AI Readiness Assessment

AI readiness assessment is a practice of a structured evaluation of your company in 5 directions:

1. data quality and availability
2. infrastructure and tooling
3. talent and skills
4. business processes
5. governance

After the assessment, the increments include a scored report with clear next steps for each area, so you understand what could be implemented, where, how fast, and how much it would take.

Our strategy work is engineering-grounded and practical. Every recommendation is actionable since it’s validated against your actual data, infrastructure, and team capacity, not theoretical and generic best practices. Additionally, we also offer PoC delivery to validate key assumptions before full investment and a full-scale AI adoption.

After we assess your AI readiness, you receive a scorecard with a prioritized AI opportunity map. Moreover, a phased roadmap helps to initiate and complete a project faster with risks registered.

An external assessment grants a structured, benchmarked view against companies at a similar stage of AI maturity in your sector. Many clients use the readiness scorecard specifically to align internal stakeholders on priorities that were previously contested.

Yes. If you proceed to development with us, the same team that diagnosed your gaps will design and build the solution, removing the translation layer that causes most gaps. We support the full AI lifecycle: 

1. assessment
2. strategy
3. roadmap
4. PoC
5. MVP development
6. post-launch advisory

You can engage us at any stage or for all of them.

The standard assessment takes two weeks from kick-off to final report delivery. Interviews and data collection for 3 days, 5 days of analysis, 3 days of draft review, and 2 days for finalization. The enterprise assessment is longer and covers multiple business units and adds vendor evaluation. It takes a month using the same structure with expanded scope in the analysis phase.

A productive assessment typically requires one executive sponsor for the kick-off workshop; one IT or engineering lead for the platform review; one data or analytics lead for the data inventory review; one security lead for the security posture review; and one or two business-unit leaders who own the candidate use cases.

That is 6–8 interviews of 45–60 minutes each, plus a half-day kick-off workshop. Total time investment from your team is 10–12 hours spread across the two-week engagement.

No. Data discovery and quality assessment are core components of the AI readiness assessment, not preconditions for it. We begin from whatever state your data is currently in and produce documents with strategies and roadmaps. Many clients use the data findings as the primary input to their post-assessment infrastructure investment decisions.

The 5-pillar framework is the same for both. However, a gen AI readiness assessment focuses the shortlist on large-language-model and multimodal applications to add evaluation criteria for prompt engineering capability, retrieval-augmented generation infrastructure, and LLM provider selection.

A general assessment in its turn applies a broader filter that includes predictive models, computer vision, and optimisation use cases alongside generative AI candidates. Most enterprise clients benefit from a combined evaluation.

No. Devox is vendor-agnostic and has no commercial referral arrangements with any foundation model provider, cloud platform, or MLOps tooling vendor. During the platform and use-case evaluation, we rank options on three criteria: technical fit for your specific requirements, total cost of ownership at your expected inference scale, and portability risk if you need to change providers later. You make the final selection.

Yes. The governance pillar explicitly covers sector-specific regulatory obligations.

For financial services, this includes GDPR data-processing requirements, model risk management obligations, and relevant PSD2 constraints.

For healthcare, it covers HIPAA data handling, clinical decision-support regulatory classification, and relevant software-as-a-medical-device considerations.

For manufacturing and logistics, it covers applicable ISO standards and supply-chain data-sharing obligations. We reference NIST AI RMF and ISO/IEC 42001 as the cross-sector baseline in all engagements.