{"id":587891,"date":"2025-08-11T05:14:45","date_gmt":"2025-08-11T12:14:45","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/why-your-ai-initiative-could-fail-and-how-to-fix-it"},"modified":"2025-08-11T05:27:54","modified_gmt":"2025-08-11T12:27:54","slug":"why-your-ai-initiative-could-fail-and-how-to-fix-it","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-gb\/blog\/post\/why-your-ai-initiative-could-fail-and-how-to-fix-it","title":{"rendered":"Why Your AI Initiative Could Fail \u2013 And How to Fix It"},"content":{"rendered":"

[vc_row][vc_column][vc_column_text css=”” el_class=”d-none”]The artificial intelligence (AI) revolution is fundamentally altering the nature of enterprise software. And nowhere is this truer than in the customer service<\/a> space.<\/span>\u00a0<\/span><\/p>\n

Traditionally, the responsibility for maintaining enterprise software systems rested largely with the vendor. However, with the rise of AI-powered software, this responsibility has shifted from being primarily on the vendor to being shared between the vendor and the customer. Consequently, the customer now has to undertake significantly more work than they would have previously done.<\/span>[\/vc_column_text][vc_column_text css=””]This blog was written by Adrian Swinscoe, Customer Experience Advisor, Author, Speaker, Workshop Leader and Aspirant Punk at Punk CX.<\/strong><\/em><\/p>\n

The artificial intelligence (AI) revolution is fundamentally altering the nature of enterprise software. And nowhere is this truer than in the customer service<\/a> space.<\/span>\u00a0<\/span><\/p>\n

Traditionally, the responsibility for maintaining enterprise software systems rested largely with the vendor. However, with the rise of AI-powered software, this responsibility has shifted from being primarily on the vendor to being shared between the vendor and the customer. Consequently, the customer now has to undertake significantly more work than they would have previously done.<\/span><\/p>\n

In addition, maintenance now encompasses not only design, testing, monitoring, management and tweaking, but it also includes all aspects of data sourcing and cleaning, as well as content generation, which are the lifeblood of any AI system. This shift also necessitates additional resources, and the investment required to support these extra efforts.<\/span><\/p>\n

These changing dynamics are causing issues within enterprises regarding their ability to prove return on investment (ROI) and to scale AI solutions<\/a> beyond the pilot or proof-of-concept stage. This is supported by recent research from both Accenture and IBM, which indicates that enterprises are currently experiencing mixed results with their AI initiatives and investments. <\/span><\/p>\n

For example,<\/span> Accenture examined over 2,000 generative AI projects<\/span><\/a> and consulted more than 3,000 C-level executives, finding that only 36% stated they have scaled generative AI solutions, while only 13% of executives report having created \u201csignificant enterprise-level value.\u201d Meanwhile,<\/span> IBM surveyed more than 2,000 CEOs globally<\/span><\/a> and discovered that only 16% have managed to scale their AI initiatives across the enterprise. And only 25% report their AI initiatives have delivered the expected ROI over the past few years.<\/span><\/p>\n

While this data may highlight some of the broad challenges that companies face when implementing AI systems in customer service, it doesn\u2019t address some of the specific challenges that customer service and CX leaders face when implementing AI.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][vc_section][vc_row][vc_column][vc_column_text css=””]Let\u2019s look at four stories from different organisations that illustrate some challenges CX leaders have encountered when incorporating AI into their customer service and support departments, what we can learn from each of them, and what it takes to<\/span> successfully harness the power of AI and your people<\/span><\/a>.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

AI Resistance in the Financial Services Industry<\/span><\/h2>\n

The first story comes from a financial services firm that implemented an AI-powered chatbot designed to help customers self-serve. Like many other AI-powered chatbots, this one included a human-in-the-loop (HITL) or human oversight capability to help train the model and monitor the quality of responses provided to customers. This would help to ensure they were on brand and aligned with the company\u2019s policies.<\/span><\/p>\n

It would also allow agents to step in when the AI encountered a query it couldn\u2019t handle. However, the project failed as the existing customer service representatives refused to engage with it, feeling threatened by the new technology and fearing that they were effectively training their replacements.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

Fear That AI Training Will Shift Agent Time and Focus\u00a0<\/span><\/h2>\n

The second story involves a customer service operations director of a retail brand who was facing pressure from the leadership and board of directors to implement AI into their day-to-day operations to help drive improvements in service outcomes and productivity. However, doing so would have required them to redeploy some agent resources away from the frontline to assist in designing, training, testing, managing and monitoring these new tools.<\/span><\/p>\n

