Where are you on your AI journey?
Wherever you are today, there's a logical next step.
Some organizations are still exploring their first AI use case. Others are already running chatbots, automation or generative AI and want to work out where AI can deliver additional value. AI needs change as you mature, but the approach to adoption is the same: start with the business challenge, then choose the use case that can deliver the value you’re looking for.
- Stage 1
EXPLORE
No AI in production yet. Action: Identify the business challenge and pick one use case worth proving.
- Stage 2
ADOPT
One live capability, often a chatbot or automation. Action: Improve performance, measure impact and support staff adoption.
- Stage 3
EXPAND
One working capability. Action: Identify the next business challenge and prioritize a use case.
- Stage 4
OPTIMIZE
Several capabilities are in place. Action: Check and tune, then look for the next gap to close.
Applying AI matters more than adding it.
A common misconception is that AI maturity means generative AI everywhere. In CX, a better outcome is from applying AI where it solves a specific operational problem, uses trusted data and provides measurable value.
The power of AI is realized only when it is practically applied. Added to a solution without thought, it can damage branding and ROI and produce results nobody wanted.
Your customers are open to artificial intelligence when it's transparent, secure, and improves their experiences.
where feasible
over satisfaction
CX metrics
Sources: Salesforce State of the Connected Customer; NTT Global Customer Experience Report.
Customer effort, not customer satisfaction, is the metric operational CX managers now watch most closely.
What Could Your Next AI Step Be?
Start from the outcome you need rather than the technology available. Four outcomes cover most of what a contact center is asked to improve, and each has a capability behind it.
Improve agent productivity
- AI summarization
- Agent assist
- Real-time coaching
Improve customer experience
- Virtual agents
- AI translation
Improve quality
- AI interaction evaluation / Quality Management
Understand your customers
- AI-powered Voice of the Customer
Find Your Next AI Step
- High after-call work AI summarization
- Agents struggle to find answers Agent assist
- High volumes of routine enquiries Virtual agents
- Multilingual service demands AI translation
- Limited visibility into quality AI Quality Management
- Agents need more support Real-time coaching
- Surveys aren't telling the whole story Voice of the Customer
One challenge in, one candidate use case out. That is the whole exercise.
Seven capabilities, and the problem each one answers
Making it easy for customers to engage has become the focus. Each of these starts from a challenge the operation already has, and names the AI capability that answers it.
Agent assist
Virtual agents
AI summarization
AI translation
AI interaction evaluation
Real-time coaching
Voice of the Customer
Improved knowledge management ranked first for boosting agent performance, ahead of higher pay and incentives.
ContactBabel, The Customer Experience Decision-Makers' Guide
If quality is your gap, a 3% sample is not a measurement
Most contact centers score a sample and use it to estimate the score for every interaction of the same type, group or agent. Adding more interactions does not fix it: overloaded supervisors introduce bias, which distorts performance metrics and undermines fair recognition of the work agents actually did. AI replaces unintended bias with objectivity.
The number of interactions typically observed as part of a traditional manual QA model do not represent a statistically valid sample set.
| Traditional manual QA, sampled | 3% of interactions |
|---|---|
| AI interaction evaluation | up to 100% of interactions |
Figures as reported in the eGuide, citing ContactBabel. Over 80% of organizations prioritizing frontline support name enhanced QA and coaching alongside increased technology investment.
Make Your Next AI Investment Count
The next step shouldn't simply be adding more AI. It should be identifying the use case most likely to deliver measurable value.
AI in CX pays in three ways: it supports staff so they can do their jobs better, it meets customer expectations by being faster and more accurate, and it holds market position as more competitors adopt it.
IDC found that for every dollar invested in AI, businesses recover an average of $3.50, and that 5% of organizations recover $8. Organizations surveyed about AI adoption are realizing a return within 14 months on average, a significant improvement on early AI timelines.
Sources: IDC via Yahoo Finance and VentureBeat; NTT Global Customer Experience Report.
Both bars drawn on the same dollars-recovered scale.
| Average recovered per $1 invested | $3.50 |
|---|---|
| Recovered by the top 5% of organizations | $8.00 |
| Businesses reporting AI automation met or exceeded expectations | 60% |
| Average time to return | 14 months |
What to measure, before and after
To quantify the return, measure a before and an after and convert both into the dollar terms specific to your contact center. Capturing these metrics from before adoption to the point where the tools are working optimally will demonstrate the success, or otherwise, of what you deployed.
| Improvement area | AI-powered tools | Measure | How to calculate ROI |
|---|---|---|---|
| Productivity | Summarization, Agent assist, virtual agents | Average Handle Time; First Call Resolution | Interaction cost per minute; cost per follow-up inquiry |
| Customer satisfaction | Automatic translation, Voice of the Customer | FCR; customer churn | Acquisition cost per customer |
| Agent training | Real-time coaching and agent assist | AHT, FCR, downtime hours; supervisor hours | Downtime cost per hour |
| Employee experience | Real-time coaching, interaction evaluation | Agent attrition | Cost per new agent, including onboarding |
| Quality of service | Summarization, agent assist, virtual agents | Customer churn and sales; live call or chat volume | Acquisition cost per customer; revenue; cost per call |
There is considerable outcome overlap between these areas, and all of them lead back to satisfaction, loyalty and retention.
From AI Opportunity to AI Outcome
Many teams can see the benefits and may even have a business case approved, but are not sure where to start. Establish your priorities as an organization, then look for a vendor partner who takes them on board.
- 01
Understand where you are
Identify the AI capabilities already deployed.
- 02
Identify the opportunity
Determine the biggest CX, EX and efficiency challenges.
- 03
Prioritize the use case
Identify opportunities offering the strongest combination of impact and feasibility.
- 04
Define success
Agree the KPIs and desired business outcomes.
- 05
Pilot, measure and expand
Prove value, optimize and identify the next opportunity.
Four things that make it work, four that sink it
There are many benefits available with AI, but like any new technology it needs to be deployed with careful consideration for the unique aspects of your business.
Pitfalls to avoid
- Underestimating change management. AI requires real changes to workflows, and buy-in from agents depends on communication and training.
- Setting unrealistic expectations. It is always better to exceed low expectations than to disappoint high ones.
- Ignoring data privacy. Ensure the tools comply with data protection regulation, especially around sensitive customer information.
- Overreliance on AI. Humans must remain in the loop to keep the experience personal and to maintain your customers' trust.
Success factors
This page draws on EnghouseAI: A Practical Guide to Applying Artificial Intelligence in Customer Service, an Enghouse Interactive eGuide published September 2026. It is written for digital natives, process improvement experts and customer support professionals looking to improve customer engagement. Third-party figures are attributed to their original publishers throughout.
Enghouse Interactive, a subsidiary of Enghouse Systems Limited (TSX: ENGH), is a global provider of AI-powered contact center software and services, serving thousands of customers worldwide for over 40 years.
The PDF carries the full ROI measurement table, the goal-setting examples behind each use case, and the complete implementation checklist.
Download the PDF →EnghouseAI: A Practical Guide to Applying Artificial Intelligence in Customer Service. © Enghouse Interactive. All information believed correct at time of publication.