How do you build trust with your audience? By consistently showing up and adding value. Jared Robin, CEO of RevGenius, is a LinkedIn regular with a unique style, a compelling story, and a clear point of view. This has helped him build a strong personal brand and connect with over 45,000 followers.

For leaders, managers, solopreneurs, and individual contributors, building a powerful presence on LinkedIn is a game-changer.

A HardNumbers study shows a strong link between social media presence and financial success for unicorn companies with active founders.

“Our report reveals that unicorn companies whose founders have the most LinkedIn followers secured over £763 million in total investment on average. This is over 20% more than the average total raised – £632 million – across the UK’s entire unicorn cohort.

“This shows that investors want to align with leaders who have a clear, compelling story. When the story resonates, investor buy-in is more likely.” [Source]

The compelling story needs to be told. We know building your personal brand is needed, and we know it works. What’s next? 

Balancing act: How to start and stay consistent? 

We’ve all been there struggling with:

• Finding time for consistent content creation
• Generating fresh, cool, and engaging content regularly
• Balancing personal authenticity with professional branding
• Engaging in a meaningful way 
• Using AI in more elaborate ways than simply chatting with Chat GPT  

Whether that’s a busy CEO, founder, manager, or IC, building a consistent and powerful personal brand is a full-time job. 

Strategies to build your personal brand 

That’s why we talked to four experts who shared their strategies for building a personal brand and engaging with their audience, all while using AI as a tool. Check out our session with Justin Simon, The Founder of the Distribution First podcast, Brianna Doe, the Founder and CEO of Verbatim, Chelsea Castle, The Head of Content and Brand at Close, and Blaine Bolus, the Co-Founder for Castmagic, or read the recap below.

Start with a strong point of view

“Be willing to take a stand and repeat it until it becomes a recognizable theme.” – Justin Simon

Having a clear and recognizable POV is the starting point in building your personal brand. The consistency in messaging and defining your unique value proposition will guide all your efforts. As Justin explains, the more you talk about the topic, the more it becomes ingrained in your audience’s minds. This clarity helps in establishing your voice and makes your content memorable. 

How can AI help? 

AI-powered tools like sentiment analysis and market research platforms can provide insights into what your audience cares about. This data helps you craft messages that resonate and differentiate you from the competition.This not only sets you apart but also gives your brand a solid foundation. Use AI tools to analyze market trends and audience preferences to refine your value proposition.

Castmagic has an iOS app that turns your thoughts into content — you can share your thoughts, and let the app transform them into multiple posts. You can also use the Content Sample feature to style your content like the influencers you follow on social media. 

Having a clear “why” and an established POV helps in setting a clear direction and reducing content fatigue.

Be authentic. Always.

Authentic content is what truly resonates with your audience and builds trust. “Be clear on your ‘why’ and set boundaries to approach personal branding from a healthy perspective,” Chelsea Castle explains. Genuine engagement is key.

Balancing professional and personal brands helps with authenticity concerns.

Define the one word you want to be known for and focus on evoking the desired reaction from your audience.

How can AI help?

Personalization at scale: AI can help personalize your interactions at scale. Use it to analyze your audience’s behavior and preferences, allowing you to tailor your messages. This personalization helps build stronger connections and fosters trust. As Justin Simon points out, it’s about integrating your brand into everyday interactions without losing the personal touch.

Meaningful Engagement Avoid using AI for generic responses, especially in comments and direct interactions. Brianna Doe, the CEO and Founder of Verbatim stresses, “Creating content online and building a personal brand online doesn’t mean you can’t have boundaries.” Focus on meaningful, thoughtful engagements that reflect your genuine interest and expertise.

Store, Revisit, and Refine Your Ideas

Coming up with consistent content ideas can be challenging. Brianna Doe shares her approach: 

“I use Notion to store and organize content ideas, categorizing them based on my content pillars.” Having a system in place ensures that you always have a pool of ideas to draw from.

Blaine, co-founder of Castmagic, recommends creating a library of high-performing content from other creators. “Building a swipe file can serve as inspiration for what to post and when,” he explains. This file can serve as a valuable resource for generating new ideas and understanding trends in your niche.

Capture and document first, and then fit those into ideas later

How can AI help?

Repurposing: Tools like Castmagic can help repurpose content from various formats, ensuring you get the most out of every piece of content you create. This means transforming webinars, podcasts, and articles into bite-sized social media posts, blog entries, and more. Imagine taking your raw thoughts from sales calls, meetings, or recordings and transform them into a variety of content types: social posts, newsletters, blogs, etc. You can turn any conversation into insightful leadership content. 

