Summary

The deliverables run in a sequence: a use case portfolio scored for value and feasibility, a data and integration plan for the selected cases, working implementations moved from pilot to production, a governance model covering acceptable use, review, and monitoring, and capability transfer so your team can operate the result. The sequence matters more than any single item. Consultants who start writing prompts before scoring use cases are selling activity, and organizations that buy implementation without governance typically end up rebuilding under pressure after the first incident. Our generative AI and LLM practice treats production operation, not the demo, as the finish line.

What Are the Engagement Phases from Pilot to Production?

Four phases, each with a decision gate. Discovery and selection identifies where language models genuinely fit, which is a smaller set than the brainstorm produces. Piloting proves value on real data with a defined success metric and a kill threshold agreed up front. Production hardening is the longest phase: integration with live systems, security review, evaluation and monitoring, and fallbacks for the failure modes pilots never see. Operation transfers the system to your team with runbooks and monitoring. The most common expensive mistake is treating phase two as the finish line; a pilot that works is an argument for the hardening budget, not a substitute for it.

What Does Generative AI Consulting Cost?

Pricing is scope-driven, and few firms publish numbers. The published reference points that exist: structured assessments that scope the opportunity and test the foundations start at $30,000 to $50,000, including Bronson.AI’s published assessment tiers, which many organizations use as the entry engagement before committing to a build. Implementation work is typically phased, priced per use case against integration complexity, data condition, and compliance requirements. Two budgeting rules hold across the market: the data and integration work usually costs more than the model work, and a firm quoting a large build before examining your data is quoting blind.

How Do You Select the Right Use Cases?

Score candidates on value, feasibility, and risk, then be ruthless about the feasibility column. High-value cases fail on feasibility when the data feeding them is fragmented or when the process around them cannot absorb a probabilistic tool. The early production wins are usually assistive: drafting, summarization, retrieval over governed document sets, and code assistance, where a human reviews output before it acts on the world. Cases where output flows directly into decisions or customer commitments belong later in the roadmap, once evaluation and monitoring have matured. A disciplined portfolio also names the rejected cases and why; rejection discipline is one of the cleanest signs an AI program is being run seriously.

What Governance Does Generative AI Need?

Generative systems need governance built for probabilistic output: defined acceptable use, human review proportionate to consequence, evaluation before deployment and monitoring after, data controls covering what may enter prompts and where outputs may flow, and clear accountability for each system. In Canada, federal institutions already work under guidance including the Treasury Board’s FASTER principles for generative AI, and regulated industries should expect their sector rules to reach generative systems quickly. Governance is also where generative work connects to what comes next: autonomy raises every requirement, which is why controls belong in the design phase now rather than in a retrofit later.

How Do You Choose a Generative AI Consulting Firm?

Apply five checks. Production references, not demo references: ask specifically what is still running a year later. Data credentials, because generative AI work is data work; certifications like SOC 2 Type II and formal governance credentials such as DCAM authorization are verifiable signals. Deployment breadth, since the right answer may be an API, a cloud tenancy, or on-premises infrastructure, and a firm that only sells one model of LLM deployment will recommend the one it sells. Capability transfer, with named deliverables for your team’s independence. And a scoped entry engagement with published or clearly structured pricing, so the relationship starts with evidence instead of a leap.

Frequently Asked Questions

What is the difference between generative AI consulting and AI consulting generally?

Generative AI consulting is the subset focused on language and content models: LLM selection, prompting and retrieval architectures, evaluation, and the governance specific to probabilistic output. General AI consulting also covers predictive machine learning, computer vision, and automation.

How long does a generative AI implementation take?

Pilots typically run several weeks. Moving a successful pilot into hardened production commonly takes a few months, driven by integration, security review, and evaluation rather than by the model itself. Timelines stretch when data foundations need repair first.

Should we wait for the technology to stabilize before implementing?

Waiting has a cost: the survey data shows production adoption quintupling in a year, and operating experience compounds. The lower-risk approach is not waiting but sequencing: assistive use cases first, governance from day one, and autonomy only once evaluation and monitoring have earned it.

Do we need our own model, or can we use commercial LLMs?

Most organizations should start with commercial models, accessed through whichever deployment model their data sensitivity allows, and invest their differentiation budget in retrieval over their own governed data rather than in model training. Fine-tuning and custom models earn their considerable cost in narrow cases: highly specialized language, strict latency or cost ceilings at scale, or requirements that rule out external providers entirely. A good consultant will price both paths against your actual constraints instead of defaulting to the more billable one.