AI automation
We sell no platform. We have no vendor relationship. Every AI engagement begins with the business case, not the technology selection.
Strategy before stack.
The POC Graveyard
Three pilots. None in production.
Trust exhausted.
This is the most common state in which organisations come to Advait for AI work. Multiple rounds of POCs, each with different vendors. Each pilot succeeded in isolation. None reached production. The organisational belief in AI has been quietly exhausted.
The failure mode is consistent: the technology decision came before the business case. A vendor told them their platform was the answer. They built a demo. The demo worked. Then it encountered the real data foundation — and broke.
​
Advait does not begin with a platform. We begin with a readiness assessment.
Why most AI programmes fail
Technology selected before business case was defined
Data foundation insufficient for production-grade model training
No governance framework for model quality and retraining
Change management absent — the organisation did not trust the output
POC success metrics did not translate to production value metrics
The Advait AI Readiness Assessment
3 weeks. Before any tool is selected.
A structured entry engagement. Standalone, fixed-fee, low-risk.
The most important investment before an AI programme begins.
step 1
Use-case identification & ROI modelling
We map all candidate AI use cases against business value and implementation feasibility. We select 1–3 high-ROI, production-viable use cases. We model the expected return and the realistic cost of each. You see the numbers before any platform is discussed.
step 2
Data foundation assessment
We audit the current data landscape: quality, completeness, accessibility, governance. We identify the specific data work required before a model can be trained reliably. Most organisations discover this is the most consistently skipped step — and the most expensive to skip.
step 3
Governance & change management framework
We design the operating model for AI: who owns model quality, how retraining is triggered, how output is validated before it reaches a decision. Governance built before deployment, not retrofitted after failure.
step 4
Technology recommendation
Only at step 4 do we recommend a platform — based on the specific use cases, the existing IT landscape, and the organisation’s internal capability to maintain what is built. We have no vendor preference.
Proof is in
The Pudding
3x
pilot success vs industry avg
$8.4M
annual inventory cost reduction
6 wks
readiness to pilot
91%
demand forecast accuracy
(from 64%)
$300M retailer: 3 failed POCs rebuilt. Demand forecasting
64% → 91%. Production in 11 weeks.
$8.4M
annual inventory cost reduction
Frequently Asked Questions
We have no preferred platform and no commercial relationship with any AI or cloud vendor. We have delivered using AWS, Azure, GCP, SAP BTP, and open-source frameworks. The platform we recommend is determined entirely by the use case, the data environment, and the organisation’s ability to maintain what is built.
Both. The readiness assessment is strategic. For organisations that proceed to implementation, Advait’s team includes data engineers and ML engineers who build production-grade models. We deliver working systems, not recommendations about working systems.
Yes — and we start with the same forensic approach we apply to failed SAP implementations. The retailer case in our Work section began this way: three prior POCs, none in production, trust exhausted.
Readiness assessment: 3 weeks. Pilot (single use case): 6–12 weeks. Production deployment with data foundation in place: 8–16 weeks. The most variable element is the data foundation work required before reliable model training is possible.