Writing assistance: While there is a debate about using AI for direct content creation, it can be incredibly useful as a writing assistant. Chelsea Castle mentions using AI to “synthesize and analyze content” and improve the clarity and conciseness of your writing. Tools like ChatGPT can help refine your content, making it more engaging and easier to digest.

Automate to focus on connections

Automate to focus on connections. Identify where collaboration happens in your content workflow to optimize for both speed and authenticity.

Part of your job as a creator is being a curator of your own content

There’s no other way, we need to minimize manual effort but without losing the personal touch. Transcriptions, summarizing, scheduling, and content distribution can all be handled by tools but curation is key:

Curate and evaluate content before automating to avoid appearing inauthentic.

And by content we mean not only podcasts you were a guest at, your articles, the company’s webinars, and interviews but also: your calls, conversations with customers, community chats with your friends AND your own thoughts. 

“Recording thoughts and conversations can create a multimedia library that can be repurposed into different content types.” [Justin Simon]

TL: DR Best Practices Cheatsheet

• Tap into your media library for evergreen ideas. Turn context into content.
• Keep it simple. Know what you want to be known for, how often you want to show up, your point of view, and who you want to connect with. Building a personal brand is all about showing up authentically.
• Record your thoughts and repurpose them as much as possible.
• Embrace authenticity and use AI as your assistant and sparring partner.
• Consider moving your audience from rented platforms to your own (like newsletters) and use AI for deep personalization.

How can Castmagic help in content creation? 

RevGenius webinar session was powered by our partner Castmagic. It’s an AI-powered tool that makes it easy to create content from your audio and video conversations. It transcribes and summarizes key points from webinars, podcasts, and meetings, allowing you to quickly turn them into blog posts, social media updates, and newsletters.

Imagine a CEO who regularly speaks at industry panels and webinars. With Castmagic, these sessions can be transcribed and key insights extracted to create LinkedIn posts, a blog, and content for a company newsletter.

To give you an actionable plan and help with building your personal brand with AI we sat down with the Castmagic team to share the multiple ways you can use it — dive into the use cases here or check out our breakdown:

• Pages — Combine multiple recordings to extract and repurpose content into a 2000+ word article, newsletter, social post, and more.

• Customer Discovery — Record your sales calls and push them through the Customer Discovery Profile. Learn more about companies to come up with authentic write-ups. 

• Courses — You can take the webinars and run them through the Courses Profile, get worksheets and quizzes, summaries, and cheat sheets, and even build out a course program. 

• Podcast — make sure to repurpose your podcast once recorded into various formats: articles, interviews, courses, and social posts. 

• Meetings — Record your meeting and get quotes, key points, and ideas listed. Let the AI organize the agenda and action items. Use the shared link to send the recap to the team. Perfect for inspiration, content posts, and more.
• iOS App — Start recording your thoughts. A 2-minute recording on your walk can turn into an endless supply of social content to build out your brand on Linkedin. 

Remember that you can create your own prompts to repurpose any audio or video recording in your writing style and brand voice.

Sign up for a free trial and experience the magic of Castmagic!

Your personal brand is a reflection of your professional journey 

As Jared Robin highlights:

“I get into dozens of conversations daily with community members, many of which are automatically recorded on Zoom. What comes out are so many thought-provoking ideas and concepts. Castmagic allows me to create content from that easily and repurpose it so the gems get captured and don’t die when the call ends. These conversations contribute to the brand I’m building.”

Your personal brand reflects your professional journey – make it impactful, authentic, and memorable. Don’t miss our upcoming webinars on marketing, sales, and GTM trends!

The post Taking Your Personal Brand to the Next Level With AI appeared first on RevGenius.

But how exactly? How will productivity improve? How will deals close faster? How will we do better discovery? Will managers stop asking for Salesforce updates? (Imagine that!). We’ll look at real examples of how life will change and what we can do to prepare.

Personalized prospecting emails and content creation are just the first steps. There is more than meets the eye. But to fully appreciate how our world is about to change, it’s important to understand what GenerativeAI is.

Generative AI simplified

Imagine someone was to ask you to predict the next words to the sentence “I will be late to___”. Answers could be “…pick up the kids” or “…the pool party!”

Based on the context, the predicted answer would change. A parent’s answer is different from a teen’s response. GenerativeAI is good at predicting the next words, given some context.

This means that, for the first time, machines can understand language and context. They can read, write and draw! AI made sense for me when I imagined it to be a young child. It can “understand” things, “do” jobs, and converse.  

And with time and data, AI’s understanding will mature to become an adult. So, what would you have your adult replica do?

