“AI readiness” gets thrown around like a vibe. In practice, it’s a set of concrete checks that answer two questions: Can your organization support real AI use cases without breaking security, budgets, or operations? And what should you build first to get value fast?
A strong partner should handle both halves of the job:
- Readiness assessment: data quality, architecture, security, governance, operating model, and a short list of use cases that make sense for your business.
- Implementation: turning that plan into working systems, then putting guardrails around them so they can run in production.
Below are five companies that publicly describe work across readiness and delivery.
How We Picked These Companies
Selection criteria were simple:
- They describe readiness assessment as a defined service or workflow.
- They also describe implementation (integration, engineering, deployment, or PoC-to-production work).
- Their materials point to practical outputs like roadmaps, gap analysis, or build plans—not vague vision decks.
1) WiserBrand
WiserBrand stands out because it presents readiness and implementation as two connected steps, not separate projects. Their AI strategy work explicitly calls out AI readiness & risk assessment alongside data foundations, roadmapping, and lifecycle practices like MLOps. Their AI integration workflow starts with a discovery-and-assessment phase that looks at systems, processes, and data readiness before moving into delivery.
What Their Readiness Work Looks Like
Expect the readiness phase to focus on what you can deploy with your current data and systems, where the gaps sit, and what needs to happen first. WiserBrand frames this around infrastructure, data maturity, and organizational readiness, with an explicit risk lens.
That matters because many AI projects fail for reasons that have nothing to do with model quality: permissions, data lineage, inconsistent definitions, and unclear ownership. A readiness assessment should surface those issues early, then convert them into a buildable plan.
What Implementation Typically Means Here
Their integration workflow is structured as a sequence: assessment first, then strategy, then build and rollout steps. In practice, implementation can include:
- integrating models into existing products or internal tools
- building data pipelines that feed AI features reliably
- setting up monitoring, evaluation, and release processes so AI changes don’t become chaos
Their published service structure suggests you can keep one partner from readiness through deployment, which reduces handoffs and decision drift.
Best Fit
Teams that want one accountable partner from “Are we ready?” through “This is live,” without splitting strategy and engineering between vendors.
2) Thoughtworks
Thoughtworks has a clear public posture on AI readiness, including a readiness self-assessment and a broader enterprise AI framing that links readiness to prioritization and delivery planning.
Readiness Strength
Their AI readiness materials emphasize understanding readiness before you build, buy, or scale, and they describe assessing readiness across multiple dimensions. This is useful when the blockers are organizational: unclear ownership, weak governance, or a platform that can’t support new workloads.
Implementation Strength
Their enterprise AI description connects readiness assessment to identifying blockers and catalysts, prioritizing use cases, and creating a roadmap for continuous delivery. That’s the right shape for teams that want more than a single pilot.
Best Fit
Organizations that need a structured readiness framework and a delivery roadmap built for iterative rollout, not a one-off demo.
3) Slalom
Slalom is a solid option for companies that want a readiness-driven plan connected to real implementation in mainstream enterprise stacks. Their published assessment offerings often frame readiness through people, process, and technology, with a roadmap as a core output.
Readiness Strength
In their Copilot-focused assessment listing, Slalom describes an approach that evaluates current state, identifies gaps, and co-creates a roadmap. Even if you’re not doing Copilot, that structure maps well to broader AI readiness work.
They also present AI-driven analysis products like enhanceIQ that surface opportunities and potential solutions across roles, which can help teams translate “AI potential” into practical workstreams.
Implementation Strength
Slalom’s consulting DNA typically shows up in the “make it real in your organization” phase: change management, operating model shifts, and adoption support—areas that slow down AI programs more than most teams expect. Their roadmap-first assessment language hints at that orientation.
Best Fit
Teams planning broad adoption across business functions, especially when rollout requires training, new workflows, and governance.
4) Quantiphi
Quantiphi positions its readiness work as a defined assessment offering, with supporting materials that connect readiness to practical delivery on major cloud ecosystems.
Readiness Strength
Their AI maturity assessment is explicitly packaged as an enterprise service. They also offer a time-boxed “AI readiness workshop” through AWS Marketplace that focuses on identifying high-impact agent use cases and assessing readiness in that context.
That’s useful if you want a structured discovery sprint and you already know the general direction (agents, automation, copilots, or GenAI features).
Implementation Strength
Quantiphi has a strong footprint around cloud delivery patterns, and their AWS-linked offerings suggest they can connect readiness outputs to build steps in that ecosystem.
Best Fit
Teams that want a packaged readiness engagement and plan to build on AWS-centric tooling or similar enterprise cloud stacks.
5) SoftServe
SoftServe is a good fit for organizations that want a defined readiness engagement tied directly to a proof of concept, with documentation that makes the scope concrete.
Readiness Strength
SoftServe publicly describes a “Generative AI Readiness Assessment and Proof of Concept” that focuses on identifying practical applications and evaluating feasibility and usability before committing to full buildout. That’s the right direction for teams trying to avoid pilot theater.
Implementation Strength
Their AI service lineup positions them to move from assessment into delivered AI/ML systems across business use cases. The readiness-to-PoC packaging can shorten decision cycles, especially if leadership wants evidence before approving broader rollout.
Best Fit
Teams that want readiness plus a PoC in one engagement, with a concrete “go / no-go” decision at the end.
A Practical First Step You Can Take
Before you talk to any firm, write a one-page brief:
- The top three workflows you want to improve (real workflows, not abstract goals).
- The systems that touch those workflows (CRM, ticketing, ERP, analytics, docs).
- The constraints: data sensitivity, latency, budget range, and who can approve access.
Bring that brief to your calls. A serious partner will ask follow-ups, challenge assumptions, and propose a readiness phase that produces decisions—not slogans.
