Sales Stack AI: How Generative AI Can Consolidate All Sales Tools

Written by Max Woo Co-founder & CRO at LavaReach

I launched LavaReach after spending years selling products and services from brick-and-mortar businesses like garage door installations to complex 6 figure ACV deals at venture-backed SaaS startups. From all those experiences I’ve extracted learnings that summarize the selling experience and now I’m on a mission to revolutionize the way teams prospect, research, and reach out that not only converts to meetings but closes the deal.

LavaReach stemmed from my frustration selling complex software without being equipped with proper research and account lists. I strongly believe that salespeople should focus on building relationships, while technology, especially with the rise of AI, should provide the necessary intelligence. I found myself ironically spending half of my time researching prospect data while working at a data company. It was then that I realized there must be a better way to leverage data and AI to enable me to deliver the value I know I can deliver, build strong relationships, and ultimately close deals.

In this article, I’ll be outlining the trends taken on by sales software over the past few years and how we think consolidation could happen very quickly aided by generative AI, not just from the big guys, but the startups as well. In our estimation, sales stack AI will quickly become essential to every sales team and many tools used today will be turned over.

State of the Modern Sales Stack

Thousands of sales software have been developed over the years and more are being released from existing companies and new start-ups. Softwares in the sales space can be very unique, serving a specific niche, or very comprehensive that manage the entire customer experience journey. A report released by Statista indicates that the sales software market will likely grow to $193.1 billion in 2032, up from $71.5 billion just last year in 2022. 

Competition and Feature Consolidation of Sales Tools

While this growth will continue to be fueled by global demand, there are also tremendous amounts of feature consolidation happening across the industry. According to CBInsights, tech stack consolidation has been a recurring theme in the minds of sales leaders. Point solutions that serve a single purpose are becoming less attractive. Well-capitalized companies from Outreach to Gong to Salesforce are building all-in-one solutions that range in contract value between $40,000 to $100,000. With such a high price tag, it seems like only enterprise customers can reap the benefits of such consolidation, leaving SMBs, emerging companies, and bottom quartile mid-market companies with pieces of this software or going to multiple smaller vendors to solve their pains.

One of the unicorn startups had feedback for Outreach that mentioned “The biggest issue with the product overall is the pricing and quality of conversational intelligence and forecasting in comparison to other tools”. From this review, we can see that even a company valued over $1B struggles with the high price tag. Although Outreach tries to compete with Gong over conversational intelligence, they are still playing catchup, especially with conversational intelligence being Gong’s core competency. The reverse can probably be said for Gong, where they are trying to build outbound automation features that rivals Outreach but they are still playing catchup too.

Another customer provided feedback to Clari that pushes back on the state of consolidation happening in the industry saying “I’m not personally convinced that there is a one system to rule them all sort of approach. And so I think my recommendation to Clari would be actually stick to your knitting and do what you do well, continue to do it really well, and try not to reach out into all of those other places that are, in my mind, distinct from what you do, and you’re probably not going to do as well as a Gong does or a HubSpot does or something else.”

Clearly, the classic jack of all trades and master of none scenario is playing out in the industry and everyone has an opinion.

Enterprise Sales Stack Feature Comparison

Let’s take a look at how each of these big players stack up in terms of features.

The table above has an excessive number of checkmarks. Interestingly, Apollo, the most product-led and agile company, has covered more features than others.

This trend of consolidation presents both challenges and opportunities for companies building sales tooling as well as customers. On one hand, there’s certainly stiffer competition from all sides, but customers may also be looking for more lightweight solutions that do parts of the job at much cheaper price points. A prime example of this is the much more affordable Gong competitors such as Fireflies.ai and Otter.ai which have both reached 7-figure ARRs and millions in funding. However, as you add more and more of these lower price point tools to your sales stack, your team will be working off of many data silos, which will eventually cause confusion, whereas the purpose of these tools was to provide clarity.

How AI Disrupts the Sales Stack

The world of sales is experiencing a paradigm shift with the advent of sales stack AI. Sales leaders are now at a crossroads, where they must decide how to integrate AI tools into their sales stack to remain competitive and relevant. Although the word disruption is thrown around too carelessly in tech, I aim to provide a comprehensive insight into how AI disrupts and enhances the sales stack, which ultimately relies solely on adoption. My goal is to equip sales leaders with actionable insights to harness the power of AI effectively.

Lead Generation and Prospecting

In traditional sales, a significant amount of time is spent on lead generation and prospecting. However, AI has transformed this landscape by automating and optimizing these processes. AI-powered tools can analyze vast datasets to identify potential leads, score them based on their likelihood to convert, and even predict the best time to contact them. In a world where only 3% of prospects are active buyers, 7% of prospects have the intent to change, and 30% have a need but are not ready to act, leveraging these AI capabilities to be in front of buyers at the right time can provide an extremely powerful edge.

