Embracing artificial intelligence (AI) in the contact centre has become a non-negotiable, but for many operators, defining and measuring its return on investment (ROI) to justify the spend is where the challenge lies.
While improving customer experience (CX) is a top priority for every contact centre operator, AI is not a “one-size-fits-all” solution. This means measuring the way AI delivers a return requires context – ROI looks very different depending on whether a contact centre is handling a surge of incoming queries or trying to proactively reach new customers.
Geographic location is another important consideration. For instance, a contact centre in South Africa that prioritises the country’s employment mandate may measure AI’s impact against its ability to improve performance and boost capacity without reducing headcount.
In contrast, an operator in the UK and US would likely measure success based on cost containment and overall savings, offering the ability to rationalise and optimise operations to mitigate rising agent attrition rates.
Operators can also apply a risk management lens to ROI measurement, as AI can help deliver compliant conversations that reduce the risk of fines or lost revenue from poor execution.
As such, building a winning business case for AI in the contact centre requires an individualised approach that clearly defines whether the target is efficiency, growth, cost savings or a combination.
Taking a deep dive
No matter the objective, the first step to realising a return on AI spend is using the technology to analyse operations and uncover opportunities to identify bottlenecks, streamline processes or create operational efficiencies.
The data-driven insights that AI can generate is the ideal starting point for the application of this technology in the contact centre because it is profoundly effective.
While an AI-led data-driven insight process may only produce material results over months, which can make it a hard-sell upfront to business executives demanding quick wins, this process is the starting point to find hard ROI use cases in the business.
In fact, Connect’s conviction in the power of AI in the data discovery process prompted the company to offer a guarantee that customers will receive a three-times return in value for their spend during this phase.
Armed with data-driven insights, Connect works with customers to identify the low-hanging fruit, rolling out AI in areas of the business that can deliver immediate gains and align with specific return metrics, and address issues in the business where shortfalls exist.
The data discovery phase is also a vital precursor to designing customer journeys that automate repetitive processes or deflect specific engagements into automated channels to reduce costs and drive faster resolution rates.
For example, if you ask a customer if they want to talk to a bot, most would say no. However, if you ask if they want to resolve this issue quickly and efficiently, everyone says yes.
The data-driven insights that AI can deliver help define the customer journey map and shape the processes they want based on historical data.
Inbound ROI: Driving efficiency
For inbound functions, the primary goal of AI is to remove friction. It’s about making the service experience smoother for the customer and more manageable for the agent.
In this realm, ROI is often measured by cost reduction – fewer agent hours per interaction and lower overheads, combined with improved customer satisfaction (CSAT) scores.
For starters, AI-powered chatbots and IVRs can handle high-volume, repetitive tasks, like password resets or order tracking, without requiring human intervention. This frees agents to focus on higher-order, value-adding engagements.
AI also streamlines task completion for agents, providing full, detailed summaries at the end of each call. This simple yet impactful application reduces an agent’s admin burden, ensuring they can take the next call sooner.
During customer engagements, AI assist superpowers agents with knowledge from databases and other sources, offering invaluable support during service calls.
In the inbound contact centre environment, AI also serves as a powerful tool to guide compliance checks around customer account access, or resolving technical queries or troubleshooting faster and more effectively by providing scripted prompts and surfacing relevant information or data in real-time.
Outbound: The revenue driver
For outbound functions, the focus often shifts from saving money to making it – or getting it in.
When applied correctly, AI can turn cold outreach into smart engagements, which is clearly evidenced in the debt collections sector.
By analysing an operator’s customer database and historical engagements, AI can identify trends, defining who pays on time and allocating low-cost, automated digital channels for those collections.
When engaging with historically tardy payers and non-payers, AI can create scripts and prompts for agents, and support predictive engagement by identifying the best times to call to reduce friction and optimise outreach for improved outcomes.
Similar applications exist in supporting the sales function. In difficult selling situations, AI can leverage real-time sentiment analysis and historical customer data to identify buying signals, generating individualised scripts and prompts to help agents close the sale. By analysing past purchase behaviour, AI can also prompt agents with the perfect product recommendation during a proactive outreach call.
The back-end benefits
While these customer engagements are taking place, AI can also listen in the background to understand where people are successful in their job role and where they are falling short.
By identifying areas for improvement, AI can develop coaching at an individual agent level to help elevate journeymen to the same level as the operator’s best agents, along with broader training for the entire agent workforce.
The same listening function can also transform the quality assurance (QA) and regulatory compliance functions. By monitoring and assessing live calls, AI can ensure that every word from every agent is compliant.
For example, AI can intervene in calls when agents forget to follow due process before jumping into a financial advisory service call. AI can prompt the agent to provide all the necessary disclosures to comply with the Financial Advisory and Intermediary Services (FAIS) Act before talking about a financial product, because doing so afterwards renders the discussion non-compliant.
Operators can also use AI to automate certain compliance tasks, leveraging IVR to share cautionary disclosures upfront before connecting to an agent, or handling terms and conditions at the end of the call, or have them sent via WhatsApp to have the customer agree, ensuring the agent can jump onto the next sales call.
From a QA perspective, traditional processes assess agents according to declarations, call procedures and etiquette. However, manual QA processes typically assess between 1.5% to 5% of all calls.
AI-driven automation moves that figure to 100%, which means QA managers can focus more of their time on mitigating risk and identifying points of failure in conversations that require more training.
Generating ROI from AI
Ultimately, a mature AI strategy targets both efficiency and effectiveness. By automating the low-value inbound tasks, you free up budget and talent to focus on high-value engagements that help grow the business. As such, calculating the ROI of AI isn’t just about the technology, it’s about alignment.
Whether you are aiming to slash your cost-per-contact or shatter your sales records, the most successful implementations are those that clearly and accurately define the metrics that define successful inbound and outbound operations, and then partner with the right contact centre AI specialist that can deliver the ideal AI solution to address those specific issues or opportunities.
Frequently asked questions.
What is the ROI of AI in inbound and outbound contact centres?
AI delivers ROI differently depending on the function. In inbound centres, ROI is typically measured through efficiency gain, reducing agent hours per interaction, lowering operational costs and improving customer satisfaction. In outbound centres, AI drives revenue by optimising engagement, supporting predictive outreach and enhancing sales or collections performance. ROI also depends on geographic context, risk management priorities and specific business objectives.
How does AI help improve contact centre operations?
AI analyses operational data to identify bottlenecks, streamline processes and uncover efficiency opportunities. In practice, AI can automate repetitive tasks, assist agents with real-time insights, guide compliance and personalise customer interactions. It also enables continuous quality assurance by monitoring 100% of calls, identifying coaching needs and supporting agent development, all of which contribute to both cost savings and revenue growth.
How should contact centres approach AI implementation to maximise ROI?
The key is an individualised, data-driven approach. Start with a discovery phase to generate insights from operational data and identify areas for immediate impact. Define clear metrics for success, whether focused on efficiency, growth, cost reduction or a combination.
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