1. High Definition sales coaching, available on-demand

• Realistic role-playing simulations will up-level reps sooner

Reps are starving for coaching. In a remote world, managers can’t sit in on every rep’s call or learn the product well enough to provide thorough coaching.

However, GenAI built on recorded customer call data can simulate a real customer conversation, including questions and objections! Just add a face and voice to the text being generated, and boom, you have a customer. F1 drivers train on car simulators, and Spotify’s new “DJ” is a GenAI voice. It sounds like a DJ on the radio playing your favorite hits.

Role-playing has always been superior to reading slide decks or watching videos. As a result, reps will be able to ramp up, contribute faster and build confidence. I was stunned to hear that reps at Zoom would prefer to learn from AI bots rather than humans — because bots don’t judge.

• Just-in-time training reduces ineffective enablement session 

While I appreciate (and have conducted) product or competitor training around QBRs, it can be challenging to retain the information. Especially if it takes time to be applied.

On-demand simulations will provide reps with practice topics just-in-time for maximum impact. GenAI can constantly learn from customer calls, providing many real-world scenarios. Sales-enablement team’s challenge will be to moderate and determine what to learn now versus just-in-time.

• AI guidance will ensure great discovery and best practices on every call

Numerous sales leaders at scale-ups have told me: “We’re going back to basics,” no matter how tenured their teams are — getting the three whys, confirming timelines, setting next steps on the phone, confirming the Champion and EB.

Once GenAI is trained using recorded sales-calls, it can start nudging reps to improve their behaviors. Simple ones like ‘5 mins left, confirm next steps’ or more complicated flows to uncover BANT using 2nd or 3rd-level discovery. It excites me to think how repeatedly good calls will improve morale and bottom lines.

2. Repetitive admin tasks will auto-complete

• Manually updating SFDC will be a thing of the past

I’ve had my fair share of managers nagging me to update opportunities, Clari, and other tools. 

AI’s ability to understand language means it can follow conversations between the buyer and the seller. GenAI understands Next Steps, 3 Whys, pain points, and MEDDPICC. Because of this, it can auto-fill SFDC after a customer interaction. This saves reps time and effort, and it also helps to ensure that data in SFDC is in the customer’s voice and up-to-date. Many reps have told me that this could help to improve trust between them and managers. And also redirect time to prospecting.

• Follow-up emails will write themselves, and scheduling will happen without humans in the loop 

Note-takers can already produce decent summaries and identify action items because they understand language. As models learn company context, accuracy will improve too. This will be extended to follow-up emails. Emails will auto-generate and wait in the seller’s Draft folder, ready for review & send. No more re-listening to Gong calls at 1.5x or searching transcripts before manually writing follow-up emails. GenAI bots will do the gymnastics of scheduling meetings across 3+ people. All this time will be reallocated to prospecting or working with the customer. 

• Creating & updating internal docs for sales engineers and management will be easier with Generative AI

Many reps feel like “content creators.” Some of it is valuable work, like building our Command Of The Message decks after a call. But some of it is not, like constantly re-jigging notes into Google Docs or Sheets for our sales engineers or managers.

Similar to follow-up emails, GenAI will auto-generate the pitch deck and then turn notes into whatever format is requested. However, the capability of artificial intelligence to work across tools is still in its early stages. Steve Brockman’s example of ChatGPT providing dinner ideas, posting them on Twitter, and then auto-ordering ingredients with Instacart is a great example.

• Answering customer questions will take near-zero effort

How often have we answered similar customer questions via email? Or gone searching for an answer by reading blogs, technical docs, Highspot, Google Drive, and asking our sales engineer?

Once GenAI is trained on the company’s knowledge base (like blogs, technical docs, white papers, and support tickets), answers to customer questions will be automatically generated. Just like Gmail’s Smart Compose feature, but with your company data and tech docs or links attached.

Imagine when a customer asks about security or integrations. GenAI will automatically search for the answer, paste a response, attach a URL or white paper, and have a draft for the AE or SDR to… simply hit Send. And this may all happen right in the AE or SDR’s Gmail inbox. In many cases, emails or support tickets will auto-reply, like chatbots. This will reduce the workload on reps, customer success and technical support teams while improving accuracy and response times. Intercom is starting to roll this out. AEs or AMs interactions will be seen as higher value-added partners in the sales process without creating new work for them. 

3. The cross-functional collaboration will happen without creating new work

Reps won’t have to content-create for Product teams 

Reps bring back learnings from the front lines, which Product Managers find valuable. The extra-email-thread or 30-min sync with the PMs reduces time towards sales activity. What’s worse is that PMs need aggregated observations rather than one-offs.

