In the CX Network Artificial intelligence in customer experience report, we asked 102 CX experts for their experiences with artificial intelligence and machine learning in CX.
When we asked respondents what their biggest challengers are, linking CX initiatives to ROI came out on top, with 36 percent.
Linking CX initiative to ROI appears to be a widespread problem in the CX space. Competing priorities and ROI issues were also the top challenges in the 2018 and 2019 Global State of Customer Experience reports. Linking CX initiatives to ROI was closely followed by building an internal culture (35 percent) and competing priorities (31 per cent).
The biggest challenge in implementing AI/ML
To further complicate things, AI will be pitted against other CX and business priorities. As resources are limited, not every project can receive funding in a timely manner, so it is important that CX practitioners can really illustrate the business benefits AI CX investments can bring in a compelling way.
Core difficulties in calculating ROI from CX investments include data silos and managing data. It is proving difficult for organizations to tear way from a business first and product focus – which offers shorter term ROI and faster results for senior managers.
On the other hand, CX initiatives can be difficult to rationalize based on predictions of increased revenue as a result of an improve brand customer experience. It is vital that industry providers can provide solutions to help CX teams evidence their ROI – as flimsy non- measurable plans can quickly get lost in the short term ROI environment – and potentially stall customer experience initiatives.
Read: Oracle On How To Start Your Ai Journey With A Winning Formula
CX practitioners will need to persuade budget stakeholders to be more patient with the lead times on the returns from the AI in CX projects. Jon Stanesby, director, product strategy A.I. Applications at Oracle on linking initiatives to ROI “Unfortunately, innovation of any kind cannot be expected to generate returns right away so if you are embarking on something brand new ensure you have the buy-in from leadership. This includes the flexibility to fail a little along the way.
“However, more established uptake of machine learning, for example more mature areas or anything off the shelf that you purchase should absolutely give returns. Measurement of that will depend on the case, but it’s worth thinking about ways to measure the effort saving nature of most ML technologies. Most will not only improve your revenues, or retention but also do so with reduced effort. So the “R” should be a combination of Revenue and Reduced effort.”
Be sure to check out the full report to hear more from Stanesby, and for more insight on top trends, challenges and solutions including the removal of data silos and building a customer-first culture.
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