Gartner Research 2026

Most companies never get past the experiment. VLNX delivers what is measurable.

Measurable AI outcomes, delivered by senior people and accelerated by proprietary assets — not by an army of consultants.

Talk to VLNX
How it works
45%
of high-AI-maturity organizations keep AI in production for 3+ years (vs 20%)
Gartner, 2025
57%
of high-AI-maturity organizations have the business ready and confident to use AI (vs 14%)
Gartner, 2025
39%
of technology leaders are confident their AI investment will have positive financial impact
Gartner, 2026
4×
more in data and analytics foundations is what successful AI organizations invest
Gartner, 2026
The problem

AI stalls between the pilot and the outcome

Market data shows a consistent pattern: AI initiatives don't scale, data isn't ready, and impact isn't measured.

Stalled AI maturity

Most companies accumulate proofs of concept that never reach production or the P&L.

Proofs of concept that never reach the P&L

Data not AI-ready

Without a trustworthy data foundation, models perform in demos and fail in production.

The data foundation separates demo from production

AI ROI not tracked

Without measuring financial impact, AI becomes a cost center with no mandate to scale.

Without ROI, AI becomes a cost center

Legacy architecture as bottleneck

Legacy systems and siloed data prevent delivering AI solutions at scale.

Legacy and silos block scale
AI Maturity Check

Want to see where your company stands?

A sample of VLNX's methodology: the check returns an automatic read-out of your AI maturity stage, in 2 minutes — the starting point, not the work.

The read is generated by our own AI — living proof that we run VLNX with AI.

Solutions

From strategy to production, with ROI as a commitment

Two steps, in this order: first the proof, in a non-production environment; then production. Both start from the outcome and work backwards.

Lean diagnostic + prototype with proof

The paid entry work: the priority case, the proof metric and the baseline agreed in writing; the data the case requires organized; and an orchestration prototype running end to end, in a non-production environment, with a measured result. Fixed scope and price.

  • Priority case, metric and baseline in writing
  • Case data organized — only what the case requires
  • Orchestration prototype running end to end
  • Result measured against the baseline

From proof to production

The next step, with its own proposal: what the proof measured becomes a solution in production, with data-centric architecture, observability and embedded technical governance.

  • Solution in production
  • AI-ready data foundation
  • End-to-end observability
  • Documented handover
Method

We start from the outcome and work backwards

Four steps, each with a concrete deliverable. The first two start on the platform; the third is the proof; the fourth, production. It closes with measurement — ROI as a commitment, not a promise.

  1. 01
    Outcome

    What business outcome needs to happen?

    Before any technology, we define the measurable outcome that justifies the investment — and the order of magnitude of what is at stake.

    Expected outcome + value at stake, as a range
  2. 02
    Problem

    What is blocking that outcome today?

    We map what really weighs — data, systems and integrations, orchestration, governance and risk, people and process — and where cases like yours tend to stall.

    Complexity read-out by dimension
  3. 03
    Proof

    What needs to be built — and what doesn't?

    We design the shortest path to the outcome and prove it: an orchestration prototype in a non-production environment, with fixed scope and a result measured against the baseline.

    Working prototype + measured result
  4. 04
    Production

    Iterative delivery validated by the outcome

    We ship to production in short cycles, validating each increment against the outcome and measuring impact.

    Solution in production + impact dashboard
Platform

The VLNX platform

The AI Maturity Check shows where your company stands. The platform starts from an outcome you want and shows the distance to it: what needs to be true, the value at stake as a range, how complex the case is, and whether it calls for direct execution, preparation before building, or a larger design.

The platform is free and, right now, in private mode: access is by invitation only. Join the list and we'll let you know when there's a spot.

Contact

Is your company ready to scale AI?

Tell us about your company's AI challenge. VLNX's paid work is a lean diagnostic followed by a working prototype, with a measured result and a fixed price.