Transforming sales funnels with AI: Insights from Luca Buccilli, Co-Founder of Accelerate AI

The release of generative AI tools like OpenAI’s Chat GPT 3.5 in November 2022 marked a significant shift in optimizing sales funnels.

These tools introduced dynamic conversational processes, surpassing previous deterministic agent-building platforms. The integration of AI capabilities into these platforms launched the era of conversational AI agents, democratizing sophisticated AI solutions for businesses. This evolution enables more personalized customer engagement, transforming every stage of the sales funnel and enhancing efficiency and effectiveness in driving sales and customer retention.

 “Every sales funnel going forward will be reshaped with artificial intelligence solutions, it’s not a matter of if, but when. The time to act  for businesses is exponentially shortening as competitive adoption rises.”  

Luca Buccilli, Co-Founder, Accelerate AI

In this interview, we speak with Luca Buccilli, Co-Founder of Accelerate AI (right, in the featured image), to explore how AI is reshaping sales funnels. Alex Krasun, left, is also Accelerate AI’s co-founder. 

When did you realize the opportunity to optimize funnels using AI as a service?

Since the public inception of Generative AI tools in November 2022 with OpenAI’s Chat GPT 3.5 the first Large Language Model (LLM) made publicly available for use; The idea of optimizing any kind of conversational process that involved task execution, information exchange and criteria evaluation, including sales funnels came to life.

Prior to this, agent building platforms existed but were limited to deterministic responses based on specific inputs, lacking true intelligence.

The market started to see agent building platform providers allow integration of Chat GPT’s capabilities eventually and this kickstarted the era of conversational AI agents, industry jargon “agentive” solutions. So one can say those startups further helped democratize Generative AI solutions for other entrepreneurs beyond OpenAI and other LLM providers.

How have funnels traditionally been conceptualized and is that changing? 

Traditionally, sales funnels have been conceptualized as linear processes that guide customers through various stages of the purchasing journey, from awareness to conversion and retention. However, with the advent of digital technology, this linear approach has undergone significant transformation.

The modern day pre-AI funnels incorporate some form of generic automated digital interaction through email or deterministic – option based constrained chatbots. Usually followed by live human interaction down the funnel.

For example, high ticket product/service sales that include some form of qualification, closing and support have presented major use cases. Qualification whether from inbound or outbound outreach at the moment is almost always done manually.

Through working with different clients, it’s obvious there is a clear business case for both automation and or augmentation. Automation meaning an entire task or job description can be performed by technology. Augmentation on the other hand meaning the most repetitive and necessary but low value parts of the job will be performed by technology to free up time to focus on higher value work.

How is AI impacting the sales funnel at different stages? 

AI conversational agent solutions can theoretically impact every point of the sales funnel:

Awareness/Interest: AI-powered conversational agents can tailor their approach through the information provided based on every unique lead’s queries. Meaning when a customer is going through their education process if you have an agent that can provide as much information/insight that they would have otherwise gone to 3rd party websites to find out, then you’ve done two things in one. Increased relationship and brand engagement, and retained more time with the customer throughout their educational journey.

Finally, with a conversational AI agent in place capable of providing what the customer wants to know before they narrow down their options. The chance of them narrowing down competitive options is dramatically reduced if your business’s brand was able to engage with them for a longer portion of their purchasing journey.

Consideration: When customers narrow down their top product picks, conversational agents can provide personalized product recommendations based on described customer needs/wants, address queries in real-time, and favorably compare products transparently to the broad market. By doing so, it increases the chance that most of the items in the consideration set would be related to the business’s product portfolio and not competitors.

The AI agent could even help make favorable comparisons between it’s products and the broad market to show transparency and simultaneously position their products’ in angles where they are superior.  All this guides the lead towards closing with said business.

Decision: Depending on the product/service, this is where the AI agent may or may not be closing the sale. Instead looking back, it has enhanced the live sales agent’s job by having thoroughly informed, product matched, compared market alternatives and finally persuaded to book a call with a sales agent. That agent has now had most of the work done for them, and they have the full summary of notes on this specific lead’s needs and possibly predictive suggestions in order to tailor close the deal.

Purchase: AI conversational agents can provide upsells and additional discounts upon the purchase or following and of course redirect the customer to a payment page assuming no live sales agent is involved.

Retention: Post purchase, AI-powered customer support, personalized marketing campaigns, and proactive issue resolution can enhance satisfaction and loyalty. By engaging in conversation should they need any support post-purchase, checking in over time and providing any further value depending on customer’s feedback.

How do you strategize the priorities and steps when optimizing processes with AI?

A business case for implementing an AI solution is first formed by understanding the business model and all the processes that cumulate to the business “machine” running. Including, aligning the organization’s overall objectives.

Second, there is an assessment of high vs. low impact processes by considering factors such as repetitiveness, time consumption, cross-functional interdependence with other processes, complexity of labour required, and so on and so forth.

Essentially, using an 80/20 approach one can narrow the key activities that take up significant resources and are necessary for the business to deliver value, but are not the activities adding the most direct value to the customer. These are the first areas where AI can be implemented to free up labor capacity and have the work force focus on what matters most.

Lastly, it’s important the client is data-ready and has an idea of the process they want to automate, meaning they have all the information and functions they would like the AI to learn and do in order to make it an expert in managing and delivering the right performance in relation to their process. Assessing the availability and quality of data required for training and deploying an AI solution effectively is crucial.