GenAI call summaries may be enough for PMs. AI agents may sit on customer calls and send data back to PMs or automatic data entry info SFDC can solve this. There is pain here that can be addressed.

4. Relevant (not personalized) content creation is accelerated

Relevant messaging will be more critical than personalization 

GenAI’s ability to personalize for prospecting, social media content, or marketing nurture is remarkable. This has been covered broadly, so I’ll make one new point. What happens when every email I receive is personalized?

Relevance becomes paramount. Relevant emails address timing. When GenAI begins weaving in intent triggers (key hires, change in leadership, product announcements, content-click-through or poor product reviews on G2), GenAI prospecting will get exponentially more interesting.

So what can we do to prepare?

Make managers feel our pain and educate them

The initial impact of GenAI is felt more by individual contributors than sales leaders, who may not use the tools but will likely be part of the buying committee. Educate them on your daily challenges, which they may not have faced in their time. Test the tools with colleagues and mention them in meetings.

Ask: “Does it add work or remove work?” 

If the new tool or process creates more work, then slow down! If exporting contact data into CRMs has created more clicks, wait. We’re bogged down with mundane admin tasks in the first place because of the older class of tools.

Pick your battles wisely

Consider which AI sales tool would have the most impact on your daily routine. Imagine replicating yourself and delegating some of your daily tasks to your replica — which tasks would you assign?

I hope this makes you bullish about the future impact on AI on our day-to-day lives as sellers. Or maybe not — if so, I’d love to learn what you see as the future. A lot of this seems far-fetched. Just like Generative AI seemed 2 years ago…

The post AI-Powered Sales: Key Tactics to Empower Sellers appeared first on RevGenius.

In this blog post, we’ll explore where Generative AI goes from here and how it continues to shape the future of B2B SaaS. We’ll look at the implications for businesses, their customers, and the wider world of technology.

GPT-4, Math & Logic

First, let’s explore how GPT-4 and a large part of GPT-3.5 (also known as Turbo) differ from their predecessor GPT-3.

One of the parts of this technology development that is not discussed much is how good the next generation of Generative AI is in terms of math and logical reasoning. To understand that, let’s look at how GPT-3 has gotten good at writing long-form text content.

The way we have been teaching AI grammar in the past has involved trying to make it understand every grammatical rule of English and then use it to generate new sentences. This has in many ways proven to not work over the years. GPT-3, however, was able to digest a large piece of text and generate whatever it thinks is Grammar which in turn was used to write long-form content.

GPT-4 will do to math and logical reasoning what GPT-3 did to Grammar and long form content generation.

Instead of making AI understand Math painstakingly one rule at a time, we are now feeding it large pieces of Math to let it understand whatever it thought of as Math and come up with its own mathematical rules to then solve problems. Similar to how Grammar was approached.

As an example, here is a math problem from a GRE standardized test:

A certain pet store sells only dogs and cats. In March, the store sold twice as many dogs as cats. In April, the store sold twice the number of dogs that it sold in March, and three times the number of cats that it sold in March. If the total number of pets the store sold in March and April combined was 500, how many dogs did the store sell in March?

GPT-3.5 makes light work of this question with the following answer:

This is a significant progress from its GPT-3 predecessor.

However, there will be lots of questions that do not have a simple answer online but need to be answered with logical reasoning. Take a question like this for instance:

How many states in the US have an odd number of neighboring states?

To answer that question you need to understand the semantics of the question as well as the reason from a map or database of neighboring states that know how many such states exist.

To see this capability in practice, take a look at this logical reasoning “hard” question from the GREs:

GPT-3.5 has no problems with the reasoning.

Logical Reasoning Over Private Datasets

You will soon have Generative AI that doesn’t just generate long pieces of text, but can generate content with logic behind the scenes.

So how does this apply to B2B SaaS businesses?

Imagine you could ask the following:

Can you find the list of prospects we have not contacted in the last year but we had reached out to the year before last and create an email sequence introducing our latest product features?

Generative AI should be able to query the needed databases and create the email sequence from this prompt in a matter of moments.

Continued Applications for GPT-4 in Business

There is a clear application in using GPT-4 models to mine leads and campaigns from large datasets quickly and accurately, such as the example given above, to help with a targeted sales campaign. Here are some other business applications:

Automating mundane tasks: GPT-4 models can automate tedious and time-consuming tasks such as data analysis, customer service inquiries, report writing, and more. This frees up employees to focus on more important projects.

