AI Maturity Assessment: Where Are You on Your AI Journey?

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Understanding where you are today can help shape your next AI step

Research shows that while AI adoption in contact centres is accelerating, many organisations are not yet seeing lasting business impacts. Often this is because technology and tools are being deployed without a clear understanding of what problem they solve or the value they bring. Built on our experience, our six-stage AI maturity ladder gives CX, IT, analytics, and digital leaders a practical way to assess where they are today and identify the next step on their AI journey.

Moving beyond the hype to solve real customer problems

The vast majority of contact centres have begun their AI deployment journey in some form. For example, nearly all respondents to the 2026 Australian Contact Centre Industry Best Practice Report said they were using AI. 98% expect AI self-service to increase, while 71% see Voice AI growing over the next two years. ^[1].

Some organisations are already seeing benefits from more structured AI adoption. Deloitte Digital's 2026 Global Contact Center Survey ^[2] found that AI-mature contact centres reported 85% higher profitability, and that 69% were more likely to rate the customer experience they offered as good or excellent.

However, given the range of AI tools and use cases, from virtual agents and process automation through to autonomous AI agents, choosing what to deploy and when can be a challenge. Many organisations have started using AI in pilot areas or have access to AI capabilities within their existing contact centre solutions, yet are not seeing measurable business value from their AI adoption strategy.

Assessing AI maturity in the right way

To understand how to move forward on their journey, contact centre leaders need to see where they currently sit. Traditional AI readiness assessment frameworks aim to provide this guide but are often focused on the quantity quantity of AI deployed in the contact centre. By focusing instead on the quality – the customer problems that AI is solving – organisations gain a more realistic view of the next steps they need to take.

Our six-stage AI maturity ladder provides this framework, focusing less on the volume of technology deployed and more on business results. This helps prioritise the next steps, investments, and actions needed to move up the ladder and meet broader customer experience objectives through your AI implementation roadmap.

The six stages of contact centre AI maturity

As with all contact centre initiatives, AI projects should focus on solving concrete problems that affect customer experience, operational efficiency, and employee productivity. Understanding current issues and how critical they are enables AI investments to be targeted in the right areas, driving progress and results.

Based on our experience, contact centres typically sit at one of six stages of AI maturity. Each stage reflects not just how much AI is in use, but how clearly it is connected to business goals, customer outcomes, and day-to-day contact centre operations.

AI Maturity Ladder

Stage 1: Exploring

At this stage, AI is a topic for discussion rather than part of a clear plan or wider strategy. Any AI activity is usually driven by new capabilities added to existing tools, meaning AI is being used by default rather than by choice. Adoption is shaped by what current vendors offer or an overall objective of deploying AI, instead of starting with a clear understanding of business needs. While vendor demos can be useful, they may not reflect your specific contact centre challenges or requirements.

Key question to ask to see if you’ve reached this stage: Have you looked at your own contact centre data to work out where AI can help, rather than vendor marketing materials?

How to move up to the next stage: Flip the process and start by gathering evidence by analysing your operations and talking to staff at all levels. Don’t start with the tool. Identify the problems first, rather than deploying AI tools and then searching for a problem that it can solve.

Stage 2: First pilots

Early AI pilots are taking place, often in low-risk areas or where vendors claim they can deliver results. There is no overall strategy in place, but the contact centre can report to senior leadership that it is using AI in some applications. Examples include a chatbot trial, AI call summarisation, or a proof of concept for call monitoring. Metrics used to define success can be vague and disconnected from overall business objectives. While benefits may be seen, they often do not extend beyond the pilot area and can even create wider issues. For example, a chatbot may triage some interactions successfully but reduce customer trust or satisfaction if it is poorly designed or does not have access to the right knowledge.

Key question to ask to see if you’ve reached this stage: Is anything AI-related handling real customer traffic today?

How to move up to the next stage: Pick the next AI pilot from what your volume and cost data tell you, not from what looked best in a demo.

