Reference

Technology leadership glossary

Plain-English definitions of the terms that come up when technology becomes a board-level question — fractional leadership, AI, modernization, and the decisions behind an acquisition. Written for executives and founders, not engineers.

Leadership roles

Fractional CTO

A senior technology executive who leads a company’s engineering, architecture, and technology strategy on a part-time, ongoing basis — embedded in the team with real decision authority, but at a fraction of a full-time CTO’s cost and commitment. Fractional CTO services →

Fractional CAIO

A fractional Chief AI Officer: a part-time senior leader who owns an organization’s AI strategy, governance, and delivery — deciding where AI creates real value, setting the guardrails, and shipping the first production use cases. Fractional CAIO services →

Interim CTO

A temporary but typically full-time technology executive who bridges a leadership gap — during a search, a departure, or a turnaround. Unlike a fractional CTO, the interim role is a short, intensive, full-time engagement rather than an ongoing part-time one.

Technology advisor

A consultant engaged for scoped, deliverable-based technology guidance — an assessment, a roadmap, a due-diligence report. It differs from a fractional CTO engagement, which is embedded, ongoing leadership with operational accountability. Both are forms of consulting.

Engagements & assessments

Technical due diligence

A structured evaluation of a company’s technology, architecture, team, and codebase — usually during an acquisition or investment — to surface risk, quantify technical debt, and validate whether the technology can support the deal thesis. Buy-side diligence assessment →

AI readiness assessment

A scored diagnostic of how prepared an organization is to adopt AI — across data, strategy, team, governance, architecture, and investment — plus a map of where automation would pay off first. Take the AI readiness assessment →

Technology roadmap

A sequenced plan that ties technology investments to business outcomes over a defined horizon — what gets built, modernized, or retired, in what order, and why. A good roadmap is a prioritization tool, not a wish list.

Technology exit readiness

Sell-side preparation that gets a company’s technology, documentation, and engineering organization ready to withstand buyer scrutiny — closing the gaps that would otherwise surface as valuation discounts during due diligence. Exit-readiness assessment →

Modernization

Legacy modernization

The disciplined process of evolving aging systems — dated stacks, brittle integrations, undocumented logic — into maintainable, scalable ones, without halting the business that depends on them. The hard part is sequencing, not technology selection. Enterprise modernization →

Technical debt

The accumulated cost of shortcuts, deferred maintenance, and dated decisions that make future change slower and riskier. Like financial debt, some is a deliberate, healthy trade-off — the danger is the kind no one is tracking.

Rebuild, wrap, or replace

The three core options for a legacy system: rebuild it from scratch, wrap it behind a modern interface or API while it keeps running, or replace it with a commercial product. Choosing correctly is one of the highest-leverage decisions in modernization. Read the modernization guide →

Strangler fig pattern

An incremental modernization strategy in which a new system gradually takes over functions from an old one — routing traffic piece by piece — until the legacy system can be safely retired. It avoids the risk of a single high-stakes cutover.

AI & automation

AI governance

The policies, controls, and review processes that keep an organization’s use of AI safe, compliant, and aligned with its risk tolerance — covering data handling, model approval, human oversight, and accountability for outcomes. AI governance for executives →

Retrieval-augmented generation (RAG)

A technique that grounds a language model’s answers in an organization’s own documents — retrieving relevant source material at query time and feeding it to the model — so responses reflect real, current, internal knowledge rather than the model’s training data alone.

Large language model (LLM)

An AI model trained on vast amounts of text to understand and generate language. LLMs power chat assistants, summarization, extraction, and code generation — and are the engine behind most current enterprise AI use cases.

AI opportunity matrix

A prioritization tool that plots candidate AI use cases by business value against implementation effort or feasibility — so leadership can see, at a glance, which opportunities to pursue first and which to defer. The AI opportunity matrix →

Shadow AI

The unsanctioned use of AI tools by employees — pasting company data into public chatbots, for instance — outside any policy or oversight. It is one of the most common and underestimated AI governance risks in mid-market companies.

Agentic AI

AI systems that take multi-step actions toward a goal — planning, calling tools, and reacting to results — rather than producing a single response. Agentic systems raise the value of automation and the importance of guardrails in equal measure.

AI automation

Applying AI to handle work that previously required a person — triaging support tickets, extracting data from documents, generating first-draft content — scoped and integrated so it holds up under real production workloads. AI automation services →

Vibe coding

Building software by prompting an AI assistant in natural language and iterating on what it produces. It accelerates prototyping dramatically, but shipping the result to production still demands architecture, review, and governance — the parts the vibe skips.

Architecture & delivery

Monolith vs. microservices

Two ways to structure an application: a monolith is one deployable unit; microservices split it into independently deployable services. Neither is universally better — the right choice depends on team size, scaling needs, and operational maturity.

Event-driven architecture

A design in which components communicate by emitting and reacting to events rather than calling each other directly. It decouples systems and scales well, at the cost of added complexity in tracing and debugging.

Platform scalability

A platform’s ability to handle growth — more users, data, or transactions — without a rewrite or a collapse in performance. Scalability is designed in early; it is expensive to retrofit under load.

DevOps

A set of practices that unify software development and operations — automated builds, testing, deployment, and monitoring — to ship changes faster and more reliably. Mature DevOps is often the difference between a team that can move and one that can’t.

Business & M&A

Buy-side vs. sell-side diligence

Buy-side diligence is performed by an acquirer or investor evaluating a target; sell-side (or vendor) diligence is prepared by the company being sold to get ahead of buyer questions. Both examine the same technology — from opposite sides of the table. M&A advisory →

Build vs. buy

The decision to develop a capability in-house or acquire it as a commercial product or service. The right answer weighs strategic differentiation, total cost, speed, and the ongoing burden of ownership — not just the upfront price.

Total cost of ownership (TCO)

The full lifetime cost of a system — licensing, infrastructure, integration, maintenance, and the people to run it — not just its purchase or build price. TCO is what separates a cheap decision from an economical one.