Improved decision-making: AI-powered systems can analyze massive amounts of data to uncover insights that could improve decision-making across all levels of a business. It can help accelerate processes by providing faster and more accurate insights into data sets or customer interactions.

Improved accuracy: By leveraging GPT-4’s natural language processing capabilities, businesses can improve the accuracy of their workflows by eliminating manual errors and ensuring high-quality results.

Reduced costs: Automating laborious processes with GPT-4 models saves companies time and money, enabling them to reallocate resources to other areas that require attention.

A lot of work on the next generation of AI will be making sure that it not only works over the same datasets everyone has but also over your own private datasets. This includes CRM, CMS, SEP, MAP but depending on other applications, it could also involve ATS, Email Servers, HRIS and others. This is really where the quality will begin to improve.

GPT-5 and Beyond

The impacts are already visible, but what comes after GPT-4?

The next major milestone for models will be combining multiple modes of content and communication simultaneously.

Right now progress is being made independently on text and images as well as the early progress on voice and video. Going forward, we will be able to have AI simultaneously produce these pieces of content. I don’t mean producing each of these pieces of content independently and showing them together but rather seamlessly combining them together.

For instance, in the future you will be able to create a video script, with the video and storyline as well as voice-over and maybe even pictures and images of screenshots. There will no longer be text-only AI and image-only AI but AI that can do all of the above simultaneously or individually as the use case needs. And that will become a great tool for B2B SaaS companies looking to scale their campaigns and programs while keeping costs low and quality and speed high.

Generative AI is an exciting development with wide-reaching implications for B2B SaaS businesses. As research continues, it’s safe to say that we’re just scratching the surface when it comes to what Generative AI can do for us moving forward.

Srinath Sridhar, CEO & Co-founder, Regie.ai

Srinath Sridhar is an experienced veteran in the tech industry with over 15 years spent leading high-performance Engineering teams for some of the most notable tech companies. He was part of the early 100-person Engineering team at Facebook and was a Founding Engineer at BloomReach. While there he worked on their innovative Search and Recommendation engine that has influenced the entire search and recommendation ecosystem we utilize today. Srinath also co-founded Onera, an AI Supply Chain startup, which was subsequently acquired by BloomReach.

Sridhar has his PhD in Computer Science specializing in Algorithms and Machine Learning from Carnegie Mellon and has written numerous patents, publications and textbooks on AI. Srinath’s knowledge and understanding of AI are rare even among the experts. He is currently Co-founder and CEO at Regie.ai, a Generative AI Platform designed for enterprise sales teams.

The post The Future of Generative AI: GPT-4, GPT-5 and Beyond appeared first on RevGenius.

But will the hype prevail?

In this article we explore how AI tools are beginning to be leveraged throughout the entire buyer journey to increase efficiency and productivity for front line teams.

What is Generative AI?

Generative AI uses artificial intelligence algorithms to generate new content that mimics how humans would write. So in the simplest form (at least at the moment) Generative AI involves writing long-form text content, generating images and is in the early days of generating videos. In the future, this will involve richer modes of content and communication but for the sake of this article, we will largely talk about text and images, the most mature modalities in this fast-evolving space.

While ChatGPT has made Generative AI more mainstream and gotten many sellers’ gears turning in terms of its applicability to them, a lot of what is being overlooked right now is how exactly it can be leveraged inside of an organization’s current GTM workflow. This tech will improve sellers’ lives only if it can integrate into the motions already in place.

How are sales teams today leveraging Generative AI?

To identify how sales teams can best leverage generative AI, simply ask yourself:

What kind of content do sales teams need?

Sales teams need content that is relevant, relatable and contextual for their buyers, in order to drive awareness, earn trust and win business.To understand how Generative AI can improve the productivity and efficiency of sales orgs, let’s organize sales activities into funnel stages, and then pick apart the core content needs of those areas:

1. Awareness
2. Prospecting
3. Engaging
4. ROI & Growth

AI for Driving Brand Awareness

While awareness is mostly associated with marketing teams, sellers are often tasked with creating awareness for themselves (personal branding), their market (category branding) and their company (product branding). This can involve a mixture of content types and modalities, such as blog posts, social media posts on LinkedIn and Twitter, videos on TikTok, and emails designed to nurture an audience.

