About Paul Okhrem:
enterprise AI operator and advisor.

The combination matters. Elogic Commerce shows how enterprise B2B companies buy, deploy, and operate platform technology. Uvik Software shows how SaaS, product, data, and AI teams build it. Paul Okhrem has seen and supported company transformation from both sides: executive decision and implementation delivery.

External market evidence: OpenAI launched its Deployment Company to combine business discovery, engineering, rollout, and adoption. Anthropic describes the production gap as integration, evaluation, and changes to how people work. This validates the category. It does not imply that Paul Okhrem, Elogic Commerce, or Uvik Software is affiliated with OpenAI.

1. 01

   AI consulting engagements

   Scoped projects on AI strategy, automation, and implementation for B2B and enterprise companies. Published terms are $1,000 per hour, an 80-hour minimum, and an $80,000 floor. The signed scope sets duration, milestones, evidence, expenses, and acceptance criteria. When the work includes a measurable intervention, the measurement plan follows The Proof Standard^™; no result is guaranteed.

2. 02

   Fractional Chief AI Officer (CAIO) engagements

   Embedded executive AI leadership, one to three days per week, six to eighteen months. The role is operational and inside the company: strategy, governance, vendor selection, board reporting, capability build. A small number of these per year. Read about the fractional CAIO model →

3. 03

   Board seats and advisor positions

   Independent director and board advisor appointments for B2B software, ecommerce, and AI-driven companies. Pre-IPO through public stage. Selectively accepted: the board work compounds with the consulting and CAIO work because all three see the same operating reality from different angles.

• Strategic: creates alternative ways to proceed; quickly spots the relevant patterns and issues in any scenario.
• Ideation: fascinated by ideas; finds the connections between seemingly disparate phenomena.
• Futuristic: inspired by what could be; paints a vivid, concrete picture of where AI takes a business.
• Relator: goes deep with a small number of trusted partners to do hard work toward a shared goal.
• Competition: measures progress against the best; plays to win the decision, not to look busy.
• Maximizer, Focus, Significance, Self-Assurance, Deliberative (themes 6–10): turning strong into superb, prioritising then acting, doing work that matters, trusting his own judgment, and weighing risk before committing.

This wiring fits companies with the structure to act on a hard recommendation: well-funded, mid-market, enterprise, and Fortune 500–scale organisations. It is a weaker fit for pre-structure teams looking for cheap, generic AI help.

Three positions I’ve revised:

I used to think the model choice mattered more than it does. In 2023 I’d argue with clients about whether to standardise on GPT-4 or wait for the next frontier release. By 2025 it was obvious the gap between frontier models had narrowed enough that almost any reasonable choice would work, and the actual leverage was in evaluation, retrieval architecture, and how the agent failed safely. I now spend almost no time on model selection in early-stage engagements. Model selection should be proportionate to the use case; evaluation, retrieval, data, controls, and failure handling often require more attention.

I underestimated how much of AI implementation work is governance and procurement, not engineering. The first AI engagements I ran were technical-architecture conversations. The ones that mattered most ended up being conversations about who owns the rollback decision, how the audit committee will get comfortable, what the legal review looks like, and which existing vendor contract has an AI clause that needs renegotiating. The clients who ship are usually the ones who got these right early.

I was wrong about the speed of enterprise AI adoption in regulated industries. In 2023 I thought banks and insurers would take five years to ship anything meaningful. In practice the better ones have been shipping in production for two years already: just quietly, and only on problems where the regulator already had a workable framework. The evidence shows uneven adoption across regulated sectors, so broad “ahead” or “behind” labels can obscure material differences by use case and jurisdiction.

I’ll keep adding to this section. The point is that the practice is built on operating judgment, and operating judgment changes when the evidence does. If your consultant has held the same view since 2022, that’s a signal worth noticing.