Almost everyone is investing in AI. Almost no one has finished the job.
92% of companies plan to invest in generative AI over the next three years. Around 1% believe that investment has reached maturity. The gap is rarely budget or model access — it is delivery: choosing the right first use case, proving value in weeks, and building the governance and skills to scale what worked.
This page is how we close that gap as Certified Claude Architects. It follows the foundation → pilot → scale approach Anthropic developed with enterprises including Cox Automotive, Thomson Reuters and NBIM, and it is the framework we run on client engagements.
Don't transform everything. Find the two places AI earns its keep.
The most common failure we get called in to fix is scope — a company tries to become AI-first everywhere at once and gets nowhere convincingly. These are the functions where enterprises see the fastest, most defensible returns, and what we build in each.
Agentic coding in the SDLC
Code generation, review and debugging, then the harder work — feature prototyping and large code migrations. The real shift is that engineers stop avoiding unfamiliar parts of the codebase because building context no longer costs hours.
Contract review and first-pass drafting
Terms review, standard document drafting, and research synthesis across sources. Routine questions get answered immediately instead of queuing, so legal spends its time on real negotiation.
Content at brand standard
Campaign and content production in hours rather than days — but only once your voice and style rules are encoded as instructions, so output arrives on-brand instead of needing a rewrite.
Reporting and variance analysis
Executive summaries generated from complex datasets, key metrics extracted from quarterly results, anomalies flagged for investigation, and variance explained in language the business actually reads.
Deflection, then product features
Start internal — an assistant over IT and support knowledge, where a wrong answer costs little. Once accuracy is proven under real traffic, the same architecture graduates to customer-facing features.
The question people won't ask a colleague
An assistant wired to every documentation source through MCP. New hires get answers in minutes instead of raising tickets, and become effective in their first week rather than their first month.
Foundation, pilot, scale — and what we own in each
Below is the full arc of an engagement. A Certified Claude Architect leads the technical layer throughout; your team keeps ownership of the product.
Lay the foundation
Organisational groundwork first: a clear strategy tied to business objectives, executive alignment that survives the first setback, and governance written down before anything ships. Technical solutions alone don't drive transformation — treat change management as an afterthought and adoption stalls no matter how good the build is.
Launch a pilot
One or two pilots, deliberately spanning different functions so the organisation sees versatility rather than a single trick. Sprint-based delivery, with meaningful results inside 30–60 days. Pilot teams get dedicated time and named roles — a pilot lead who owns outcomes, technical resources, business users who test real scenarios, and an executive sponsor. AI as someone's tenth priority fails predictably.
Scale impact
Scaling is not replicating what worked — it is building capability at every level. Different audiences need genuinely different journeys, and a pilot win that stays inside one team is worth almost nothing to the business.
Agree the numbers before the build, not after
Every engagement fixes four dimensions of success up front, with automated collection wherever possible so reporting isn't a manual tax on the team. Weekly to catch problems, monthly for trend, quarterly for the executive story.
The earlier you write the rules, the faster you can move
Governance is not the brake on an AI programme — it is what lets you say yes quickly. Teams that leave it until scale end up retrofitting policy onto systems already in production, which is where projects quietly die.
We write your framework during Step 1, tuned to your jurisdictions — GDPR through to sector rules in healthcare and financial services — and we hold the build to it.
Score yourself honestly before you pick a path
Pick the number that describes your organisation on each of the eight dimensions. Your total tells you whether to launch broadly, start narrow, or fix the foundation first — and it is the first thing we work through on a discovery call.
Scroll the table sideways on a narrow screen →
Plenty of firms will hand you this framework. We've run it on ourselves.
Certified, not self-declared
Claude Certified Architects and a Claude Partner badge for Claude Code, issued by Anthropic and verifiable on Credly.
See the credentials→Agents in production, ours
OpsAgents.AI is our own platform for deploying, monitoring and governing Claude agents — where the SOSA methodology came from.
Read the case study→We built the trainer
Thousands of exam-level questions, mock exams and group rounds across three certification tracks. Free, and how our own architects certified.
Open the trainer↗Want the original? Read Anthropic's guide.
Everything on this page is our delivery of the framework Anthropic published — the three steps, success metrics, governance components, the readiness matrix, and how Anthropic's own engineering, legal, finance and HR teams run on Claude.
Open the guide↗Start with the readiness matrix. We'll take it from there.
Score yourself on the eight dimensions above and send us the total. We'll come back with a candidate first pilot, a realistic timeline, and what your team needs to learn to own it afterwards.