Stage 3: Point solutions

The trials carried out in stage 2 have now matured and have become part of the contact centre’s ongoing operations. However, they are still deployed in narrow silos, often led by different teams or departments. Applications could be a bot for FAQs, speech analytics in a single team or AI quality management in a specific area. There are no connections between different tools or sharing of outputs or data. Solutions don’t deliver to their full potential as they are constrained by team or application boundaries.

Key question to ask to see if you’ve reached this stage: Can you state what outcome any of the AI use cases have delivered, in numbers your finance team would accept?

How to move up to the next stage: Measure one use case properly, end-to-end, so the result survives a finance conversation and sets the standard for the rest.

Stage 4: Connected use cases

At this stage, results from at least one AI tool are shared beyond the immediate team or department, helping to drive wider operational change and a clearer understanding of AI’s positive impact. For example, analysis of virtual agent transcripts could be used to update and optimise human agent scripts. AI-driven quality reports might feed into coaching conversations, while analysis of questions asked through chatbots and virtual agents could improve the knowledge base. However, integration remains limited and connections are often ad hoc. No one is responsible for making sure AI outputs are shared and acted on across teams.

Key question to ask to see if you’ve reached this stage: Are the results from AI tools being used by other teams to improve their performance?

How to move up to the next stage: Give one person the job of connecting outputs across teams, with the authority to change what those teams do.

Stage 5: Managed AI

At this stage, AI is integrated into the fabric of overall contact centre operations, with clear ownership, metrics, and reporting. Rather than relying on ad hoc connections, AI projects have named owners, clear governance, and business-focused objectives. Progress and outputs are reviewed regularly in a structured, joined-up way and outcomes are measured and communicated. Essentially, AI is now part of how the contact centre is run, and is managed, evaluated, and measured through existing operational metrics.

Key question to ask to see if you’ve reached this stage: If the person who leads your AI work left tomorrow, would it keep running to the same standard?

How to move up to the next stage: Document ownership, reporting line and a success measure for every use case, then review them on your existing operating cadence.

Stage 6: Embedded AI

Managed AI delivers major benefits. However, it is essentially reactive. It improves existing operations and processes, but it does not drive innovation or transform the contact centre’s operating model. Moving to the embedded AI stage enables proactive change. Alongside the benefits of managed AI, outputs are used to shape how decisions are made and how operations are run. Joined-up AI evidence from multiple systems shapes conversations around key areas such as resourcing, routing, product, and process decisions. AI is part of the operating model, rather than a separate stream, and is accepted and understood by all. There is no distinction between an AI decision and a business decision.

Key question to ask to see if you’ve reached this stage: In the last quarter, did AI output cause you to take a decision you would otherwise have taken differently?

How to stay at this level: Continue to put AI outputs into the meetings where decisions already get made, rather than into a separate AI review.

Benefiting from the AI maturity assessment ladder

Every organisation is different, meaning there is no “one size fits all” solution to achieving AI readiness. Moving forward is about assessing:

Where the contact centre sits now
Where there are gaps
Priorities moving forward

Many organisations are achieving some benefits from AI and automation, but are not yet realising their full value. However, assessing yourself to be at stage 1 or stage 2 is not a mark of failure. Instead, it shows what needs to be done, and in what order, to realise the benefits of AI maturity. By taking a more realistic approach, organisations can ensure that AI delivers consistent, long-term benefits.

It is also vital to understand that AI adoption is not a linear process. As with any ladder, contact centres can move down as well as up. Pilots can be cancelled, project sponsors can move on, and progress in one area may not be replicated across the wider operation. Any setbacks need to be understood and learned from as part of the ongoing journey.

Achieving AI maturity on your journey

AI has the potential to deliver benefits to every contact centre. However, while most organisations have set out on their AI maturity journey, the majority are still in the early stages. Moving forward requires a focus on more than simply implementing technology. Instead, take a strategic approach that understands business and customer pain points, then uses them to identify where AI adds real value. The six-stage AI maturity ladder helps focus efforts, ask the right questions, and drive progress towards embedding AI across the contact centre.

How Enghouse Interactive supports 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.

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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 practises.