About
Case Studies Compare
Global Get in touch
B2B SaaS, enterprise software, infrastructure, AI-native companies

AI consulting for
Technology & Software.

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 a 100-hour minimum and a $100,000 project floor.

Discuss a technology AI engagement See use cases

Technology & Software · Worldwide engagements · Prague-based · Global travel

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, generating approximately 30% operational efficiency, across Elogic Commerce and Uvik Software. The work is operator-led and vendor-neutral, priced at $1,000/hour with a 100-hour minimum and a $100,000 floor, and validated under The Proof Standard™.

Who you’re hiring

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

Not advice. Decision leverage. Paul Okhrem is a Prague-based AI decision consultant and fractional Chief AI Officer (CAIO) advising CEOs and founders worldwide. CEOs in B2B software vendors, platform companies, and technology firms shipping AI features into existing products hire Paul Okhrem to pressure-test the next major AI decision before it goes to the board — vendor, scope, governance, capital. By my count, fewer than one in five working AI consultants have actually shipped AI inside a company they own. That’s the difference that matters. Paul Okhrem has two decades of B2B software operating experience, two production-AI software companies under his ownership, generating approximately 30% operational efficiency gains across both companies. The work in technology and software focuses on AI strategy at the product level — what to build, what to buy, what to integrate, and how to defend the architecture decision against the next generation of vendor consolidation.

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). Co-founded Uvik Software in 2015 (London HQ, Python-first).
  • Recognised. Magento Community Engineering Award, Magento Imagine 2019.
  • Three engagement modes. Scoped AI consulting ($100K floor, $1,000 per hour, 100-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.

Use cases

Where the leverage actually shows up in software.

01

Internal engineering productivity

AI-assisted development that produces 40–55% more code per week per developer, without compromising review quality. Operating leverage in engineering is the largest cost line for most software companies.

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 that handle technical discovery calls, demo customization, and POC support at a fraction of the cost of human SE time. Particularly powerful for high-velocity, mid-market motion.

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.

Outcomes

What recent technology & software engagements have produced.

Technology and software engagements span engineering productivity, product architecture, customer success automation, and AI vendor selection. Recent outcomes include developer productivity gains of 40–55% measured by code commits per engineer-week, and customer success automation that scaled CS capacity without proportional headcount growth. Outcomes are measured under the Proof Standard, not claimed.

Specific case studies are typically governed by NDA. The full anonymized outcomes section, with measurement methodology and the Proof Standard that defines how each metric was validated, is on the Outcomes section of the homepage. The pattern across technology & software engagements: scope the metric that must move, define the measurement window before the engagement begins, validate against client analytics rather than consultant claims.

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 runs two engineering firms shipping AI in production, advising software CEOs on product strategy and vendor independence, vendor-neutral.

How much does AI consulting for technology companies cost?

Paul Okhrem prices at $1,000/hour with a 100-hour minimum and a $100,000 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 — so 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 runs 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 is the cleanest in 2026 — AI-assisted development produces 40–55% more code per developer per week without compromising review quality. 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 prices technology AI consulting engagements at $1,000 per hour with a 100-hour minimum and a $100,000 project floor. Typical scope: 8–16 weeks for project work on a defined AI workstream (engineering productivity rollout, product architecture review, AI-native competitive analysis), or 6–18 months for fractional Chief AI Officer engagements at $50M–$500M ARR companies building AI strategy at the executive layer.
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?
Yes, but with different engagement shapes. Early-stage (pre-Series B): consulting engagements focused on the AI architecture decisions that lock in for years. Growth-stage ($50M–$500M ARR): fractional Chief AI Officer engagements that hold the CAIO seat through the executive build-out. Public/late-stage: board advisor seats and consulting engagements focused on competitive positioning and capital allocation against AI-native threats.
Discuss an engagement

Get in touch 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.

Goes directly to paul@paul-okhrem.com. Reply within two business days. No marketing list, no third-party tracking.

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™

100-point scored read of where AI is creating commercial advantage — and where it is 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 where the next decision needs that perspective.

 Proof

AI Operating Leverage Case Studies

Three anonymized engagement walkthroughs. Baseline, intervention, validated result — published in full.

 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.