Consequently, this would have jeopardised their ability to meet their existing KPIs<\/a> and service-level agreements (SLAs), putting not only their own performance at risk but also the rewards that agents would have received if they had met their targets. As a result, the customer service operations director decided to do nothing until they received the budget and additional resources required to undertake this new work.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

Clear and Inconsistent Documentation Upends AI Value<\/span><\/h2>\n

The third story comes from a customer service department in a business services firm that wanted to deploy agent-assist technology to help improve the productivity and efficiency of their agents. They procured some technology and proceeded to upload a slew of policy documents, training materials and FAQs to train the AI model<\/a> and to make sure any suggestions were on brand.<\/span><\/p>\n

However, following the implementation and training period, they discovered that the output from the agent-assist technology was largely ineffective because their documentation wasn\u2019t clear or consistent. This was an issue their agents faced daily, but they had managed to provide effective and appropriate responses based on their understanding of the business.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

AI Empowers a Greater Focus on Customers<\/span><\/h2>\n

The fourth story comes from a customer service department at an eCommerce firm that successfully implemented an AI-powered chatbot<\/a> to enable customers to self-serve, along with agent-assist technology that included an automated messaging capability. This allowed them, using AI and automation<\/a>, to respond automatically to a large volume of simple and straightforward queries that took up the majority of their team\u2019s time.<\/span><\/p>\n

As a result of implementing this technology, they were able to turn the phones on, something they hadn\u2019t previously managed to do because of capacity and resource constraints. This enabled them to spend more time talking to customers, solving tricky problems, building connections and adding value.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

1. Bring Your Agents With You<\/span><\/h3>\n

You must bring your agents with you \u2013 and have a story to tell them about what you want to build, why you want to build it, what will be your agents\u2019 role, how that will change and how you will help them adjust. If not, then there is a distinct possibility that your initiative will fail or, at the very least, won\u2019t meet its objectives.<\/span>\u00a0<\/span><\/p>\n

This is supported by<\/span> research from McKinsey<\/span><\/a> that shows that over 70% of all transformation projects fail, largely due to under-investment in three crucial areas: communication, behavior and training. That might not be a surprise to many people who have their own personal experience of going through a transformation project. However, it seems like it is a lesson that is often worth re-learning.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

2. Align Metrics and Resource Your Implementations<\/span><\/h3>\n

It bears repeating that, with the arrival of AI-powered software, the nature of enterprise software has changed. If we are to successfully harness AI\u2019s potential, we must ensure that we resource it appropriately and align our internal targets, metrics and incentives to give ourselves the best chance of success.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

3. Ensure You Have Quality Data<\/span><\/h3>\n

In computer engineering, there\u2019s an adage that says: \u201cGarbage In, Garbage Out.\u201d This applies not only to your quantitative data but also to the qualitative data that you use to train your chosen AI solution.<\/span><\/p>\n

As the third story above illustrates, just because you have written policies, procedures and FAQs doesn\u2019t mean they\u2019re effective. Before you blindly assume that your official company documents are worthwhile, ensure you work hard to make certain your data \u2014 particularly any documentation you will be relying on \u2014 is clear and consistent. Remember: Your AI solution will only ever be as good as the data it\u2019s trained on.<\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

4. Be Clear About the Choices You\u2019re Making<\/span><\/h3>\n

Many brands are choosing to implement AI with the aim of improving efficiency and productivity, as well as reducing operating costs. While it may not always be evident from the outset, this often results in a reduction of headcount due to the efficiency and productivity gains achieved through leveraging AI.<\/span><\/p>\n

However, there are others, like the company featured in the fourth story, that are making different choices with the increased agent capacity that they now have available. They\u2019re choosing to leverage the vast amount of agent intelligence that has now been freed up to not only solve complex customer problems but also to invest time in talking to and building better relationships with their customers. They\u2019re secure in the belief that if they do so, it will yield positive returns for the business in terms of increased loyalty, revenue and profitability. <\/span>[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section][vc_section][vc_row][vc_column][vc_column_text css=””]<\/p>\n

Conclusion<\/span><\/h2>\n

Implementation of AI-powered software doesn\u2019t happen in a vacuum. Be clear about the choices you are making and why.<\/span><\/p>\n

Despite the current level of hype surrounding AI, it\u2019s still early days in the AI revolution. Therefore, if you\u2019re looking to implement AI to enhance the customer service and experience that you want to provide, you should consider these lessons if you want to give your efforts the best chance of success.<\/span><\/p>\n

To learn more,\u00a0check out Adrian\u2019s blog,<\/a>\u00a0download a\u00a0podcast<\/a>\u00a0episode or\u00a0email him<\/a>.[\/vc_column_text][\/vc_column][\/vc_row][\/vc_section]<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

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