In the realm of leveraging artificial intelligence to revolutionize sales operations, the dichotomy between augmentation and automation plays a pivotal role in determining the success and efficacy of such endeavors. Let’s delve into the contrasting narratives of two fictitious companies, Company A and Company B, each embarking on a quest to enhance their sales processes through the power of AI technology.
Case Study for AI in Sales: Same ideas – different approaches
Company A made hundreds of sales calls every week. During these calls, the sales team pitch customers about different products or services and get to know them better. Through a meticulous fusion of human expertise and AI assistance, they optimized their sales calls by transcribing conversations and seamlessly updating CRM records with pertinent data (size, vendors, account typology …). Sales team could then review all suggestions and make any relevant adjustments. With just one click, the information would be added to the CRM.
On the flip side, Company B opted for an automation-centric approach, adopting an AI-powered CRM plugin that crawls through all CRM records, identifies missing fields, and attempts to automatically fill in those fields based on a history of all available transcripts from all recent sales calls.
Pause…
Which approach do you think was more successful?
Here’s what happened:
After launching a prototype on a test user group, Company A realized that the amount of sales fields in the CRM would go up by about 40% immediately, compared to a salesperson who was not in the test group.
Company B encountered challenges during the prototype phase as only 75% of missing fields were mapped by the AI, with 10% requiring manual correction. Scaling across the entire CRM necessitated a new workflow, leading to significant costs incurred within the first 6 months for implementation, consultation, and AI service operations.
Learnings:
What was the main difference between the two approaches, and why was Company A so much more successful?
The underlying catalyst for Company A’s triumph lay in their strategic foresight. By prioritizing the augmentation of human capabilities over blind automation, they harnessed AI as a tool to amplify productivity and streamline operations organically. Conversely, Company B’s automation-centric ethos, albeit well-intentioned, underscored the perils of prioritizing tools over tailored solutions, ultimately culminating in costly setbacks.
Unlocking the Power of Augmentation
What is AI Augmentation?
AI Augmentation epitomizes a paradigm shift in which technological advancements are harnessed to empower individuals in executing tasks with heightened efficiency, accuracy, and efficacy. Contrary to displacement, augmentation accentuates the synergy between human intellect and AI capabilities, fostering a harmonious coalescence of strengths.
Knowledge Augmentation: Enriching decision-making through data-driven insights and foresights.
Conversational Augmentation: Facilitating interactive problem-solving through simulated dialogues.
Contextual Augmentation: Personalizing user experiences by adapting to individual workflows and preferences.
Creative Augmentation: Suggesting unconventional ideas to spur creativity and innovation.
Navigating Towards Success
Automation is pretty much at the other end of the spectrum. The goal is to perform tasks repeatedly without human intervention. However, as practice has shown countless times, trying to automate everything perfectly can be costly and not always worth it, especially if the cost of maintaining the automation is higher than the cost saved by it.
That’s why it’s usually best to start your AI journey with an augmented solution that has a lower level of automation and a lower level of integration (yes, you can build amazing use cases with low integration and low automation!). From there, you can take it step by step and progress to more complex solutions. [Check out this article]
While automation purports seamless task execution, the judicious integration of augmentation fosters a holistic transformation of business processes. Initiating the AI journey with an augmented approach not only yields immediate returns but also paves the way for a gradual evolution towards more intricate solutions, culminating in a robust AI ecosystem.
