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How Ramisun approaches Now Assist enablement for organisations holding AI entitlements that are not yet delivering — diagnosing the real constraint, remediating the foundations, and deploying against measured baselines.
When a Now Assist pilot fails to demonstrate value, the constraint is usually data quality rather than model performance. The approach is to run a structured readiness assessment across categorisation, resolution notes, knowledge base health, licensing and release position; remediate the specific gaps found; define AI governance with staged autonomy before any capability goes live; capture measurable baselines; then re-launch against high-volume, low-variance use cases where the outcome can be verified.
A pattern seen across the market. An organisation purchases Now Assist entitlements during a platform renewal, runs a pilot focused on incident summarisation, and finds that agents do not use the output and handling time does not move. Sponsorship fades and the entitlements risk being written off at renewal.
The instinct is to conclude the technology underdelivers. In most environments the models are performing correctly and reflecting the quality of the records beneath them — a very different problem with a very different remedy.
Generative features summarise what exists and retrieve what is indexed. A pilot on weak data faithfully reports that weakness back.
A large share of incidents sat in generic categories, leaving the model little reliable signal for similarity or routing.
Sampling closed incidents showed a majority with resolution notes too thin to summarise meaningfully.
A significant portion of the knowledge base had no owner and no review in over two years, producing confidently wrong retrieval.
Each capability is switched on only once the data supporting it can sustain it — foundations first, then staged autonomy.
Ran a seven-point readiness assessment covering categorisation, resolution notes, knowledge health, use cases, governance, licensing and release position.
Rationalised the category tree and used Predictive Intelligence to back-fill historical records — ML is well suited to this specific task.
Retired unowned and duplicate content, assigned owners and review dates to what remained, and restructured for retrieval rather than browsing.
Introduced a minimum closure standard with template prompts, improving note quality within the first quarter.
Defined approval gates, logging, escalation paths and data boundaries before any capability went live — recommend, then supervised, then autonomous.
Captured handling time and deflection baselines, then deployed against high-volume, low-variance use cases with weekly measurement.
Handling time and deflection now tracked weekly against a pre-deployment baseline.
Historical records back-filled and the category tree rationalised and owned.
Recommend → supervised → autonomous, with each stage gated on evidence.
AI capability the business was already paying for is now in daily operational use.
Book a free consultation for an honest readiness assessment — we will tell you plainly whether the gap is data, governance or genuinely the technology.
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