AI predictive routing

Traditional routing asks a simple question: which available agent has the required skills, and who has waited longest? Genesys Cloud AI predictive routing asks a better one: of the agents who could take this interaction, who is most likely to achieve the outcome we want? Predictive routing uses machine learning to rank each agent in the target pool for how well they are predicted to handle a specific interaction, then offers it to the strongest match. It supports inbound voice, email and asynchronous messaging, including SMS, web messaging, open messaging and third-party messaging platforms.

The appeal for contact centre leaders is clear. Skills and proficiencies are human estimates, updated occasionally; predictive routing reflects how people actually perform, interaction by interaction. It can surface strengths, such as an agent who is unusually effective with a particular kind of contact, without anyone defining a new skill. Genesys states that personally identifiable information is excluded from routing decisions. QVCCS helps you decide whether predictive routing is right for your contact centre today, which queues would benefit most, and what must be in place before machine learning can add value rather than noise.

Predictive routing is a routing method set at queue level, alongside standard, bullseye, preferred agent and conditional group routing. Each queue optimises one KPI. Genesys lists Average Handle Time and Next contact avoidance as generally available and Custom KPI as beta. Custom revenue KPIs – sales conversion, customer retention, customer churn and sales value – can be driven by wrap-up codes or by outcome data sent from systems such as your CRM through a Genesys API, and Genesys has announced a refreshed framework for them that no longer relies on Journey Outcomes. We confirm beta access with Genesys before designing a queue around a custom KPI. Predictive routing also consumes Genesys Cloud AI Experience tokens for the interactions it routes, so we model that consumption in the business case.

Adoption follows three phases. A benefit assessment, which incurs no charge and changes no queue settings, estimates optimisation potential per queue and KPI; Genesys checks, for example, for at least 900 inbound interactions over 45 days of the last 90. A comparison test then alternates predictive routing with your existing standard or bullseye method on the same queue. Finally you either run ongoing value monitoring, which routes predictively 80% of the time against a 20% baseline, or activate it full time. Workload balancing, skill matching and reporting on how agents were scored are part of the configuration QVCCS designs.

Machine learning can only optimise what it can measure. If conversion is the goal but sales outcomes live in another system, or wrap-up codes are vague and optional, the model has nothing trustworthy to learn from. QVCCS starts with a data readiness assessment: interaction volumes per queue and agent, consistency of wrap-up codes, availability of outcome data and the integrations needed to bring it into Genesys Cloud. Where gaps exist, we specify the outcome feed in an Interface Control Document and build the data capture first, which improves your reporting even before predictive routing is switched on.

Choosing the KPI is a business decision with consequences. Optimising purely for handle time can reward rushed conversations; optimising for conversion on a service queue can feel wrong to customers. We facilitate that decision with operations, finance and customer experience leaders, use MoSCoW prioritisation to agree guardrail measures that must not deteriorate, and record the rationale in the design decision log. We also confirm which queues are suitable – those with enough volume, enough agents and enough variation in outcomes for prediction to make a difference, and how mixed-media queues should be handled.

Governance protects both customers and colleagues. Agents may worry that a model judges them, and supervisors may see work distributed differently than they expect. We design clear communication, monitoring of workload distribution and a review cadence that looks at outcomes, fairness and side effects, with defined steps for returning a queue to its baseline routing method if results disappoint. This aligns with your wider AI governance and with Genesys platform assurance such as ISO 42001 for AI management, so predictive routing is managed as a responsible, explainable use of AI rather than a black box.

Predictive routing sits where data, routing and people meet, so we muster specialists from our own bench for all three. A Senior Business Consultant / Business Analyst leads discovery with operations, data and customer experience stakeholders and writes the success measures. A Solution Architect owns candidate queues, KPIs, outcome capture, the comparison approach and governance. A Senior Developer builds the wrap-up structures, outcome integrations and data actions that feed results into Genesys Cloud, and systems integration testing confirms that outcome data arrives against the right agent and conversation before any comparison begins. A Business Analyst interprets the results with you, including any queue where predictive routing does not help, and we are prepared to tell you when a queue is not ready.

Comparison results are easily skewed, so the test plan controls for the obvious distortions. Changing skills, staffing patterns or queue membership mid-test makes the two routing methods incomparable, so we agree a change freeze for the queues involved. Seasonal peaks, campaigns and outages can swamp a modest effect, so the plan sets a test period and the secondary measures to watch, such as customer satisfaction, transfers and workload balance. Small agent pools limit how much any ranking can help, and a single strong agent who leaves can erase the gain. We also watch occupancy, because routing more work to the best-matched agents can tire them, and we brief workforce planners and supervisors on how distribution may change before the test starts.

Models reflect the contact centre they learned from, so changes in products, teams, channels or customer behaviour can shift their effectiveness. We run ongoing value monitoring reviews that compare the predictive and baseline results, check workload fairness, and confirm that wrap-up and outcome data remain consistent, since a quiet change to wrap-up codes can undermine a revenue KPI. Release impact assessments cover Genesys changes to predictive routing, KPIs or outcome APIs, and support fits your Genesys Cloud CX consumption model. We recommend when to change a KPI, extend to further queues or return a queue to conventional routing, all through change control. As your AI capability grows, we connect predictive routing with virtual agents, agent assistance and analytics.