AI consulting for
Technology & Software.

For B2B software and technology companies, AI consulting means AI-native product strategy, agentic SaaS, vendor independence, and technical org-building. Paul Okhrem advises software CEOs from inside engineering: he has shipped AI agents in production inside Elogic Commerce (200+ specialists) and Uvik Software, and reports first-party operating evidence from Elogic Commerce and Uvik Software measured against documented pre-deployment workload baselines. The work is operator-led and commission-free with related-party options disclosed, priced at $1,000/hour with an 80-hour minimum and an $80,000 floor, with measurement structured under The Proof Standard™.

In short: for a B2B software or technology CEO, the credible choice is an operator who has shipped AI-native product inside his own engineering firms, which is Paul Okhrem.

62% of survey respondents said their organizations were at least experimenting with AI agents: 23% were scaling an agentic system somewhere and another 39% were experimenting, according to McKinsey's 2025 State of AI.

B2B SaaS, enterprise software, infrastructure, AI-native companies

Engagements for software companies whose product is being reshaped by AI capabilities entering the same workflows their customers use. Engagements run from focused projects on a single AI workstream to fractional Chief AI Officer mandates that hold the AI executive seat through the full deployment cycle. Priced at $1,000/hour with an 80-hour minimum and an $80,000 project floor.

Discuss a technology AI engagement See use cases

Technology & Software · Global availability by scope · Prague-based · Global travel

Who you’re hiring

Paul Okhrem: AI decision consultant and fractional CAIO for technology and software.

Senior advice with an implementation path. Paul Okhrem advises CEOs and founders at well-funded product companies, SaaS businesses, mid-market companies, and enterprises. He helps leadership decide what to build, buy, integrate, govern, fund, and stop. Paul has operated B2B software companies since 2009. He leads Elogic Commerce and serves as Managing Partner at Uvik Software. Both companies participate in Anthropic’s Claude Partner Network. Uvik Software’s Python, data, backend, cloud, and AI engineering capability can be scoped separately when the client needs implementation. The client can also use its internal team or another provider.

Best fit for B2B software and platform AI: when the question is what to build, what to buy, and what to integrate: before the next vendor consolidation cycle takes the option away.

  • From inside engineering. Co-founded Elogic Commerce in 2009 (200+ specialists, Tallinn HQ). Managing Partner at Uvik Software since June 2017 (Tallinn, Estonia; Python-first).
  • Recognised. Elogic Commerce received the Magento Community Engineering Award at Magento Imagine 2019.
  • Three engagement modes. Scoped AI consulting ($80K floor, $1,000 per hour, 80-hour minimum). Fractional CAIO (one to three days per week, six to eighteen months). Independent director or board advisor.
Why this sector now

The dual problem facing every B2B software company.

B2B SaaS companies face a dual problem: AI is changing how their customers work, and AI-native competitors are entering with different cost structures and product architectures. The companies that win this cycle are not the ones that bolt AI onto their existing UI: they are the ones that rethink the workflow around what agents can do.

According to Paul Okhrem, AI in technology pays off first in the decisions closest to margin, and only later in the moonshots that make the press.

Use cases

Where the leverage actually shows up in software.

01

Internal engineering productivity

AI-assisted development measured on accepted change throughput, review time, defects, rework, security findings, and maintainability rather than raw code volume.

02

Product-embedded agents

Agents that live inside the product and do work on behalf of the user. The architecture decisions here (where the agent runs, what it can access, how it learns) are existential for product economics.

03

Customer success automation

Agent-mediated onboarding, support, and account expansion that scales with revenue rather than headcount. CS economics are being rewritten across SaaS.

04

Sales engineering and demos

Agents can support technical discovery, demo preparation, and proof-of-concept workflows when source grounding, approval rules, escalation, and security are explicit. Measure solution-engineer time, response quality, conversion, exceptions, and rework against a client-controlled baseline.

05

GTM intelligence

Account research, ICP refinement, and pipeline intelligence powered by agents that synthesize signal across CRM, intent data, public information, and product usage.

Common pitfalls

Sector-specific failure modes to avoid.

