Luo et al. (2021): Artificial Intelligence Coaches for Sales Agents: Caveats and Solutions
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What are the three caveats (waarschuwingen) in leveraging AI coaches for sales training?
- Feedback generated by the technology may be too comprehensive for agents to assimilate and learn, since the big data analytics power of AI coaches
- AI coaches lack the “soft” interpersonal skills in communicating the feedback to agents, which is a key advantage of human managers
- The design of AI coaches often focuses more on information generation but less on learning by agents who may differ in learning abilities
Which three research questions are key in the paper of Luo et al. (2021)?
- Which types of sales agents (bottom-, middle-, or top-ranked) benefit the most and the least from AI vs. human coaches?
- What is the underlying mechanism? Does learning from the training feedback account for the impact of AI coaches?
- Can an assemblage of AI and human coach qualities circumvent the caveats and improve the sales performance of distinct types of agents?
What are the three key contributions of the paper by Luo et al. (2021)?
- Nuanced value of AI for sales force management: the AI coach can be deployed to assist agents to learn and improve performance, rather than displace them.
- Distinct challenges are identified that are faced by top and bottom ranked agents when trained by AI
- Designing an assemblage in which smart machines assist human managers proves to be most effective in training salespeople for optimal performance.
AI coaches vs. Human coaches, what is the main advantage of both?
- AI coaches: Hard data computation skills for generating feedback
- Human coaches: Soft interpersonal communication skills
Why is there an inverted U-shaped effect of AI coaches on sales agents?
- Bottom ranked agents may encounter information overload with AI coaches
- Top ranked managers will display the strongest aversion to AI
- AI is most effective for middle ranked agents: More knowledge than bottom ranked and less aversive than top ranked
Which two hypothesis were stated in the article by Luo et al. (2021)?
- H1: The incremental impact of the AI coach over human coaches is in an inverted-U shape: middle-ranked agents improve their sales performance by the largest amount, and both bottom- and top-ranked agents show limited gains.
- H2: Agents’ learning from coaching feedback mediates the inverted U-shaped relationship in H1.
The AI–human coach assemblage allows firms to achieve a three-win scenario. Wich three wins are meant here?
- Sales agents can attain greater learning and income
- Managers can be freed from mundane and repetitive training tasks and spend more resources on tasks that require creativity, judgment, and leadership;
- Companies can enjoy higher sales revenues
Explain the model about the three field experiments wich were conducted in the study by Luo et al. (2021)?
- In experiment 1 the inverted U-shape was found and that this was driven by a learning-based mechanism (info overload and aversion).
- In experiment 2 the feedback amount was restricted and this resulted in an performance improvement
- In experiment 3 they looked at the combi of AI and human coaches. This outperfomed both coaches alone on training effectiveness and performance for both top- and bottom (gained more) ranked agents.
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