Overcoming the AI Adoption Chasm

As we all know, AI will revolutionise customer experience (CX) - yet research shows under 5% of CX-focused AI initiatives actually reach any sort of significant scale.

But why the gap between the hype and reality?  

Our ebook – entitled ‘Revolutionising the Customer Experience’ - identifies key barriers organisations face when implementing AI across the customer journey. 

The report details how the initial ease of testing AI solutions leads teams to underestimate subsequent scaling challenges. Once beyond basic prototypes, cross-business coordination becomes vital but difficult while siloed systems and disconnected, poor data also hamper efforts to feed increasingly large AI models. 

Four cultural and operational "pain points" stand out as organisations attempt to progress pilots into full deployments: 

• Getting started is all too easy: The availability of ready-made large language models allows teams to rapidly build prototypes. However, without proper planning, they struggle to transition these to enterprise-grade solutions. 

• Scaling-up is complex: Moving from departmental to company-wide deployments requires coordination across functions. This relies on breaking down organisational silos - already a perennial CX challenge. 

• Achieving the right human/AI balance: Despite the hype, AI generally augments rather than replaces human capabilities. Companies must strike the optimal equilibrium across activities. This demands open discussion to address valid workforce concerns. 

• Lack of operational maturity: Most AI platforms remain fast-evolving. It is the pace of this evolution which is challenging for organisations looking to understand the capabilities of the technology. Managing and maintaining these complex solutions long-term requires specific skills and resources. 

To overcome these adoption barriers, our AI experts advocate a structured approach across four dimensions: 

• Cultural - Instill confidence by communicating a clear AI vision from the outset. Upskill workforces via training initiatives focused on data-driven competencies. 

• Operational - Begin with tightly scoped "basics first" pilots to test processes before expanding reach. Build accountability by empowering project teams. 

• Financial - AI demands new ways of investment thinking. Compare it to the internet revolution - early adopters stand to gain tremendous advantage. 

• Technology - Leverage cloud infrastructure for AI's vast compute requirements. Audit existing systems and data flows to ensure readiness. Plan post-implementation support. 

With careful change management grounded in realistic deployment roadmaps, AI can drive immense customer experience improvements – but equally, getting it wrong can be costly leading to a waste or even total loss of investment. 

Only by acknowledging AI’s potential and its limitations in equal measure can organisations negotiate the gap from ambitious, theoretical promise to real, daily and widespread impact.