The world of AI is rapidly evolving. Salespeople are now taking advantage of new technologies to make more informed decisions, cut down low-value work, and ultimately, close more deals faster.
AI isn't new to the world of sales. Whether we talk about conversational intelligence or classification, we've seen many examples of how it can help sales teams. However, AI is now more natural and safe to interact with than ever before. This unlocks a whole new realm of use cases, making AI much more (A) accessible by individual contributors and (B) integrated within daily workflows.
Where AI currently falls short
Over the past few years, AI has made its way into sales, helping organizations make more informed, data-driven decisions about their revenue engine. It is a natural evolution from the last decade when revenue organizations focused on gathering large amounts of data (thanks to CRMs).
We've come a long way, but there is still much to be done for AI to assist sellers in selling.
Focus on management
The main use cases for AI today appear to be centered around management needs such as forecasting, performance management, funnel analytics, etc. This is to be expected, as they are the ones with the authority to allocate funds. However, these use cases do not directly lead to an improvement in win rates or acceleration of sales velocity. As a result, individual contributors do not experience the benefits in their daily work.
Countless dashboard. No action.
Dashboards are so 2012... We have created an abundance of reports, dashboards, graphs, and charts to analyze CRM data. Let me be clear, these are important, but it becomes overwhelming and confusing for sellers. Many don't know what information is relevant, what is outdated, or how to use that information to advance their pipeline. These dashboards are not conducive to taking action.
Not there when you most need it
Today's analytics solutions are typically housed in separate systems from those used by individual sellers in their daily work. As a well-known Aristotle once said, "out of sight, out of mind." As a result, their adoption by sellers is typically low, and they do not see the benefits in terms of productivity or performance. This is a missed opportunity.
One major barrier to the adoption of AI by sellers is the issue of trust. Many sellers are skeptical of the accuracy of the insights presented to them. This skepticism is understandable, as sellers are the ones with their commission on the line and who would need to explain to their managers why a deal was lost. The skepticism often stems from the lack of transparency in AI. People are unclear about the data being used and how decisions are being made.
What can we expect from AI in sales
2023 will be the year where AI will be put to the service of individual sellers, not their managers. It will allow sellers to work more efficiently and make more informed decisions on their pipeline.
With the rapid advancements in AI innovation, one can only imagine the variety of use cases it unlocks for sales teams. We've highlighted below a few examples of how AI can be applied to support sellers in their day-to-day.
Automation of data entry
Data entry is a major annoyance for many sellers because it is incredibly time-consuming. It often feels like a punishment, with the repetitive and manual updating of the same information across different systems.
AI can automate most of these low-value administrative tasks by leveraging CRM data and third-party integrations, such as notes, calendars, and tasks. Here are some examples:
Updating fields of existing records Whether it's the close date or deal amount, AI can learn from existing information and guide sellers on how to keep their CRM accurate and up-to-date.
Creating new records AI can automatically create a new contact for an opportunity based on the attendees of a meeting, individuals cc'd in an email thread, or recent job changes in a prospect's organization.
Syncing activities Say goodbye to manual entry of activities. AI can integrate with various event tracking systems, such as calendars, emails, and dialers, and automatically assign opportunities to records.
With the abundance of information available today, we are now capable of identifying similar opportunities based on various factors such as industry, sold products, geography, deal size, and so on. With a significant amount of data, sales teams can take advantage of this classification to recognize successful trends, playbooks, and so forth.
Think of a scenario where a deal has been stalled for a prolonged period. It is reasonable to believe that AI can guide sellers on...
Who are the relevant stakeholders to engage at various stages of the sales cycle?
What sales tactics have the highest win rate?
What content leads to higher conversion rates?
What upsell and cross-sell opportunities are available for a particular account?
AI has the ability to predict the best course of action that can increase the chances of a sale, ultimately leading to shorter sales cycles and an improved win rate in the sales pipeline.
This is exciting! There has been a rise in new natural language processing technologies in the past few months. With large language models like GPT-3 or LaMDA, AI can now generate text content and other types of content based on natural language prompts.
AI can now write for you with accuracy like never before. For sellers, this means that AI can:
Create a personalized email for reaching out to a new stakeholder, based on their role, pain point, and your product's value proposition.
Create a meeting follow-up email that synthesizes meeting notes.
Create and pre-fill an account plan structure.
Create a template for qualification questions based on the prospect's industry.
This helps save sellers a tremendous amount of time and reduces variability in the quality of work across the team's sellers. This is real-time enablement.
There is a growing volume of information out there for sellers to leverage: more playbooks and data in your internal systems, more social activities, and more available reports on your target accounts.
A seller's responsibility is to navigate, synthesize, and analyze available data to make decisions on which deal to focus on or what best actions he should take on a deal. With research and synthesis being extremely time-consuming for sellers, AI can speed things up. It can fetch information from thousands of sources and boil it down to the most important 10 most recent highlights of an account, contact, or lead.
This saves hours per week for the sellers, leaving sellers with information to analyze and take action on - something only humans can do.
2023 will be the year where AI will be put to the service of individual sellers, not their managers
In the world of sales, AI will always remain a decision raid rather than a replacement for sellers' jobs.
Where sellers meet AI
At Pod, we're revolutionizing the use of AI for account executives by making it more accessible and user-friendly. Our AI-powered productivity workspace integrates AI into the daily sales workflows of AEs, enabling them to close more deals at a faster pace. Additionally, AEs can save hours each week by cutting down on low-value administrative tasks.
At Pod, AEs come for the AI and stay for the productivity!