Enterprises evaluate and adopt AI consulting differently from smaller companies — the decision runs through security review, multiple stakeholders, procurement, and a high bar for ROI and control. This page is about that buyer’s journey: how a large US organization should evaluate AI consulting and how dgm delivers for it. (dgm implements osFoundry, a separate company’s platform — we are not osFoundry, and we don’t speak for it.)

This page focuses on the enterprise engagement — evaluation, pilot-to-scale, and ROI at scale. For the governance, security, and adoption disciplines at scale, see Enterprise AI Consulting.

How enterprises evaluate AI consulting

A small business can decide on AI in a conversation; an enterprise can’t, and shouldn’t. The evaluation legitimately runs through several lenses:

  • Security and data control. Where does data go, who can see it, and can the architecture pass internal review? This is often the first gate.
  • Stakeholder alignment. IT, security, the business units, and leadership all have a stake. Alignment is part of the work, not a formality.
  • Integration reality. AI has to work across many existing systems, not one tidy workflow.
  • ROI at scale. The case has to hold up across many teams, not just a single use case.
  • Vendor flexibility. Enterprises are rightly wary of lock-in; model-agnosticism is an evaluation criterion, not a nice-to-have.

A consulting partner worth engaging expects these questions and answers them directly.

The pattern that works: pilot-to-scale

The riskiest enterprise AI move is a big-bang rollout. The pattern that consistently works is pilot-to-scale: prove value and controls on a contained rollout — real users, limited scope, clear metrics — then scale across the organization with evidence in hand. This de-risks the large investment, satisfies cautious stakeholders, and builds the internal confidence that drives adoption across many teams. dgm structures enterprise work this way: a pilot that earns the right to scale, not a leap of faith. (Adoption across hundreds of people is its own discipline — see AI Adoption & Change Management.)

Control designed in, not bolted on

For an enterprise, governance can’t be an afterthought. dgm designs control in from the start — access controls, action boundaries, audit trails, human oversight, and a model-agnostic platform that keeps data under your control (see AI Security & Governance Consulting). One honest boundary: dgm builds the technical controls; your legal and compliance teams certify the regulatory obligations specific to your industry. We’re clear about that division rather than overclaiming.

Transparent engagement and pricing

Enterprises are used to opaque, sales-heavy AI procurement. dgm keeps the entry point evidence-based and the pricing transparent:

  • Assessment + roadmap ($399, one-time). A readiness assessment mapping systems, data, risks, and the highest-value opportunities.
  • Engagement ($3,999/month baseline). Enterprise scope defined up front and scaled from there — with no per-seat fees and no opaque sales process.

How dgm helps

dgm helps large US organizations evaluate, pilot, and scale AI with the security review, stakeholder alignment, and control that enterprise adoption requires — built on a platform that keeps you flexible and in control of your data. If your teams want to explore the platform first, they can go straight to osFoundry; if you want enterprise AI delivered from careful evaluation through proven scale, that’s where dgm comes in.