Enterprise AI can produce impressive usage figures without producing a measurable outcome. Ronnie Sheth, CEO of SENEN Group, sees organisations acting on executive pressure before they have a data strategy, operating model, or roadmap that explains what value should change.
Gartner estimates poor data quality costs organisations an average of $12.9 million annually through wasted resources and missed opportunities. AI can magnify that cost by turning weak information into fast, confident output.
Sheth’s prescription for 2026 is deliberately practical: fix the data, define the outcome, and only then choose the model.
Data Strategy Must Precede Data Governance
One SENEN client initially requested a governance initiative. The deeper need was a data strategy explaining why information mattered, how it would be used, and which business outcomes the organisation wanted.
Governance without that direction can become a catalogue of rules detached from decisions. Strategy defines priority, ownership, quality, and acceptable use; governance turns those choices into an operating model and controls.
The client progressed from raw data through descriptive and predictive analytics before developing an AI strategy. That sequence builds capability while exposing errors earlier, when they are cheaper to correct.
Organisations should identify authoritative sources, resolve conflicting definitions, assign owners, measure quality, and document lineage. A clean demonstration dataset is not enough if production workflows still depend on fragmented records.
Quality targets should follow the decision: an optional summary and a regulated customer outcome do not need identical controls.
Readiness Is an Outcome, Not a Tool Count
Companies often begin with a directive to “adopt AI” and celebrate employee accounts or pilot volume. Neither metric proves that revenue, cost, risk, service, or decision quality improved.
A readiness assessment should connect a defined problem with available data, process ownership, user capability, governance, and a credible baseline. It may conclude that conventional analytics or workflow redesign should come first.
That conclusion is progress, not delay. Building a model on poor foundations creates correction work, distrust, and technical debt that make every later use harder.
Once shared data is reliable, the investment becomes reusable. Multiple models and applications can draw from the same governed foundation rather than repeating integration and reconciliation.
Readiness also includes people. Business owners must know what decision the system supports, employees need training and recourse, and technical teams require monitoring and incident procedures.
Practical AI Connects Deployment With Value
Sheth argues that the enterprise conversation has shifted since 2024. More organisations now seek help with data before demanding immediate AI adoption, indicating that early disappointments have improved strategic maturity.
Practicality does not mean abandoning innovation. It means selecting a bounded use case, defining the expected outcome, testing it against current performance, and expanding only when evidence survives production.
Metrics should include correction, failure, adoption, and downstream effects alongside time saved. A system that generates faster output but requires hidden review may move cost rather than remove it.
Leaders should also decide when to stop. If a use case lacks adequate data, cannot be evaluated, or automates a broken process, resources belong elsewhere until the constraint changes.
SENEN’s role spans data and AI advisory, operationalisation, governance, and literacy. That breadth reflects the nature of the problem: no model can compensate for missing ownership or an organisation that does not agree what its data means.
The strongest AI roadmap may begin with work that does not look like AI. Definitions, stewardship, access, quality controls, and process maps rarely command a keynote, but they determine whether later output can be trusted.
Enterprise AI becomes practical when the question changes from “How many people are using it?” to “Which outcome improved, and can we explain why?” Data creates the foundation; disciplined measurement turns it into value.
