What’s the Next Step in Your AI Journey?

Prioritising the right AI use cases in contact centres to deliver customer benefits and ROI

Given the range of AI use cases in the contact centre, choosing those that will provide the most value can be difficult. Some may deliver limited impact or even lead to higher costs in other areas. All of this means that contact centres need to take a measured approach to their AI journey, starting by assessing their own AI maturity and then prioritising use cases based on customer and business metrics.

For further practical guidance on planning AI adoption around clear outcomes, including ROI, deployment and risk, see our guide to applying artificial intelligence in customer service.

Taking a planned approach to maximise benefits

AI is not a single application or use case. As a transformative technology it can be used across a wide range of areas within the contact centre, each of which can promise value. However, thanks to this range, picking the right applications that maximise ROI can be difficult. The result? Organisations and their customers implement AI and see some improvements, but not all that were expected.

Backing this up, in research from COPC just 44% of contact centres met their expected ROI from AI implementations ^[1]. Gartner analysis found that while AI is increasing contact centre productivity, it isn’t necessarily reducing costs ^[2].

All of this has led many people to see AI in customer service as a poor investment. However, the reality is that to deliver real positive impact, organisations need to take a strategic, mature approach to the technology. In my previous blog I explored the stages of AI maturity, and why organisations need to focus on the outcomes they want AI to deliver, not the technology alone.

Once you’ve assessed your AI maturity, you know where to start. You can then take the next steps to implement AI use cases in your contact centre, guided by business need, customer impact and ease of adoption.

Making the right decisions for AI ROI

Too often, organisations start an AI implementation by choosing a technology first, then looking for a contact centre problem it might solve. This may work in some cases, but it does not always deliver meaningful results or ROI. For example, a virtual agent may improve First Contact Resolution (FCR), but strengthening agent knowledge may achieve the same outcome at lower cost and with higher customer satisfaction.

Rather than a technology-first approach, successful AI adoption strategies require organizations to build a roadmap based on business need, customer impact, and ease of adoption. This ensures contact centre AI use cases are focused in the right areas to deliver lasting benefits.

To decide which AI use cases to prioritise in your roadmap, score each opportunity across these three areas:

Understanding the business need

Start by identifying where the contact centre could be falling short. Look at operational data first, focusing on targets or metrics that are being missed. Then build out the picture by speaking with agents and supervisors, and by reviewing customer feedback. Once you have a list of possible improvement areas, score each one by its impact on the business. This gives you a clear starting point for practical problem-solving and AI adoption.

Questions to ask:

• Where are we not hitting our targets when it comes to key metrics such as First Contact Resolution?
• How are these issues impacting the business, in terms of productivity, agent experience, or efficiency?
• Which are the most pressing in business/staff satisfaction terms?

Measuring customer impact

The objective of the contact centre is to deliver the best possible customer experience, as efficiently as possible. So, review your ranked list of business needs and score each one by the impact it would have on your customers. Ensure you cover all your channels and customer demographics; what is acceptable to some groups may be viewed negatively by others. For example, customers with 9-5 jobs may be angry that your contact centre closes at 6pm and does not operate at weekends, unlike older, retired demographics. This process may show that a pressing internal business need has limited impact on customer experience, even if AI could help solve it. That doesn’t make it unimportant, but it may place it later in your AI implementation roadmap.

Questions to ask:

• What areas do customer feedback highlight as issues?
• How does satisfaction vary across channels/demographics?
• Where does your customer experience fall below your competitors?
• Which areas do agents and customers identify as leading to churn or poor satisfaction ratings?

Focusing on ease of adoption

Finally, examine how easy it would be to solve the problems you have identified using available AI or other technology. This includes whether you will need to change processes and ways of working within the contact centre itself, retrain agents and deploy new systems. If an issue spans other parts of the business (such as finance or logistics), how easy will it be to collaborate with them to solve it? Keep in mind that people have varying concerns about AI, and this can impact engagement and adoption rates. Evaluating this metric enables you to complete your scorecard; the highest scores go to the easiest projects.

Questions to ask:

• How long will it take to implement the technology?
• How disruptive will it be to our existing processes and ways of working?
• Do we need to work with other departments to solve the issue?
• Do other solutions or changes need to be in place to enable adoption?

Putting the factors together to choose the right AI solutions

Combining your scores for all three areas produces a total, enabling you to rank projects by urgency and impact. This gives a priority pipeline of which projects to focus on first, and which need to be tackled later in your AI journey.

The key is not to start with AI, but the real business and customer need that must be solved. Only then should you look at which AI tools and solutions can meet these specific requirements. It may even be that issues can be solved through non-AI solutions, such as traditional technology or changing processes. Once you have identified solutions, create a full business case based on financial or satisfaction metrics, backed up by realistic deployment timelines in order to get buy-in and sign-off.

For a broader view of where AI pays off, what it can return, and how to go live while minimising risk, see our practical guide to applying artificial intelligence in customer service.

Balancing immediate and long-term needs

Many organisations suffer from implementing too many AI point solutions across multiple areas, without them connecting or improving performance outside a single team, as detailed in Stage 3 of our AI maturity model. This can even happen when following a planned approach based on business needs, customer impact and ease of adoption.

To ensure your AI journey is seamless, understanding where you are in terms of AI maturity will help you work out how to get to the next stage. Asking the right questions, you can move forward strategically and in a single direction, rather than in a disparate or disconnected way. Involve representatives from across the contact centre, and the overall business, in your AI discussions to give a holistic view. This will also help identify any obstacles that must be overcome to deliver success. Examples of these could be fixing data quality issues or ensuring AI solutions fit with corporate guidelines.

Confidently moving forward with AI

Applying AI can transform the customer experience, but only when it is focused on the right problems. Start by understanding your current level of AI maturity, then build a roadmap of ranked use cases based on business need, customer impact and ease of adoption. This gives you a clearer way to prioritise your next steps, invest where AI can deliver measurable value, and create lasting benefits for customers, staff and the wider business.

Enghouse Interactive: Supporting contact centres on their AI journey

Enghouse Interactive helps contact centres use AI to improve customer experiences, support agents, streamline quality processes, and uncover insights that guide better decisions. Built on deep contact centre experience and real-world understanding, Enghouse AI focuses on solving practical business challenges and delivering measurable value.

Ready to focus your AI roadmap on your greatest CX needs?

Wherever you are on your journey today, Enghouse can help you pinpoint the AI use cases most likely to improve customer, agent and business outcomes.

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About the Author

Steve Nattress VP of Product Management
Enghouse Interactive
LinkedIn

Steve’s unique background in Customer Support and AI development positions him perfectly to drive product and AI innovation at Enghouse. Steve is a creative and strategic leader, passionate about the potential of AI to transform customer experience, while staying sharply attuned to the critical need for secure and transparent industry practices.

References

1. COPC: How to Close the AI ROI Gap: Why 56% of Contact Centers Are Failing to Realize Value
2. Gartner: Customer Service Leads on AI Adoption but ROI Remains Elusive