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AI Agent Workflows for Sales Operations
AI Agent Workflows for Sales Operations

Examples of scenarios where Thunk.AI agents can automate sales processes

Praveen Seshadri avatar
Written by Praveen Seshadri
Updated over a week ago

Automation of sales processes is a well understood concept. For example, every sales/CRM system like Salesforce or Hubspot has automation features. These are typically used for lead qualification, for SDR communications, and for customer followups.
This article addresses four questions:

  1. How does an AI agent improve what we can already do today?

  2. What is the benefit of a self-service AI agent automation platform like Thunk.AI?

  3. What are some examples of Thunk.AI workflows used for automated sales operations?

  4. How does Thunk.AI coexist with a CRM system?

How can an AI agent improve sales operations?

In existing CRM systems and indeed in all automation systems over the last two decades, the automation logic is strict and deterministic. Typically, there is some boolean logic (some condition to be evaluated) and then specific actions to be taken which will use an integration with another system to fetch some data (eg: information about a potential lead) and add it to the CRM system.

AI agents allow this logic to be intelligent, adaptable, and flexible.

  • The inputs to the logic may be semi-structured and the AI agents can still extract sensible information from them. A very common case is for leads to arrive via email or messaging, sometimes in different languages. AI agents can translate and extract relevant information for entry into the CRM and subsequent processing.

  • Information associated with a sales lead often includes urls and documents. AI agents have the ability to read, understand, and reason about the content of all of this information.

  • Background research is always valuable to qualify leads and to steer initial customer conversations. It is usually a human-intensive process, yet AI agents that have access to the web and other customer databases can automate much of this work.

  • Many AI processes are human-centric (eg: any conversation between a customer and an SDR or account executive). And traditionally, sales teams depend on those humans to follow guidance, take notes, updates records in the CRM, etc. Every sales leader knows that this process is very lossy and inefficient. AI agents can monitor and reason about customer conversations, and they can automatically analyze sentiment, and record status updates.

  • There are many scenarios that require judgment and context, and therefore defy simplistic automation logic. For example, customer conversations related to renewals often require subtlety, yet in most teams, renewals are handled as mechanical operations rather than opportunities to expand or early-warning signals for churn. AI agents that can detect sentiment from conversation can increase expansion rates and reduce churn.

  • Sales collateral tends to be expensive to construct and therefore relatively static. AI agents can dynamically assemble sales collateral specific to each individual customer scenario.

The benefits of a self-service AI agent automation platform?

AI has much promise, but to apply AI in a truly effective fashion, one has to go beyond having humans sitting in front of a ChatGPT browser window and manually engaging with AI models. That is not an "AI agent" --- it does not run automatically.

It is difficult to build a reliable and scalable AI agent platform on top of the underlying LLM models. This is why there are hundreds of new research papers being published every week, each exploring how to balance the "intelligence" of the AI system with "stability and predictability". For a business, stability and predictability and control are crucial.
A sales team that wants to utilize automated AI agents has one of three choices:

  1. Use software engineers (expensive and slow) to build a software layer on top of the LLM models. In addition to the obvious costs of this approach, this is not a standard skill set for today's software engineers, and the LLM models are changing so fast that any software built today risks becoming instant legacy within a year.

  2. Use the AI that is built into the CRM systems. Every CRM system --- Salesforce, Hubspot, Intercom, etc, has sprinkled some "AI icing" on to their existing pre-AI systems. This is certainly valuable, but limited. Each such system is a silo of knowledge, but the challenge is that much of the information used by a sales team lives in email / messaging / cloud file systems / internal web sites / meetings -- a variety of locations outside of the CRM system and its AI system. And further, the use of AI through these systems is limited to the scenarios that the systems allow, rather than support the flexible needs of each sales team.

  3. Use a general-purpose AI agent automation platform that can solve the problems of a sales team with the custom priorities, process, and logic mandated by the sales leadership and sales operations leadership, and integrating all the local information and policies that influences behavior and decisions.

Thunk.AI enables choice #3. Because it is a self-service (no-code!) platform that doesn't need any software engineers, any sales operations team can start applying AI in a matter of minutes. Exploration of new ideas is easy and rapid. These agents can be custom to the team, integrated with other parts of the team and company, and provide a fundamentally differentiated advantage.

Examples of Thunk.AI workflows for sales operations

Here are five canonical scenarios where human workflows in sales can be augmented by Thunk.AI agent automation:

  1. New leads: conduct background research on new leads, score and qualify based on intelligent criteria, automatically assign to appropriate sales team members,

  2. Agent performance: analyze call transcripts to ensure adherence to company policy and processes, analyze and augment email communications to improve outcomes

  3. Customer engagement: estimate customer sentiment from communications and meeting transcripts, generate appropriate and custom communications based on the context of each customer, evaluate proposed contracts and agreements.

  4. Automatic data entry: Update CRM systems automatically with status information synthesized from emails, calls, and meetings, automatically record product issues or feature asks,

  5. Enable transitions between team members: maintain detailed customer summaries for new sales reps to ramp up rapidly, enable ad-hoc querying of customer history

And of course, an entire sales funnel can be implemented in Thunk.AI. This is particularly relevant if most of the sales activity is happening "outside the CRM" via email and other messaging tools.
Please see the growing set of live copyable samples as starting points to explore these opportunities.

How can Thunk.AI co-exist with existing sales systems

For most sales teams, the systems utilized are:

  1. The CRM system: this is the system of record, used by the sales leaders for reporting and analysis.

  2. The communication system: usually email but also messaging, WhatsApp, Zoom, Gong, call centers, etc. Sometimes integrated with the CRM but often not. The communications system usually has auto-recording features for live communications, and message sequences for outbound engagement.

  3. The lead generation system: usually owned by the marketing team, driving leads into the CRM system. But also often arriving ad-hoc via email and other sources.

  4. The sales collateral system: repository of product content, sales collateral, whitepapers, presentations, etc. This might be integrated with the CRM system or might be distinct.

  5. The ordering, renewals, and contracting system: for a variety of documentation at the later stages of the sales funnel.

The CRM is a system of record where human sales people record the work that they do. It doesn't make the sales person more efficient -- it is usually an overhead for the sales person to record their work. The actual work of the sales agent is in evaluating and understanding the customer, engaging with the right customer, collecting and sharing information with the customer, presenting the right solution for the right problem, and ensuring the customer's concerns are addressed.

Thunk.AI helps do all of these things. In addition:

  • Thunk.AI agents can automatically interface with the various other systems --- the communications system, the lead generation system, the collateral system, the contracting system.

  • Thunk.AI can automatically record progress and outcomes into the CRM system of record

In short, Thunk.AI co-exists and integrates with existing sales systems, just as it co-exists with the existing email system and the existing file system used by the sales team. It does not seek to replace them. Rather, it seeks to augment the productivity of the human sales team that already uses these sales systems. The AI agents in Thunk.AI add value by doing some of the work that humans have so far had to do manually. This makes the sales team more efficient and more successful in delivering sales outcomes.

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