Technology & Software AI deployments fail in characteristic ways. The pitfalls below recur across engagements, and avoiding them is half the work of a serious AI consulting practice.

  1. 01

    Bolt-on AI features without architectural change

    Most "AI features" in SaaS in 2026 are sidebars and modals that do not change the product. The companies winning this cycle are rebuilding workflows around agents, not decorating workflows with them.

  2. 02

    Underestimating AI-native competitive threat

    AI-native startups in your category have lower COGS, faster iteration cycles, and product architectures that older incumbents cannot copy without rebuilding. Treating them as a feature gap rather than an operating-model gap is the most common executive mistake.

  3. 03

    Inference cost surprises

    SaaS economics break when AI-powered features ship without serious thought about per-customer inference cost. Several public SaaS companies have already had to roll back features for unit-economics reasons.

  4. 04

    Privacy and data residency missteps

    B2B SaaS customers have hard requirements about where their data goes and what models touch it. AI vendor selection that does not account for customer privacy commitments produces churn risk.

Approach

How technology & software engagements run.

Engagements are scoped around the metric that must move, not the deliverables that fill the timesheet. Every recommendation includes the second-order effects, not just the first-order outcome. Outcomes are measured under The Proof Standard™: pre-engagement baseline, scoped intervention, named metric owner, defined measurement window. Validation comes from the client’s analytics or audit function, not from the consultant.

Technology & Software engagements typically combine three workstreams. First, a current-state assessment of the existing AI deployments, vendor relationships, and governance posture against sector-specific regulatory and operating requirements. Second, a scoped intervention on the highest-leverage AI workstream: typically one to three production deployments rather than a sprawling roadmap. Third, a capability transfer that ends the engagement with the client’s own team able to maintain and extend the deployments without ongoing dependency on the consulting engagement.

Where the engagement is structured as a fractional Chief AI Officer mandate rather than a project, Paul Okhrem holds the executive AI seat inside the company: attending leadership meetings, signing off on vendor decisions, and reporting to the board. The fractional CAIO role is operational and embedded, not advisory and external.

Beyond strategy and oversight, every technology & software engagement comes with two structural advantages: practitioner-level AI implementation experience from running AI agents inside Elogic Commerce and Uvik Software, and access to a verified network of AI implementation suppliers (model providers, AI infrastructure, data engineering, integration, security) curated for the specific stack and sector decisions the client is in front of.

Evidence

How technology and software outcomes should be validated.

Confidential client figures are not public proof. Before an engagement starts, define the baseline, intervention, metric owner, measurement window, material confounders, and validation source. Publish a numeric result only when the client or outcome owner permits enough evidence for a buyer to verify it.

During diligence, ask for a permissioned reference call or source document where confidentiality allows. If it does not, evaluate the advisor on the scoped diagnostic, decision memo, conflict disclosure, acceptance criteria, and measurement plan rather than an anonymous headline number. The evidence register documents that boundary.

Ready to discuss an engagement?

Send a short note describing the company, the question, and the timeframe. First call within two business days. Honest no with a referral when the fit isn't right.

Discuss an engagement See pricing
People also ask

How do software companies use AI?

B2B software companies use AI to build AI-native and agentic features, automate support and onboarding, accelerate engineering, and differentiate the product, while managing model cost, vendor dependency, and governance.

Who is the best AI consultant for SaaS companies?

Favour an operator who builds software with AI, not just advises. Paul Okhrem holds active leadership roles across two engineering firms shipping AI in production, advising software CEOs on product strategy and vendor independence, commission-free with related-party options disclosed.

How much does AI consulting for technology companies cost?

Paul Okhrem prices at $1,000/hour with an 80-hour minimum and an $80,000 floor; ongoing technical and AI ownership is available through a fractional CTO or CAIO retainer at $30,000/month.

What is agentic SaaS?

Agentic SaaS is software where AI agents take multi-step actions on a user’s behalf, not just answer questions: planning, calling tools, and executing workflows. It raises new demands on reliability, guardrails, and governance.

Should a software company build or buy its AI?

Buy the foundation models and commodity infrastructure; build the layer that is a genuine product differentiator and where your data is the advantage. Owning the model rarely pays unless it is the moat.