Here are some of the core ways teams are using Generative AI for sales to scale top of funnel content production:

• Blogs – It has never been easier to avoid writer’s block and come up with creative ideas now that you have AI to help you autocomplete your thoughts. Generative AI can easily help individuals write blogs and is what most people seem to be using it for today. There are plenty of tools out there that offer competitive pricing and feature sets for writing blogs such as Jasper, Copy, WriteSonic and others.
• Social Posts – It is more important than ever to develop a presence on LinkedIn and Twitter,  especially if that is where your primary buyers live and consume information. Generative AI can now search through news, company posts and more contextually relevant data points to generate best practice-aligned social posts optimized for any channel.
• Nurture Campaigns – Creating content just became easier but you also need to think about proper distribution, especially because your buyers live and engage in more channels than ever before. When you or your company creates a blog post, you must leverage multiple channels to push that content out. The primary distribution channel is email. When sent through HubSpot, Marketo or other Marketing Automation Platforms (MAP), emails can reach large audiences.  Generative AI can now help you ingest blog content, regurgitate a relevant nurture stream that references the content, and then publish to your MAP for sending.
• Event Follow-Ups – Your company is likely running events like webinars, conferences, dinners and other demand generation programs to meet new buyers. You’ll need to ensure that you can invite all the ideal prospects to the events, remind them leading up to it, and follow up after the event in a timely fashion. Generative AI can help with all of that email creation and automate the sending.

Generative AI for Sales Prospecting

The art of prospecting is a great example of where generative AI will make an impact due to its ability to automate many of the mundane tasks associated with it. Sales teams will benefit from being able to focus more on closing deals rather than spending time on researching and crafting content.

Most notably, Generative AI can now help automate the process of creating engaging, personalized touch patterns that convert. Sales teams can create content quickly and easily, without spending hours researching prospects or writing personalized emails. This not only saves time but also leads to better results as the AI can craft content that resonates with each prospect based on the research it’s able to collect on the person and their company.

When we think of personalized outbound, here are the five biggest areas for streamlining content creation using Generative AI:

• Email Sequences & Cadences – Multi-touch sequences that are generated for specific sales engagement platforms like Outreach, Salesloft, HubSpot and others will soon all be written by Generative AI using performant data and following industry best practices.
• Call Scripts – Call scripts can be automatically generated by AI going forward and can even be written on a per-prospect basis.
• Manual Email Steps – At least a few of the emails inside a sequence or cadence tend to be manual steps that require personalization. These emails can now be written by AI based on the research on the prospect, the company, the funding and technographic information. Most sequences are dominated by automated steps, but this suddenly allows for more of those touches to be personalized. Companies like Regie.ai are already introducing this dynamic functionality into sequence writing.
• Videos – Generative AI startups like Tavus can help create personalized 1-1 videos that can be sent to prospects, saving you time and resources on creating those assets in-house.
• Calls – We will soon witness personalized voicemail drops and I predict it’ll be possible to make calls automatically using AI at some future date.

AI for Engaging Pipeline

Once you have your prospect on the proverbial line, how do you keep them engaged? That is where the real art and skill of sales starts to take shape, especially if you have long sales cycles and complex deals that often require multi-threading and consistent nurturing.

Generative AI can have a profound impact on sales teams’ ability to maintain movement in their pipeline by automating various engagement points, and quickly identifying and analyzing customer data (including events, behaviors, and interests) to determine the next best action for sustained engagement. Sales teams can now spend more time developing relationships with prospects, which is what they are hard-wired to be great at, and less time on mundane tasks that can be automated by computers such as follow-ups.

By leveraging the power of AI, sales teams can make sure that each customer is getting the best experience possible while they spend time on the most valuable actions. Here are some of the immediate ways Generative AI is going to help AEs with pipeline engagement:

• Sales Collateral – Customer case studies, testimonials and other collateral can be built using Generative AI. Oftentimes the internal content creation workflow between sales, marketing, ops, and other functional areas, can be time-consuming. Generative AI can jump start that creation process for all team members.
• Post-Call Follow-Ups & Replies – A lot of tech platforms are retrofitting their solutions to include generative capabilities specific to email and call follow ups. For instance, Outreach, Microsoft and Gong have all announced that they will soon be utilizing Generative AI to do transcriptions of calls and automate the follow-up emails. Other startups like Oliv are also AI assistants for sellers in this space that help draft replies to sales emails, objection handling, and more.

AI for ROI & Growth

Lastly, companies should also consider leveraging Generative AI to maximize their customer expansion motion by helping sales teams identify customer trends, track customer sentiment and establish better relationships. All of these activities can result in an improved ROI for companies looking to protect and grow the base of their business.

