Like all emerging technologies, generative AI is following the trajectory of the Gartner Hype Cycle. Almost three years on from OpenAI’s launch of ChatGPT, generative AI has climbed rapidly through the ‘peak of inflated expectations’, a stage that is invariably followed by a descent into the ‘trough of disillusionment’.
With the global AI market expected to reach £3.6 trillion ($4.8 trillion) by 2033, leaders urgently need to understand what separates successful pilots from costly dead ends, as generative AI looks to climb the ‘slope of enlightenment’ and into the ‘plateau of productivity’. One of the clearest roadmaps towards that plateau is found in Customer Experience (CX), where AI is already delivering measurable return on investment (ROI).
Why are AI Projects Failing?
A recent report by MIT found that 95% of generative AI pilots at businesses are failing, creating a stark divide between the 5% of high-value projects that deliver ROI and the rest, which the researchers are terming the “GenAI Divide.” The same MIT study found that while 60% of organisations explored enterprise-grade AI tools, only 20% ever reached the pilot stage, with just 5% subsequently entering production. This report amplifies Gartner’s earlier prediction that 30% of generative AI initiatives will be abandoned after proof-of-concept by the end of this year.
According to IMB research, only 25% of AI projects have delivered their expected ROI. Against this unpromising background, CX stands out as the early outlier – a place where results are tangible and repeatable. Contact centres are uniquely positioned to generate demonstrable ROI because they already operate with optimised people, processes, and data following decades of progressive consolidation and refinement. This makes CX perhaps the first significant business function to which organisations can apply AI in a meaningful and measurable way.
Where AI Delivers Real ROI
A recent study found that 77% of organisations saw either “significant” or “some” cost savings from their AI implementations in CX. However, the benefits of these AI initiatives are extending much further to encompass employee productivity and well-being, as well as strengthening customer loyalty.
So why are AI projects failing, and what are three things organisations can learn from the successful application of generative AI in CX, when it comes to demonstrating their own ROI?
1. Tackling Hallucinations
Generative AI hallucinations, incorrect or fabricated outputs, can quickly undermine trust. Deloitte reports that 77% of businesses are concerned about hallucinations, especially when these impact cybersecurity. Current hallucination rates range between 17 and 45%, which could create serious risks for highly regulated industries such as legal, finance, or healthcare.
To combat the risk of hallucinations, organisations should use Retrieval Augmented Generation (RAG), which validates generative outputs against approved data sources. In contact centres, providing links to original sources furthermore allows employees to check context instantly, cutting the risk of errors while building trust.
2. Shutting Down Shadow AI
The MIT report also highlights the spread of “Shadow AI,” where employees use unsanctioned AI tools without IT oversight. Workers gravitate towards these tools because they integrate into workflows and are easily customisable to the individual’s specific needs, even if they bypass governance, which makes it harder to measure ROI as well as compromising data security and consumer safety. Between 2023 and 2024, the adoption of generative AI among employees jumped from 74% to 96%, with the most popular applications cited as ChatGPT, Grammarly, and Microsoft Copilot. Use of Shadow AI demonstrates the underlying demand, but also the governance challenge: businesses need visibility both to measure ROI and manage risk.
In CX, shadow AI is less of a concern when enterprise-grade tools are embedded directly into the systems employees already use. For example, AI can act as a ‘second pair of ears’ during interactions, automatically surfacing and compacting appropriate knowledge articles for contact centre workers from pre-approved sources. This delivers the dual benefit of enabling smoother, more informed dialogue and cutting staff training time. Here, the strengths of human and machine complement each other: instead of forcing people to operate like machines, AI can take over the “machine work” such as repetitive tasks, allowing employees to excel at decision-making and trust-building.
3. Applying AI Where It Matters
The MIT study found that some of the strongest ROI from generative AI comes from back-office automation, an area closely tied to CX. By making front-office note-taking and data entry faster and more consistent, AI enables contact centre workers’ back-office colleagues, such as the assessors and underwriters who need to approve customer transactions, to work more efficiently and smoothly, boosting significantly the productivity of these high-value professionals.
Returning to the front-office contact centre, CX leaders generally know when not to automate. When an interaction is highly complex, emotionally charged, or time-critical, it is better to be given to a human who can solve problems and can offer real empathy and understanding, leaving AI to process the straightforward, low-sensitivity queries. The right balance maximises both ROI and customer satisfaction.
ROI for Generative AI
To fully embrace AI, organisations need a vendor-agnostic partner that understands the aims of implementation and has a holistic view of the latest technology and vendors, including horizon scanning for emerging players. An AI partner will be able to offer fully developed solutions that can make an instant impact, based on their ROI objectives. The right AI partner can furthermore deliver AI technology that fits an organisation’s requirements while keeping the end-user up to date with the latest innovations on an ongoing basis.
Closing the GenAI Divide
Ultimately, the “GenAI divide” is not just about technology, but also about execution, and CX offers a clear roadmap for organisations looking to get the most ROI from their AI investments. This principle of AI ‘helping humans to help humans’ should guide deployments across all parts of the organisation, not just CX. By focusing on collaborating with trusted AI vendors, businesses can address the risks of AI through deploying enterprise-grade tools that automate tasks appropriately and maximise ROI.