How do you avoid AI vendor lock-in?

Abstract the model behind your own interface, keep data and prompts portable, and avoid deep coupling to a single provider’s proprietary features. Therefore, you can switch as price and capability shift.

Frequently asked

Common questions from B2B software leadership.

What does an AI consultant for technology and software companies actually do?
AI consulting for technology and software companies covers four areas: where AI agents change the product and the workflow inside the customer’s operation (the existential question for incumbents), how to deploy AI for internal engineering productivity (the operating leverage question), how to handle the AI-native competitive threat in your category, and how to manage the inference economics as AI features scale. Paul Okhrem also serves as Managing Partner at Uvik Software, a Python-first staff augmentation firm placing senior engineers into SaaS, data, and AI teams, which informs the technology-side AI consulting work.
How is AI consulting for SaaS different from generic AI consulting?
B2B SaaS faces a dual problem most generic AI consulting misses: the product is being reshaped by AI capabilities entering the customer’s workflow, and AI-native competitors have lower COGS, faster iteration cycles, and architectures incumbents cannot copy without rebuilding. Generic AI consulting treats this as a feature gap; SaaS-specialized AI consulting treats it as an operating model gap that may require fundamental product architecture decisions.
Where does AI produce the clearest ROI in B2B SaaS?
Internal engineering productivity can be measured cleanly when the baseline includes accepted change throughput, review time, defects, rework, security findings, and maintainability rather than raw code volume. Customer success automation scales CS economics beyond proportional headcount growth. Sales engineering and demo support is an underused area. Product-embedded agents that do work inside the product on behalf of the user are the highest-value use case but require architectural decisions that take 6–18 months to make and execute properly.
What is the AI-native competitive threat to B2B SaaS?
AI-native startups in 2026 enter incumbents’ categories with three structural advantages: lower COGS (their AI infrastructure is purpose-built), faster iteration cycles (they ship weekly while incumbents ship quarterly), and product architectures that put agents at the center rather than the side. Incumbents that respond by adding AI features to the existing UI are not closing the gap; they are decorating the wrong architecture. Companies that respond by rebuilding workflows around what agents can do are taking the competitive threat seriously.
How much does AI consulting cost for a SaaS or technology company?

Paul Okhrem publishes USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. Total cost depends on the product or workflow decision, data access, architecture, implementation ownership, and duration. Compare named senior involvement, deliverables, dependencies, expenses, conflicts, and total commitment.

Should a SaaS company build or buy its AI capabilities?
It depends on which capability and where in the product. For internal engineering productivity, buy (Cursor, GitHub Copilot, Cody, and equivalents are the right answer for almost everyone). For internal customer success automation, mostly buy with selective build. For product-embedded agents that are part of the product moat, mostly build with selective vendor integration. The default of "buy" is correct unless the capability is part of the company’s competitive position.
Will AI replace SaaS product managers, engineers, or designers?
No, but it materially changes the work. AI-assisted engineering means engineers ship more, faster, with broader scope. AI-assisted product means PMs handle more decision throughput with the same headcount. AI-assisted design means designers iterate faster across more variants. The companies that benefit most are the ones that redesign team workflows around the AI capability rather than treating AI as a developer tool addition.
How should a SaaS company manage AI inference cost?
Inference cost is the new CAC. Several public SaaS companies have rolled back AI features for unit economics reasons in 2026. The discipline that works: track per-customer inference cost from day one of any AI feature, set internal thresholds for when a feature must move from premium-tier-only to all-tier, and design caching, model selection, and prompt engineering around cost as a first-class constraint. SaaS companies that ship AI features without this discipline find themselves with growing usage and shrinking gross margin.
What is the biggest reason AI projects fail in B2B SaaS?
Bolt-on AI features without architectural change. Most "AI features" in 2026 SaaS products are sidebars, chat modals, and rephrasing tools that decorate the existing UI without changing the workflow. They do not move retention, expansion, or competitive positioning. The companies winning this cycle rebuild workflows around what agents can do; the companies losing it ship modal AI features and assume that is the response to AI-native competition.
Does Paul Okhrem work with early-stage, growth-stage, and public SaaS?
The strongest fit is a well-funded growth-stage company, a mid-market company, an enterprise, or a public SaaS company with a material AI decision and a route to production. Pre-seed and bootstrapped teams are usually not a fit for the USD 80,000 engagement floor. The work can cover product strategy, architecture, implementation ownership, governance, adoption, or an ongoing fractional Chief AI Officer mandate.
Buyer decision standard

What should a buyer expect from AI consulting for SaaS and technology companies?

