INDUSTRY INSIGHT
THE KEY TO THIS SUCCESS LIES IN ENSURING THAT AI RECOMMENDATIONS ARE ROOTED IN HUMAN EXPERTISE, SALES PSYCHOLOGY AND REAL- WORLD EXPERIENCE.
It’ s not all bad news, however. Gartner also believes Agentic AI will resolve 80 % of common customer service queries by 2029, leading to a 30 % reduction in operational costs. Other commentators simply reject the MIT findings. Who should we believe?
Correction of course – not a crash
While we may see a market correction, AI adoption is certain to continue after businesses have learned how to approach it in a more systematic way. The real skill in designing AI applications lies in identifying opportunities to serve customers more effectively while also generating commercial value.
With this in mind, there seems no good reason why firms have so readily embraced AI for process efficiency and customer support, yet hesitate to apply it in areas like sales, where the outcomes are clear and immediately measurable.
Unlocking AI’ s true potential in sales
Sales is one area where AI agents, guided by human insight, can deliver a bottom line impact that captures the attention of even the CFO. In the telecommunications sector, for instance, AIled sales journeys have already driven significant uplifts in conversion, increased attachment rates and boosted average order values – all while enhancing customer confidence and satisfaction.
The key to this success lies in ensuring that AI recommendations are rooted in human expertise, sales psychology and real-world experience. To achieve immediate success, businesses deploying AI for sales should adopt a robust, proven and accessible framework from the start.
Ben Gilbert, VP, 15gifts
Currently, most companies are leveraging AI for efficiency, such as automating workflows or streamlining customer support. However, these benefits often take years to deliver tangible returns and are difficult to measure beyond time savings.
Consequently, AI projects with unclear gains or delayed ROI will be the first to be cut. Without definite value, these initiatives risk becoming costly experiments rather than profitable investments. As expenses mount and results lag, businesses will inevitably abandon projects that don’ t directly contribute to growth or profitability.
Bounded vs unbounded problems
Bounded problems are highly defined and predictable, representing the areas where AI has already proven its effectiveness. Examples include automated reporting, routine customer service and churn prediction.
Unbounded problems are more complex, involving fluid inputs and subjective human variables like nuanced decision-making and personal interaction. It is precisely in these areas that AI holds the most transformative potential.
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