AI Practice
Why AI matters.
The Real Challenge
AI is no longer experimental. Most organizations have already invested in AI tools, generative AI pilots, and custom AI agents.
The question now is whether those investments will deliver returns the C-suite will fund a second time. Industry data is sobering: an estimated 95% of enterprise AI pilots do not produce measurable ROI, only about 36% of AI licences become active users, and fewer than one in nine organizations have AI agents running in production at scale.
The 2Oaks AI Practice is built to close those gaps with practical, vendor-neutral work that survives the move from slide deck to operational reality.
How We Help
The 2Oaks AI Practice helps organizations turn artificial intelligence from a budget line into operational results. We deliver the full scope of AI consulting required to make that happen: AI strategy and readiness assessment, data foundations, generative AI pilot programs, AI-accelerated legacy modernization, and AI adoption and change management. Our consultants work with private and public sector organizations that need AI investments to deliver measurable value, satisfy regulators, and hold up under real-world use. Whether you are scoping an initial AI strategy, building a first production pilot, modernizing legacy systems with AI engineering tools, or trying to lift AI tool adoption beyond the licence count, our work is built to ship and last.
What this
practice does.
Our consultants deliver the full scope of work required to take AI from concept to operational value:
AI maturity and readiness assessment, use case prioritization, and AI strategy roadmaps
AI-assisted reverse engineering and recovery of legacy system knowledge
Data governance, quality, and AI-ready data infrastructure for generative AI workloads
AI-accelerated modernization, including .NET and Java upgrade, cloud migration, and containerization
Custom AI agent and pilot program implementation, engineered for production from day one
Evaluation, observability, and responsible-AI governance frameworks
AI adoption, change management, and AI Council formation
Cost forecasting, regulatory alignment, and AI operating model design
Our Point of View:
The Practice is built around six positions we hold consistently across every engagement:
Practical Problem Focus. We start with operational problems where AI demonstrably reduces cycle time, cost, or risk, and we say so when a use case is not worth the spend.
Pilot-to-Production Discipline. Pilots are scoped, instrumented, and architected so they ship to production rather than getting re-engineered six months later.
Vendor-Neutral Advisory. Strategy, assessment, operating model, and regulatory work remain platform-agnostic. We adapt to the technology environment of each engagement rather than anchoring you to a single stack.
Change Built In. Adoption is treated as a program, not an announcement, with champion networks, governance frameworks, and measurable adoption metrics running alongside the technical build.
Responsible AI by Design. Data residency, privacy, PIPEDA, OSFI guidance for federally regulated institutions, and equivalent U.S. obligations are factored into the assessment phase, not bolted on at the end.
Evidence Over Hype. We bring published industry data, real pilot outcomes, and a measured view of what AI delivers today, so your investment decisions rest on what is true rather than what is promised.
Services within the Practice
Partner with 2Oaks to turn AI from a budget line into operational results that protect enterprise value, satisfy regulators, and survive contact with real users.
For technology leaders who want the detailed methodologies, frameworks, and tooling behind this work, our AI Capabilities Brief is available on request.
5
Where should we start with AI?
1
Most organizations start with an AI readiness assessment because it surfaces the gaps that will block scaling later. We work pillar by pillar across an established AI readiness model covering business strategy, governance, data foundations, AI strategy and experience, organization and culture, infrastructure, and model management. The output is a prioritized portfolio of use cases scored on impact and feasibility, plus a phased implementation roadmap. Our blog post AI Readiness: A Five-Pillar Checklist outlines the framework we use as a starting point for these conversations.
6
Most enterprise AI pilots fail. Why would this one be different?
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Industry data shows about 95% of enterprise AI pilots do not produce measurable ROI, and fewer than one in nine organizations have AI agents running in production at scale. We design around those odds rather than pretending they do not apply. Pilots are scoped to a clear business problem with quantitative success criteria defined before the first line of code. Architecture decisions are made for production from day one, not retrofitted later. And we are willing to recommend stopping a pilot that cannot meet its criteria, rather than letting it drift. Libro Credit Union's CITO Chris Palmer described a similar discipline in our CIO Spotlight series, Building AI That Works.
Are you tied to Microsoft Azure, or can you work with other AI platforms?
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We work with the major AI ecosystems and adapt to the technology environment of each engagement. Our practice has particular depth in Microsoft Azure tooling because that is where many of our clients have standardized, but we deliver across other platforms when that is the right answer. Strategy, operating model, regulatory, and assessment work stays platform-agnostic on principle. We will tell you when the right answer is not a Microsoft answer, or not an AI answer at all.
Can AI help with the legacy modernization work we keep deferring?
What does an AI engagement look like for a regulated financial institution?
4
Our employees have AI licences but barely use them. Can you help with adoption?
Your Questions Answered:
Most of our AI work is with regulated organizations, including credit unions, banks, and insurers operating under OSFI, OCC, FFIEC, NCUA, FDIC, Federal Reserve, CFPB or FCAC supervision. A typical engagement begins with a readiness and use case assessment, followed by data governance work, then pilot implementation with evaluation, observability, and responsible-AI controls built in. Through our CIO Spotlight series, you can see how Canadian financial institutions are approaching this in practice, including Northern Credit Union's governance-first philosophy and UNI Financial's deliberate foundation-building approach.
Yes, and this is one of the strongest commercial use cases for AI right now. AI engineering tools have changed the economics of legacy code analysis, framework upgrades, and cloud migration to the point where business cases that previously did not pencil out are now viable. Legacy estates of twenty to fifty applications that would conventionally take two to three years can be addressed in a fraction of that time. Our blog post The Problems Nobody Wants to Solve covers how AI is changing the economics of automation for the operational work that always gets deferred.
This is one of the most common ways clients first engage us. Roughly 36% of enterprise AI licences become active users; the rest sit dormant. Adoption is rarely a tool problem. It is a workflow, training, and governance problem. We build the AI Champions network, AI Council, role-specific enablement, and measurement systems that turn licence counts into actual usage and measurable business outcomes. General Bank of Canada's approach in our CIO Spotlight series, Removing the Drudgery, is a good illustration of how an adoption-led mindset shapes the work.