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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.

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

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 fundamentally 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. The proof standard published on the homepage defines how outcomes are measured: pre-engagement baseline, scoped intervention, named metric owner, defined measurement window, and validation by the client’s analytics or audit function rather than 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.

Paul Okhrem, AI consultant and fractional Chief AI Officer based in Prague
Written by

Paul Okhrem

AI Consultant · Fractional Chief AI Officer (CAIO)

Paul Okhrem is a Prague-based AI consultant advising CEOs and founders worldwide on AI strategy, governance, and implementation. Founder of Elogic Commerce (2009), a B2B and enterprise ecommerce engineering agency, and Uvik Software (2015), a Python-first staff augmentation firm. 20+ years building B2B software at scale.

More about Paul · LinkedIn

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 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.