There are two clear ways Generative AI will help Customer Success, Cross Sell and Upsell motions that support target account growth:

• Cross-Sell, Upsell Sequences and Retention – Generative AI can help write sequences to begin renewal conversations 90 days before renewals are due.. Similarly, AI can also monitor account activity and suggest cross-sell and upsell email sequences and cadences that are timed-bound based on the user journey.
• Customer Support – This is an area where AI will shine easily. Whether that is customer support on the website or on calls, Generative AI is sure to help provide exceptional, well-timed and accurate service for customers needing support and it will only get better as the technology and underlying datasets are refined.

The applications of Generative AI for sales are just getting started—what’s next?

Generative AI is proving to be a powerful tool for sales teams, helping them scale and automate their workloads. From data-driven conversations with prospects to intelligent account analysis and renewal notifications, sales teams globally are using AI as part of their toolkit to deliver vastly improved results, boosting productivity and helping them win more.

As this technology continues to evolve, we are sure to see even more advances in its application in sales. Which reminds me… I need to start writing an article that contains my predictions for the future…

What’s next? That’s a great question! Generative AI is still on the move, revolutionizing sales on the way! It’s set to make a huge impact on B2B Tech sales, so it’s definitely worth checking out the current expertise gap and getting a sneak peek of the next moves.
Let’s dive in more on Revmag!

Curious about partnering with RevGenius? Talk to us! 

Srinath Sridhar, CEO & Co-founder, Regie.ai

Srinath Sridhar is an experienced veteran in the tech industry with over 15 years spent leading high-performance Engineering teams for some of the most notable tech companies. He was part of the early 100-person Engineering team at Facebook and was a Founding Engineer at BloomReach. While there he worked on their innovative Search and Recommendation engine that has influenced the entire search and recommendation ecosystem we utilize today. Srinath also co-founded Onera, an AI Supply Chain startup, which was subsequently acquired by BloomReach.

Sridhar has his PhD in Computer Science specializing in Algorithms and Machine Learning from Carnegie Mellon and has written numerous patents, publications and textbooks on AI. Srinath’s knowledge and understanding of AI are rare even among the experts. He is currently Co-founder and CEO at Regie.ai, a Generative AI Platform designed for enterprise sales teams.

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Either you are an executive who is getting this question from the board and your advisors, or are a manager or individual contributor who is thinking through the real-world workflow implications of this new tech.

But it’s for good reason. Generative AI has exploded in popularity and mainstream awareness in the past four months, in large part to the launch of Chat-GPT back in November 2022. Few times have businesses been forced to evaluate and implement a strategy so quickly. Reminds me of the advent of the internet, when every company had to think about what their online strategy was, or the emergence of social media, when every company had to think about what their Facebook and Twitter strategy was and or even mobile when every company had to think about what their SMS strategy was.

2023 will be Generative AI’s year. Mark my words.

It’s high time this advanced technology, which has been decades in the making, starts to be applied to sales teams’ workflows to increase efficiency and productivity in various parts of the sales process.

But before we can look at the promise of where we are headed, we need to take a step back and look at how we arrived here.

In this article, we’ll explore the history of Generative AI from a broad technical evolution lens. This article benefits anyone wanting exposure to the broader backstory of how we arrived at the Chat-GPT pandemonium of today.

Late 1970s – Early Days of Neural Nets

This story starts back in the late 70s and early 80s. Researchers at the time were developing neural nets designed to mimic the structure of the human brain in order to process data in a way that was similar to how humans do. The idea behind this technology was to assemble a set of neurons, each of which was connected to a set of other neurons. That way they could pass information from one to another with some very basic logic and together the network of neurons could perform complicated tasks. Although it was very primitive in its imitation of the human brain, amazingly this architecture has stayed intact through today.

Two canonical examples of AI have traditionally been speech recognition and image recognition:

• Speech recognition is converting human speech to text
• Image recognition involves identifying objects inside images

From the very early days, neural networks were used for both of these tasks (although for image recognition, humans had to perform a lot of gymnastics in code, such as removing the background of images, identifying the boundaries of images, and converting colors from one form to another). Identifying features inside an image, like eyes, nose, ears, etc. were painstakingly manually programmed, but it worked.

While minimal advances in both of these areas were made, neural networks and the associated improvements in speech recognition and image recognition remained a largely dormant field of research and development for roughly 20 years from the early 90s until 2010.

Early 2010s – Deep Neural Nets

In 2012, Google pioneered deep neural networks that added a lot more data, hardware and computer resources and intermediate layers.

Their breakthrough? They first used this to identify cats in YouTube videos.

The beauty of this method was that they did not have to do any of the coding gymnastics previously required with image processing. Instead, they just took the full image, passed it into the neural network and asked if it had a cat in it.

This breakthrough revived the field of neural networks and AI for good.

