We do not sell hosting, infrastructure, or AI-specific software. Those are deliberate decisions that keep us aligned with the client's interest in vendor selection and program design. We are happy to coordinate with the client's chosen build partner or with a system integrator the client already trusts.

Want to engage? Request a capability statement and we will respond within one business day with a capability package and a no-obligation discovery call.