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Chatbots Ranked - Forrester’s Top 10 For Enterprise Missing 2 Key Criteria

In a recent ranking of Chatbots for Enterprise Customer Service, Forrester Research detailed the 10 criteria by which they judged solutions in the field of artificial intelligence (AI) based digital care.

Specifically they include machine learning, intent engines, dialog management, natural language understanding on the AI side, alongside reporting & analytics, security & administration, and multichannel administration, as well as roadmapping, vision, and revenue considerations from a product perspective.

However, taking those criteria into consideration there are two glaring omissions: expertise and focus. Any AI-based digital care solution worth its salt is only as good as its corpus – sticking to only Forrester’s criteria might send you down the path of bad bot if you ignore these two pillars of AI at large. While the previous 10 criteria are important, let me take a moment to explain why expertise and focus should be on the list.


Industry domain expertise is an essential pillar for any AI-based digital care service. As AI increasingly enters the public lexicon, industries at large continue to misuse the term or at the very least misconstrue what AI is and in turn is capable of. There is no digital multi-tool which you can throw at any industry in the hopes it can solve any customer dilemmas. The inquiries and questions asked in wireless industry digital care vary greatly from those asked of florists, pizza parlors, and other simpler, small-scale consumer businesses.

Not only is an off-the-shelf solution flawed due to the great variance one finds in customer based interactions, but even within a single industry there are so many differences between the needs of separate companies dealing with the same product. The language (and local variances) used to describe problems and products, the variations of product uses by region, and a myriad of other use cases prove why industry knowledge is vital to implementing an AI-based digital care strategy efficiently, and at scale.

An off-the-shelf or general knowledge AI solution in digital care can cost your business both profit and time. One either has to wait for the platform and their team to learn the industry in which they are deployed, or choose a platform where they have to put in the development work to ensure it aligns with that industry, and continue that work as the it evolves. Choosing a platform that aligns with your industry ensures they are evolving with it and the business cases that come alongside it.

In wireless specifically, we are seeing continued growth in smartphone and wearable usage, and the evolution of new technology like 5G networks and autonomous vehicles – both of which will grow in tandem with carriers across the globe.

“Customers should be delighted when they seek support, not frustrated,” said Ian Collins, CEO and co-founder of CrowdCare. “In today’s wireless marketplace, having a fast, efficient, AI-based digital care solution can be a competitive advantage for carriers who choose and value those that concentrate on their specific industry problem set.”


The other criteria goes hand in hand with expertise. Just as I said above, where AI has entered the public lexicon, there is a temptation to shoehorn a factitious AI solution into every product.

The opposite can also be true: essential AI solutions which concentrate on specific use cases and industries can succumb to the temptation of adding features which don’t align with their industry and can in turn add development time and frustrate users.

Take another example from the wireless industry. Customers interact with support consistently around billing inquiries – would it not make sense for digital care to ask customers if they want to be reminded about their bill due dates, data caps, or other aspects of their plan? Not necessarily. Wireless companies already have systems set up for these alerts, and some users suffer from alert fatigue as it is – they may be frustrated at the mere thought of adding another notification to their smart device.

It’s not that asking users about these options is necessarily a bad idea, but they (and larger companies as a whole) are better served by their AI-based digital care solutions solving the issues they were created for, rather than kitschy add-ons made to impress in the short-term.

One can see how these two added criteria are essential for implementing a well rounded, AI-based digital care solution. Ignoring them when evaluating your platform options could prove perilous in the long-term.

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