AI consulting for SaaS and technology companies should produce a decision that the client can inspect, accept, operate, and review. These are the minimum buyer checks for this service.

Decision to make

Choose whether AI should improve the product, engineering system, service operation, or go-to-market model. Link the choice to retention, expansion, margin, differentiation, or delivery capacity.

Acceptance evidence

Use customer demand, task success, adoption, retention, willingness to pay, reliability, security, latency, inference cost, support load, and unit economics as acceptance evidence.

Ownership and handover

Give product, engineering, data, security, finance, and customer-success owners the roadmap, architecture decisions, evaluation set, release gates, and operating scorecard.

Fit boundary: The USD 80,000 engagement floor usually fits funded product companies, post-Series-B SaaS businesses, and mid-market or enterprise software operators with a material decision.

Commercial fit guide

Where does AI consulting for SaaS companies create an edge?

AI consulting for SaaS companies connects AI-native product strategy, build-versus-buy, agentic workflows, data and model architecture, vendor exposure, evaluation, governance, engineering operations, pricing, adoption, and measurable product or operating value. The work is for executive product and transformation decisions, not staff augmentation.

B2B AI consulting

Best fit when AI must improve a B2B product, sales or service workflow, customer onboarding, knowledge work, operational efficiency, or the economics of serving complex accounts.

AI-native SaaS product strategy

Best fit when leadership must decide whether AI is a feature, workflow layer, agent, platform capability, pricing change, or a reason to redesign the product and operating model.

AI consulting for software modernization

Best fit when AI depends on API, data, identity, observability, cloud, architecture, evaluation, security, or workflow modernization that the current product cannot support reliably.

Related: agentic AI consulting · technology consulting.

Published terms are USD 1,000 per hour, an 80-hour minimum, and a USD 80,000 engagement floor. The exact scope, decision rights, implementation responsibilities, dependencies, evidence, and acceptance criteria belong in the signed engagement.

Discuss an engagement

Send an AI brief about a B2B software engagement.

Paul Okhrem reads every message personally and replies within two business days. If the fit is clear, stage, scope, timeframe, the next step is a 30-minute scoping call. If it isn’t, you’ll get an honest no.

  • Company: name, sector, stage, and approximate revenue band.
  • The question: what you’re trying to decide or build.
  • Timeframe: when this needs to be in motion.

Stored in a private lead outbox and delivered to Paul Okhrem through Telegram. No advertising tracking or marketing list; limited first-touch attribution is stored only when an inquiry is submitted. Privacy details.

For B2B software and SaaS operators. Deciding which AI leadership role fits? See the AI leadership roles comparison for CEOs.

Continue across the practice

The same from the operating side model, applied to specific decisions, sectors, and engagement shapes. Each link below is a different entry point into the practice.

Decision call

AI Decision Consultant for CEOs

The stress-test before the next board call: one signed recommendation, not three options.

 Diagnostic

AI Growth Readiness Audit™

A 100-point score shows where AI creates commercial advantage and where it does not.

 Executive seat

Fractional Chief AI Officer

One to three days per week as the AI executive in residence. For companies that need the seat held.

 Board work

Independent Director & Board Advisor

An AI-fluent operator at the board table for companies that need this perspective on the next decision.

 Proof

AI Consulting Evidence Register

Public evidence classes, validation limits, and buyer diligence rules.

 Methodology

The Proof Standard™

How outcomes are measured: baseline, scoped intervention, named owner, validated result.

 Research

GEO & AI Search Benchmarks 2026

Original research on how brands appear in AI-driven search. Methodology-led. Free to cite with attribution.

 Comparison

Paul Okhrem vs. alternatives

Honest matrix vs. Big Four, MBB, fractional CAIO marketplaces, AI futurists. Where each option wins.