Research accelerated on the hardware side as well during this time. Both Google and NVIDIA invested heavily in specialized hardware to help with neural networks. For instance, in 2011, Apple launched SIRI, the first mass speech recognition application, which at the time was still rough around the edges but researchers could see that wouldn’t be for long. Similarly, Amazon and Google launched Alexa and Google Home, respectively. But this still had a long way to go before the technology’s full potential was reached for the public.

Mid 2010’s – DeepMind

In 2014, Google acquired DeepMind which built neural networks for playing games. DeepMind, with the investment from Google, built AlphaGo which went on to defeat all the top Go players.

→ The documentary on AlphaGo is definitely worth watching for both the human and AI angles.

One of the oldest board games to still be played to this day, Go is a highly strategic strategy board game in which the aim is to surround more territory than the opponent.

Why does this matter?

The big difference between playing Go and say playing Chess is that the number of next moves in Go is infinite compared to the number of next moves in Chess. Furthermore, unlike Chess, Go pieces are all weighted the same. So while you can easily assign “points” in Chess to get some semblance of good possible next moves, this is next to impossible in Go.

AlphaGo now had neural networks that could generate human-like candidate moves first. This was a pivotal moment as it was some of the first industrial applications of Generative AI using computers to generate candidate moves that looked like human moves. Until then, all the AI tasks had been on recognition (like image recognition and speech recognition) and not generating human-like outputs.

Given these rapid advances of DeepMind and Google the year prior, in 2015, OpenAI was created to democratize AI and set up as a non-profit. OpenAI wanted to ensure that the tech giants like Google and Facebook did not run away with the technology that was not yet available to the general public.

Late in 2018, Google responded and built an early version of a general-purpose pre-trained text generation model that could write human-like text. Until this point, computers could barely stitch two sentences together so this was another flag-planting moment for this tech, similar to the advancement seen in AlphaGo when we went from not being able to beat Go amateurs to beating the very top players.

All of these 2010 advancements by OpenAI and Google primed the world for the floodgates that were about to be opened.

Early 2020s – GPT-2 and GPT-3

In 2019, OpenAI improved on BERT by adding a lot more text and rebranded it to be called GPT-2 (short for Generative Pre-trained Transformer). At this point, Microsoft saw the potential mass application for this tech and invested $1B into OpenAI similar to how Google poured its resources into DeepMind five years prior. With this, OpenAI then created a for-profit organization.

In 2020, while in private Beta, OpenAI improved on GPT-2 and started releasing GPT-3. That is when it allowed other tech startups to build industrial applications on top of it with companies like Regie.ai getting early access to it.

Just a year later, in November 2021, GPT-3 was finally ready for prime time. OpenAI released GPT-3 to the public via an API (even though Microsoft had exclusive access to the models which were not open-sourced), putting them ahead of the field. Google, Facebook and others were caught flat-footed.

All of this momentum now brings us to a mere four months ago, when in November 2022, OpenAI released ChatGPT, an easy, one-button way for humans to talk to GPT-3, which does not involve any APIs. Rather, chats are saved as HTML files, in which GPT-3’s responses are displayed as images. While this may seem like a step back from the two previous interfaces, it was actually a monumental leap forward in terms of accessibility for the everyday person.

In four short months, this technology is already proving how it can help businesses across different sectors automate frequently asked queries and reduce human contact required for generating content-based outputs. However, as with all technology, it solves problems while creating new ones. A looming consideration is the social responsibility of us all to use it for good and not evil. To help empower the human, not replace it.

What’s next?

Like every disruptive piece of technology that has ever been introduced, all of us will soon adapt to the new normal and have a hard time imagining businesses without it.

The progress in this field has been at nothing short of a breakneck pace since 2012. It’s hard to believe that it has only been 10 years since the time humans started working on deep neural networks to now with the mass application and utilization of GPT-3. It is equally hard to believe that it has only been three years since the inception of GPT-3 and only 12 weeks since ChatGPT was released.

ChatGPT has truly been the Trojan Horse to accelerating the awareness and accessibility of this technology, and it has tremendous application potential when it comes to modernizing revenue teams. But that’s more for another day.

Srinath Sridhar, CEO & Co-founder, Regie.ai, is an experienced veteran in the tech industry with over 15 years spent leading high-performance Engineering teams for some of the most notable tech companies. He was part of the early 100-person Engineering team at Facebook and was a Founding Engineer at BloomReach. While there he worked on their innovative Search and Recommendation engine that has influenced the entire search and recommendation ecosystem we utilize today. Srinath also co-founded Onera, an AI Supply Chain startup, which was subsequently acquired by BloomReach.

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