Approved AI budgets stall on data infrastructure gaps
LLM projects fail when data lives in siloed systems with no catalog, lineage, or quality baseline — discovered after the budget is already committed.
Strategic Enablement
Enterprise AI Readiness
The digital assessment at /assess scores readiness across six dimensions and returns a maturity band, prioritized opportunities, and a recommended VTOS phase. Human-led Enterprise AI Readiness and Transformation Planning is the separate next step. It does not come with a fixed duration, and a 90-day plan is not appropriate for every organization.
Diagnostic score and maturity bandMCT-Led DeliveryHuman-led planning availableScope depends on complexityStrategy, Data, Skills & Governance
Why This Matters Now
The challenges that bring enterprise teams to this conversation
Engineering teams lack production AI skills despite demos working
Teams confident with proof-of-concept demos struggle with Python orchestration, Azure deployment, and prompt evaluation in production-grade implementations.
No AI policy means legal cannot approve regulated-data LLM usage
AI deployment in finance, healthcare, or government requires a documented policy framework before any LLM is permitted to process sensitive or regulated data.
Strategic context: A score is useful when someone can read it against the actual business. The diagnostic shows readiness, maturity, and priority opportunities. Planning after that is a separate conversation, and the artifact depends on complexity, readiness, and scope.
Capability Coverage
What the program covers
AI Strategy Readiness Audit
Evaluate strategic alignment, executive sponsorship, and budget-to-outcome mapping before committing to an AI implementation roadmap with hard delivery milestones.
Data Readiness Assessment
Assess data quality, cataloguing maturity, lineage documentation, and infrastructure readiness for AI and LLM workloads against specific use-case requirements.
Skills Gap Analysis by Role
Map current team skills across engineering, analytics, and product roles against the specific capabilities required for your AI implementation targets.
Governance Readiness Review
Evaluate AI policy maturity, responsible AI control coverage, compliance requirements, and accountability structures against your implementation risk profile.
Enablement After Planning
Where an enablement engagement is agreed, the pathway follows that conversation. The diagnostic score does not select a commercial track or an engagement duration.
Planning After the Diagnostic
When planning is in scope, the artifact depends on complexity, readiness, and scope. A 90-day roadmap is used when that depth is warranted. The assessment does not generate it, and it is not the right plan for every organization.
Delivery Approach
How we deliver this
01
Assess
AI Readiness Assessment
Complete the diagnostic. It returns a readiness score, dimension results, a maturity band, prioritized opportunities, and a recommended VTOS phase. It does not assign an engagement duration.
02
Design
Enterprise AI Readiness and Transformation Planning
Human-led Enterprise AI Readiness and Transformation Planning is available after the digital assessment. It reviews the business context and determines the appropriate next step. A 90-day plan is not appropriate for every organization, and the assessment does not generate one.
03
Enable
Enablement When Scope Is Agreed
Where enablement is agreed, delivery is MCT-led and matched to the approved scope. The diagnostic does not select the program or its duration.
04
Adopt
Policy, Baselines, and the Right Plan
Planning work closes with the artifact the scope warrants: policy, skill baselines, governance ownership, or a longer roadmap when complexity requires it. A 90-day plan is not the default.
One methodology
Choose the right transformation path.
The assessment returns a score, dimension results, a maturity band, prioritized opportunities, and a recommended VTOS phase. A consultant recommends the depth afterward, from the business outcome, the use case, the data, integrations, security, governance, and whether the result should be a prototype, a pilot, or production. Headcount does not set the range.
Typically 2–4 weeks
AI QuickStart
One prioritized use case for a startup, a small team, or a first serious attempt. Depending on readiness and scope, the engagement can produce a validated prototype or an operational pilot. Production depends on data access, integration, security, and operating controls.
Typically 30–45 days
AI Transformation Sprint
One substantial use case that needs more governance and integration than a QuickStart, often for a department or a smaller business. Typical outcomes include readiness findings, architecture direction, a pilot, and enablement, depending on scope and readiness. Full production still depends on security, support, and the operating model.
Typically 45–60 days
AI Transformation Program
Several teams or several use cases. The planning artifact is a roadmap. This depth is a program when VNode is carrying out the approved work. Sixty days is a typical ceiling for this depth.
Up to 90 days
Enterprise AI Transformation Program
Complex, regulated, or multi-unit work. The engagement sets direction, an operating foundation, priority implementation, and a scale model. Sustained production continues after that when the estate requires it. The 90-day roadmap is the planning artifact at this depth.
Roadmap and program
A roadmap is the plan: the sequence of use cases, architecture, controls, and ownership. A program is the engagement in which VNode carries out an approved plan.
Who executes
Customers buy the plan and execute it themselves, ask VNode to lead the work, or split it between their team and VNode.
Prototype, pilot, and production
A prototype proves the concept, the experience, or the architecture direction. A pilot is limited real use, with real users or business data, a defined scope, and success measures. Production is sustained operation, with the security, identity, governance, support, and lifecycle controls that use requires.
A QuickStart or a Sprint stops at the outcome the current readiness supports. Smaller depths use a shorter plan, such as a 30-day action plan, a 45-day plan, or a next-phase roadmap. A 90-day roadmap is reserved for enterprise complexity.
Capability Programs
Programs for this area
Proof & Perspectives
Implementation evidence and strategic context
AI Adoption & Agents3 min read
AI Readiness: The Work That Happens Before You Touch a Model
Organisations that struggle to get value from enterprise AI investments almost never fail because of model capability. They fail because the data is not ready, the teams are not aligned, and the governance layer does not exist. This post outlines the readiness work that precedes meaningful AI adoption.
AI StrategyEnterprise AIData ReadinessGovernance
Read insightReady to Begin
Start your Enterprise AI Readiness program
Work with our team to design an enablement program matched to your team's readiness, platform priorities, and delivery timeline.
Diagnostic score and maturity band
MCT-Led Delivery
Human-led planning available
Scope depends on complexity