Personalization at Scale

One of the critical challenges in sales is maintaining a personal touch while managing a large number of prospects and customers. AI bridges this gap by enabling personalization at scale. Sales teams can implement AI-powered recommendation engines and chatbots that provide personalized product recommendations and responses to customer inquiries. This ensures that every customer feels valued and understood, leading to increased trust and loyalty. I think there’s a step beyond what most personalization tools are providing, and that missing piece that’s blocking superior quality is data. Data about your value prop, data about the triggers that are relevant, and data and research about your unique prospects. Before starting LavaReach, we built a LinkedIn automation tool that aimed at personalized connection requests. What we found is that although the messages are personalized to the LinkedIn profile, it all sounded very similar with each other and to other tools, and that’s when we realized that we have to start with the prospect research and real time data/triggers. 

Enhanced Data Analysis and Forecasting

Accurate data analysis and forecasting are crucial for making informed sales decisions. I have witnessed firsthand how AI algorithms excel at identifying patterns and trends in large datasets, providing sales teams with valuable insights that might have been overlooked. By adopting AI-driven analytics tools, sales leaders can enhance their decision-making process, leading to more accurate sales forecasts and strategies. While doing discovery for problems in the sales space, we made hundred of cold calls to VP of Sales and CROs, and the recurring theme of pipeline forecasting kept occurring, where reps get happy ears and misrepresent the stage or interaction in the CRM, causing forecasting to be inaccurate and tedious, which leads to awkward boardroom conversations and missed targets. With better AI and understanding of a company’s process, this could be eliminated and there should be more certainty around what is the true state of your pipe. 

Automation of Routine Tasks

Sales representatives often find themselves bogged down by routine administrative tasks, such as data entry and appointment scheduling, not to mention hours of work spent researching prospects. AI has the potential to automate these tasks, freeing up valuable time for sales reps to focus on more strategic initiatives and revenue generating activities. AI-based automation tools that can perform semantic search ensures that sales teams can maximize their productivity and efficiency, leading to improved performance and morale, because let’s be honest, these are the most tedious parts of the job. 

Improved Communication and Collaboration

Effective communication and collaboration are the bedrocks of a successful sales team. AI-powered communication tools can transcribe and analyze sales calls, providing reps with actionable feedback to improve their communication skills. Additionally, AI can facilitate collaboration among team members, ensuring that everyone is on the same page and working towards common goals. Do we really need to be in a sales pit where we are hearing each other’s calls, where managers are trying to coach live, when notification can be sent, alerts can be created, and automatic coaching experiences could be delivered? Again I believe that it goes back to data, and the upfront commitment and work that went into setting up any AI tool up for success, without core datasets, the AI will just give you gibberish coaching and random alerts to deviations to the agreed upon sales process.

Enhanced Customer Experience

In today’s competitive market, providing an exceptional customer experience is paramount. AI plays a pivotal role in this area by enabling real-time customer support, personalized interactions, and proactive problem resolution. Imagine if the post sale experience can be delivered with the consistent voice of the rep that closed the customer, and with the proper training of the AI, this is certainly possible. Not only will the sales rep have more peace of mind because they can continue to fulfill the promise made during the sales cycle, customer success will be thrilled that the handoff between both teams are smoother than ever. 

AI is not just a technological advancement; it is a game-changer in the sales domain. Sales leaders must embrace this change and integrate AI into their sales stack to stay ahead of the curve. I’m sure there’s anxiety around this, but my view is that a more interesting time lies ahead.

AI is Here to Enable Sales People, Not to Make Them Obsolete

It’s a common misconception that AI aims to replace human roles, particularly in sales. However, it’s paramount to understand that AI is not here to make salespeople obsolete; rather, it is a tool designed to enhance and empower them.

From my expertise as a sales strategist and technologist, I can affirm that integrating AI into sales processes is a strategic move to optimize efficiency, improve accuracy, and ultimately, drive sales growth. AI algorithms, when correctly implemented, can analyze vast amounts of data at an unprecedented speed, providing salespeople with actionable insights that would be humanly impossible to gather in a timely manner.

Our approach to AI prioritization and personalization start with your existing research sources and relevant market signals and end with your CRM/automation tool filled with more actionable insights.

For sales leaders contemplating the adoption of AI tools, it is crucial to focus on how these tools can complement the human element rather than replace it. AI can automate routine tasks such as data entry, lead qualification, and initial customer interactions, thereby freeing up salespeople to focus on more complex and value-adding activities.

For instance, I have observed a notable increase in sales productivity when AI is used to analyze customer data to identify patterns and preferences. This enables salespeople to tailor their approach and offer personalized solutions, which significantly enhances the customer experience and increases the likelihood of closing deals.

To fully leverage AI in sales, it is imperative for sales leaders to invest in training and development programs that equip their teams with the necessary skills and knowledge. This not only ensures that the salespeople are proficient in using AI tools but also fosters a culture of continuous learning and adaptation. As a sales leader, it is your responsibility to ensure that your team is well-equipped to harness the power of AI, fostering a synergistic relationship between humans and technology to drive sales success.

