Newsletter Article
Data becomes most valuable when it helps an organisation decide what to do next. AI accelerates that opportunity.
Gartner’s top data and analytics predictions for 2026 name “the need for context” explicitly as one of the themes shaping the discipline, alongside leadership, governance and talent, a sign that context is moving from a nice-to-have to a defining requirement for how AI is expected to perform. In a contact centre and enterprise environment, that plays out in a specific way.
As an example a Genesys interaction can provide rich insight into the customer experience: the channel used, queue conditions, wait time, interaction history, agent activity and what happened during the conversation. That establishes an important signal.
But the wider context may sit elsewhere.
Where context lives
- CRM, customer history and value
- ERP, order and fulfilment status
- Billing, account and payment context
Workforce systems, staffing, adherence, capacity
And beyond that
- Service platforms, cases and resolution history
- Digital systems, behaviour before contact
- Operational systems, conditions affecting delivery
Source Data, channel, queue, wait time, interaction history
Connecting those signals creates something more useful than simply accumulating more information. It creates context. And context creates the pathway to action.
See the Signal → Set the Context → Understand the Impact → Act with Confidence
A live example: an afternoon abandonment spike
Imagine abandonment increases unexpectedly during the afternoon. A dashboard can identify that the metric has changed. Analytics can show which queues are responsible. But contextual intelligence can go further, bringing together:
- Actual demand against forecast
- Scheduled versus available employees
- Adherence
- Queue performance
- Average handling time
- Channel shifts
- Customer demand patterns
- Operational events affecting contact volume
The question is no longer simply:
“What happened to abandonment?”
It becomes:
“What changed, why did it change, what is the operational impact, and which response is most likely to improve the outcome?”
That is a fundamentally more useful decision, and the same pattern repeats across the organisation:
- A CX leader sees satisfaction falling and understands the journeys driving the change.
- An operations manager sees rising demand and understands which downstream process is responsible.
- A workforce manager sees service levels deteriorating and understands the relationship between demand, capacity and availability.
- An executive sees performance move and understands the business drivers beneath the headline KPI.
This is the role context plays. It turns signals into understanding. And understanding gives people, and increasingly AI, the confidence to act.
WHAT THIS MEANS FOR EMITE
Bringing Genesys experience data together with enterprise and operational information allows the interaction to be viewed within the wider circumstances surrounding it. The objective is not simply more data, it’s the right context, available at the right moment, for the decision being made.
