How &above Delivers Enterprise AI at Startup Speed

Most enterprises know what AI should deliver. Fewer know how to make it happen fast. Deloitte's 2026 State of AI in the Enterprise report reveals the gap: 42% of companies believe their strategy is ready for AI adoption, but they feel unprepared when it comes to infrastructure, data, risk, and talent. The result? Stalled pilots, fragmented initiatives, and AI programs that take quarters to deliver what should take weeks.

&above operates differently. Startup speed. Enterprise scale. The kind of velocity that takes their Google Cloud solution from concept to deployment in 90 days, cutting costs by 84% while accelerating timelines by 3x. That's not luck or shortcuts. It's methodology.

This is how it works.

The Speed Problem in Enterprise AI

Traditional consultancies move slow by design. Discovery phases stretch for months. Deliverables arrive as slide decks, not working systems. Knowledge stays locked in the consulting team, not transferred to yours. When the engagement ends, momentum dies.

Pure tech vendors move fast but shallow. They ship tools, not transformation. AI gets bolted onto legacy workflows instead of reshaping them. Adoption stalls because nobody designed for the humans who have to use it daily.

The industry data proves it: Gartner predicts that by 2026, over 80% of enterprises will have GenAI in production, but most will struggle to scale beyond isolated use cases. PwC's research shows the pattern: organisations spread efforts thin, placing small sporadic bets that deliver modest efficiency gains but never add up to transformation.

&above sits between these extremes. Enterprise rigor without enterprise drag. Startup velocity without startup chaos. The approach flips traditional consulting: instead of delivering then disappearing, teams embed, build side-by-side, and transfer knowledge as they go. Tools stick. Momentum builds. Results compound.

The 90-Day AI Launchpad: From Opportunity to Production

Speed starts with structure. The AI Launchpad compresses what typically takes six to nine months into 90 days, moving from opportunity identification to working prototype to production-ready system. This AI strategy and transformation methodology combines Define, Build, and Embed phases to deliver measurable outcomes fast.

Define: Uncover Opportunities & Test What's Possible

Most AI initiatives stall before they start because teams can't align on what to build or why it matters. The first phase cuts through ambiguity:

• AI opportunity discovery sessions map where AI delivers genuine business value, not just technical novelty
• Hands-on co-creation workshops bring stakeholders into the build process early, surfacing constraints and priorities before they become blockers
• Proof of concept development validates feasibility with real data and real workflows, not hypothetical scenarios

This phase typically runs between 2-4 weeks. The output isn't a lone strategy document. It's a working PoC and a roadmap with clear sequencing.

Build: Craft Smart & Scalable AI Products

With direction locked, the build phase moves fast. Designers, engineers, and AI specialists work as a single unit, shipping iteratively rather than waiting for perfection.

Behavioral design sits at the core. UXMatters' 2024 research shows behavioral design increases adoption rates by up to 200%. That's the difference between an AI tool that gets ignored and one that becomes essential. &above designs for the humans first, then wraps AI around their actual workflows.

The results show it. Dunnhumby's self-service adoption jumped 2.6x. Outperform's sign-ups climbed 210%. These aren't marginal improvements. They're step-changes driven by systems people actually want to use.

Embed: Integrate Experts to Transform & Iterate Fast

AI transformation doesn't end at deployment. The third phase embeds experts directly into client teams to sustain momentum, refine systems, and scale across the organisation.

• AI training and workshops build internal capability so teams can iterate without external dependency
• Embedded delivery squads plug into existing structures, working in client hours and adapting to client rhythms
• Office hours and consultancy provide ongoing support as edge cases emerge and requirements evolve

This is the &above philosophy in action. Knowledge doesn't leave when the engagement ends. It stays embedded in the team, the tools, and the processes.

Why Speed Doesn't Mean Shortcuts

Fast delivery without rigor creates technical debt and adoption failure. &above's speed comes from eliminating waste, not cutting corners.

Clear goals before code. PwC's 2026 AI predictions emphasise what separates successful AI programs from stalled pilots: leadership picks specific, high-value workflows and applies enterprise muscle to execute. &above follows this pattern. The Define phase locks goals, success metrics, and ownership before building starts. No ambiguous mandates. No scope creep mid-sprint.

Behavioral scaffolding, not feature lists. Most AI projects fail on adoption, not technology. Teams build impressive capabilities that nobody uses because the system doesn't fit how people actually work. Behavioral design addresses this upfront, designing workflows around human behavior rather than forcing behavior change to match the tool. This focus on organisational readiness for AI transformation separates sustainable implementations from surface-level pilots.

Data quality as a foundation. AI can't deliver on bad data. &above treats data architecture as a first-order concern, not an afterthought. Clean, accessible, un-siloed data enables faster iteration and more reliable outputs. When Google Cloud saw 3x deployment acceleration, data readiness was part of the foundation.

Proof-of-concept discipline. Define measurable goals. Sandbox the work. Use test data. Iterate quickly. Ship value early. This approach de-risks investment and validates direction before scaling. MIT's 2025 NANDA report shows strategic partnerships like this deliver 2x the success rate of internal-only AI builds, with 88% of early enterprise adopters reporting positive ROI.

The Embedded Team Model: How Integration Accelerates Delivery

Traditional consulting creates a wall between the consulting team and the client team. Information flows through status meetings and slide decks. Decisions get delayed. Context gets lost in translation.

&above embeds directly. Designers, engineers, and strategists work as extensions of client teams, not external vendors. They join standups, use client tools, work client hours, and ship to client roadmaps.

This model solves three speed killers:

1. Decision latency drops. When the team building the system sits in the same (virtual) room as the stakeholders who'll use it, questions get answered in minutes, not days. Iterations happen faster because feedback loops tighten.
2. Knowledge transfer becomes automatic. Clients don't just receive documentation at the end. They learn by doing, side-by-side, throughout the build. By the time the system goes live, internal teams already know how to maintain and extend it.
3. Cultural fit improves adoption. Embedded teams absorb organisational context naturally. They understand the unwritten rules, the political dynamics, the legacy constraints. Systems get designed with this reality baked in, not as an afterthought during deployment.

Tesco's 71% faster campaign launches didn't come from better technology alone. They came from a team that understood how Tesco's marketing org actually operated and designed the system to fit that reality.

What Enterprise Speed Actually Looks Like

The results tell the story better than the process:

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These aren't isolated wins. They're the pattern that emerges when methodology aligns with execution.

The common thread: each engagement started with a tight scope, clear metrics, and a working prototype within weeks. No six-month discovery phases. No endless requirement gathering. Define, build, embed. Ship value early. Iterate based on real usage, not projected needs.

Deloitte's research shows only 34% of organisations use AI to deeply transform their business. The rest optimize at the margins. &above's model targets that 34%. Not incremental efficiency. Wholesale transformation of how work gets done.

Enterprise AI doesn't have to move at enterprise pace. The constraint isn't technology or talent. It's methodology. Traditional models optimise for billable hours and knowledge retention, not speed and knowledge transfer.

&above optimises differently. Tight scopes. Clear goals. Embedded teams. Behavioural design. Iterative shipping. Knowledge transfer built into every sprint. This isn't a marketing claim. It's a repeatable framework proven across Google, Tesco, Dunnhumby, and dozens of ambitious organisations.

Startup speed. Enterprise scale. That's not a contradiction. It's the operating model for AI transformation in 2026.

Ready to move from strategy to prototype in 90 days? Explore the AI Launchpad or get in touch to discuss your transformation.