How Sales Stack AI Can Consolidate Other Tools

In my years of experience working closely with sales teams and integrating various technological solutions to optimize sales processes, I’ve witnessed firsthand the transformative power of Sales Stack AI. It’s not just a buzzword; it’s a game changer, especially when it comes to consolidating numerous tools that sales teams rely on daily.

Firstly, let’s delve into what Sales Stack AI actually is. At its core, it’s a sophisticated suite of sales tools powered by Artificial Intelligence, designed to streamline and enhance various sales processes. From lead generation and prospecting to deal closure and customer relationship management, Sales Stack AI encompasses it all.

Now, as a sales leader, you might be juggling multiple tools to manage these processes. CRM software, email tracking tools, call logging tools, and sales analytics platforms – the list can be exhaustive and, quite frankly, overwhelming. This is where Sales Stack AI comes into play, serving as a centralized hub that can integrate and consolidate these disparate tools.

By doing so, we’re not just decluttering the sales tech landscape; we’re also ensuring that data flows seamlessly between different stages of the sales funnel. This interoperability is crucial. When information is siloed within different tools, it creates gaps in the sales process, leading to inefficiencies and missed opportunities.

Sales Stack AI, with its ability to consolidate tools, ensures that data is synchronized and accessible across the board. For instance, the information logged in a call logging tool can be automatically updated in the CRM system, providing real-time insights to the sales team. This not only saves valuable time but also ensures that every team member is on the same page, working with the most up-to-date information.

Furthermore, Sales Stack AI leverages the power of AI algorithms to analyze this consolidated data, providing actionable insights and recommendations. Imagine having a virtual assistant that not only helps in organizing data across tools but also guides the sales team on the next best action. This is precisely what Sales Stack AI brings to the table.

From a strategic standpoint, this consolidation of tools also translates to cost savings. Instead of investing in multiple standalone tools, sales leaders can allocate resources more efficiently, investing in a comprehensive Sales Stack AI solution that addresses multiple needs.

In my experience, the successful implementation of Sales Stack AI requires a thoughtful approach. Start by auditing the existing tools and identifying areas of overlap. Engage with the sales team to understand their pain points and areas where they feel existing tools fall short. This bottom-up approach ensures that the consolidation process is driven by actual user needs, rather than being a top-down mandate.

Sales Stack AI is not just a trend; it’s a strategic investment that can streamline sales processes, ensure data consistency, and provide actionable insights. As a sales leader, embracing Sales Stack AI is a step towards building a more efficient, data-driven sales organization. It’s an opportunity to consolidate tools, reduce complexity, and empower the sales team to focus on what they do best – selling.

Who Will Win As The Super Revenue Platform?

So in the end who will beat out the noisy sales stack and be leaps ahead of others? Some big well capitalized company or some small startup that’s going to be well timed enough where leveraging AI will be enough to capture all the needs of the customer?

My take is that every company has automations except automated cold calls. If someone can figure that out first they can be a winner very quickly, however, that will be also quickly copied or acquired so that win will be short lived. So automation is not the wedge in. Personalization in itself is also not super impressive in my opinion because so far I’ve not seen a LLM that do this better than GPT, and unless a verticalized model specifically is built for sales, I don’t see a differentiator, especially not for a startup that does not have the capital to R&D around the base model. I believe in the short term the companies who win in this space will be:

1. Companies that focus on finding leads that are industry specific or hyper relevant
2. Companies who can integrate different tools together to reap the strong suits of each
3. Companies that can build an end to end feature rich revenue platform but offered it at very low cost and use a PLG model to grow (like how Apollo competed with Zoominfo)

For 3), the likely winner here will be a venture backed startup that can move quickly and have an amazing product and engineering team.

For 2) the key in my opinion is to ensure that the product is intuitive enough where sales people can easily use, like an even more dumbed down version of Zapier, but for sales tools.

Finally for 1), what’s needed is a team that understands certain industries and tries to tackle a lead database for that specific industry, like a Zoominfo for [insert your industry]. 

We are taking a slightly different approach, at least for now we are leveraging AI to conduct prospect research that would otherwise take your sales reps hours to do. From trigger identification and decision maker analysis to semantic sentiment analysis of your research sources like earning reports, we want to help you identify the segment of buyers in your TAM that are most likely to take action on your product/service, and connect that research to your CRM so your sales team have something relevant to talk about, both in your outbound campaigns and actually face to face sessions.

LavaReach allows you to define custom intent signals and receive high intent leads.

About the Author

Max Woo is a multiple time founder with years of first-hand experience in B2B sales and revenue leadership. He has a consistent track record of helping companies experiment and implement outbound in SaaS and other industries. Throughout his career, Max has set up numerous outbound motions for the first time for companies that previously had not found success with sales led customer acquisition. Max also regularly shares his insights on LinkedIn in areas such as trigger based selling, AI enabled customer acquisition, outbound automations, prospect research, and more. He is dedicated to empowering sales leaders and individual sales people to not only become better professionals, but also learn to embrace unique strategies and build experiences that are tailor fit for the way their prospects can realize value, and he believes that empathy for customers and prospects always triumph against tactics that just closes the deal.