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Rekindling the Relationship Between AI Vendors and Customers When the Honeymoon Phase Ends

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Many enterprise AI users and their vendors find themselves in unhappy relationships these days. 47% of telecommunications companies responding to a recent Fierce Network survey, sponsored by Zayo, reported that they’re only “somewhat satisfied” with their AI vendors, and 10% reported being somewhat to extremely dissatisfied. 

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As a telecom company striving to enhance network operations, tackle challenges, and boost customer experience with technology, Zayo partners with multiple AI vendors. As an AI customer ourselves, we understand the challenges and growing pains that come along with experimenting with new tech. Although we’ve benefitted from parts inventory, incident resolution, churn analysis, and employee productivity optimizations thanks in part to AI and machine learning (ML) tools, the path to achieving these successes was marked by substantial trial and error. 

We believe that there’s a huge opportunity here, especially if AI vendors and customers – like Zayo – can agree to work together through these growing pains to solve problems collectively. 

Although telecommunications customers were the focus of our research, the less-than-satisfactory relationships between Ai vendors and their customers isn’t an industry-specific problem. AI users across industries who eagerly hopped onto the hype train in fear they’d otherwise be left behind are now wondering where they went wrong. Their AI vendor had love-bombed them – they’d promised to give them the life they’d always dreamed of if they partnered up. Their customers believed them, rushing into the relationship, ignoring the red flags, and setting their new partner to a standard they couldn’t uphold. 

“The cycle of hype is just catching up to reality,” Nikos Katinakis, Chief Technology Officer at Zayo shares, “As AI emerged as a viable set of tools, everyone jumped in thinking that it would be ‘easy.’ Then, reality set in when companies realized that to make AI work and realize actual benefits, they would have to invest a ton of time, effort, and money to prepare for it.” 

The honeymoon phase is now over – AI isn’t solving all of these customers’ issues or making their lives easier, as their vendors promised. Instead, the metaphorical bills are piling up, dirty dishes are stacked in the sink, and the bed is unmade – but who’s to blame? Can this relationship be repaired? 

Seeing Beyond Rose-Tinted Glasses

Ultimately, both parties are to blame for the relationship turning sour. Neither party has communicated effectively and a lack of trust between the two threatens to end the relationship. And they’re not alone – public trust in AI tools is falling

The vendors in this scenario are to blame for selling what they had to offer as a one-stop solution, wooing their customers with love letters at the start of the relationship, and then being unable to deliver on their promises as the relationship progressed. 

But don’t be mistaken – this relationship is a two-way street, and the customer needs to be held accountable, as well. Customers brought their own baggage into the relationship. 

For one, AI customers brought messy data. Structuring, cleaning, and organizing years of poor-quality data takes time and resources, and with no clear immediate ROI, many companies just put it off. But the success of AI hinges on clean, accurate, and correctly formatted data. In other words: customers often point fingers at their vendors for problems they caused in the first place. Ensuring data integrity is a critical first step in laying the foundation for AI – and one that’s often ignored.  

It’s not just bad data and broken promises causing rifts in the relationship. In many cases, customers aren’t prepared for AI when they adopt AI tools. Responses from telecommunications companies in the Fierce Network report reveal that a few key challenges stand in the way of fully, and beneficially adopting AI. The most commonly cited include integration with existing systems, a lack of skilled professionals in AI, a high initial investment cost, unclear ROI, and data privacy and security concerns. 

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If AI vendors are to overcome the various challenges standing in the way of a successful relationship with their customers, they need to be able to address these challenges in a meaningful way. Similarly, customers must consider the tools that are most compatible with their organization’s systems, skill sets, budgets, and security standards. Coming into the relationship with clear and realistic expectations sets a positive foundation for a lasting alliance. 

Time for Couples Therapy or Headed for a Breakup?

So, what can be done? With customers skeptical of their vendors and vendors promising something they can’t deliver, both sides need to work toward a resolution – or risk breaking up for good. 

Companies looking to invest in AI need to first clean up their data. Then, set realistic expectations for what can be done with AI and determine what you want out of the relationship with your AI vendor. AI isn’t a magic button you can press to solve all of your problems. However, if you come to the table with clean, correct data, a plan of action, and an understanding of how the AI tool will integrate with your world, you can make AI work for you. 

AI vendors need to make it clear what they’re actually selling and set realistic expectations for what they can deliver. The main negative consequence of AI hype is that it’s caused a lot of skepticism – and no relationship can work without trust. 

“The best approach is setting realistic expectations for what the technology will actually do and the amount of pre-work that will be required,” Katinakis advises. Communicate clearly to customers and prospects that your solution can’t deliver everything and ask them clearly what their goals are for your partnership so you can meet halfway. 

However, like in human relationships, sometimes things just don’t work out. Here’s when to know it’s time to break up with your AI vendor: 

  • The solution isn’t meeting expectations. Ideally, you’ve set goals and KPIs for your AI solution. You can’t expect your AI solution to do everything for you, but you should identify a few key areas you expect to improve with your tool. If it isn’t meeting these key expectations, it’s time to say goodbye. 
  • It doesn’t integrate well with your existing systems. You want a vendor that can play nicely with your friends. If they just don’t mesh and aren’t able to work it out, your vendor may be causing more problems than they’re worth. 
  • You’re concerned about security. Concerns about the security of your data with AI solutions are a serious red flag. Don’t stay in the relationship long enough to suffer the consequences of data breaches or a lack of security on the part of your vendor. 
  • Your vendor provides poor support and communication. Good communication is the key to every good relationship. Don’t stick around with an AI vendor that leaves you on read. 
  • The costs outweigh the benefits. Maybe your AI tool isn’t providing the ROI you need, or they spring unexpected fees on you. Opt for a vendor that is straightforward and honest about the costs of your relationship. 
  • The solution performs poorly or is outdated. Set your vendor to a high standard. If they consistently fail to deliver results or upgrade their technology to the latest advancements, don’t settle. 
  • Your vendor is inflexible. Compromise is key. If your AI vendor’s solutions don’t adapt to your needs, it can hinder your ability to leverage AI effectively, leading to poorer results. 

Want to dig deeper? Read the Fierce Network report, sponsored by Zayo, for more insight into the AI and automation landscape in the telecommunications industry.

Want to Dig Deeper?

Read the Fierce Network report, sponsored by Zayo, for more insight into the AI and automation landscape in the telecommunications industry.