A few weeks ago, Facebook parent Meta Platforms saw a record-breaking one-day gain of $196 billion in market cap marking the largest single-day increase in Wall Street history. Two years prior, Facebook set a record for the largest one-day value drop in stock market history, losing more than $232 billion in value. Why does one of the most technologically sophisticated and well-resourced companies in the world have such unpredictable performance?

The short answer is sales. For decades, even the most advanced enterprises have struggled to forecast and manage revenue growth. The uncertainty of sales performance has plagued management, created risk for investors and shareholders, and left employees vulnerable to unexpected misses that can only be adjusted for with layoffs and cost cutting.

This problem has become acute. Sales has suffered through the unprecedented volatile market conditions (a pandemic, the “Great Reshuffle,” mass layoffs, remote work, inflation, war, rising oil prices, a banking collapse) where so-called “Black Swan” events are no longer exceptional but characteristic of the global and digital-first world.

As the Internet matured, consumer facing companies like Amazon and Netflix pioneered a new approach to goods and services building highly sophisticated engines that could both anticipate customer demand and adapt their offerings to meet it. In short, they changed the face of commerce by leveraging highly complex feedback systems designed to rapidly surface insights and personalize buying journeys.

The enterprise is now undergoing a similar transformation. A key advantage is that companies don’t have to build engines to unearth buying patterns or to optimize how their sales teams execute, they can plug into foundation models that both replicate human intelligence and produce insights that people can use to improve outcomes. Some, like OpenAI, are focused on language automating time consuming tasks like content blogging and prospecting, and another called Collective[i] leverages AI to automate tasks like data input, forecast revenue and help sellers adapt to the complexity inherent in B2B buying.

This type of AI is essential and solves for the current crisis enterprise sales organizations face. In the consumer world, business leaders are obsessed with the customer. Everything from the site to the products presented are designed to maximize revenue. B2B has historically lacked the technology to receive real time insights. Up until recently, collecting data into CRM was highly manual and heavily biased as a result. Without good data, the only way to monitor sales activities was via rigid processes supported by heuristic based technology.

A typical GTM (go to market) strategy involved mapping out a single customer journey and then creating guard rails for selling professionals to prevent deviation. Technologies emerged like conversational intelligence to monitor and enforce language used in the selling process, outreach tools that dictated the order in which prospects would receive emails, and reporting applications to measure the volume of activities performed. The result was that sales organizations became slaves to stages, scripts, and sequences that had no alignment to prospect’s patterns or preferences. In fact, 90% of B2B buyers don’t follow a linear path with an average of 11 stakeholders (double the number from a decade ago) adding to the complexity of tracking purchasing decision making.

Sales metrics assumed outcomes were predestined. Rules like having a pipeline with triple the number of opportunities than a person could engage with to the rule of 8 touchpoints to get a buyer interested became religion at the expense of time spent on training and monitoring seller skill and changing markets. Sellers became spambots measured on volume rather than skill with little thought to how this bombardment of emails and cold calls impacted the buying experience.

This approach is no longer working. Generating revenue today is more time consuming, risky, and expensive than it has ever been. Win rates have plummeted to 15%. Average contract values have dropped by a third with sales cycles that are 30% longer. Presumably due to remote work and the recent recession, enterprise buying teams are larger than ever. More damning is the response of buyers. They have had enough. More than 65% would rather go online than talk to a person.

For investors this poses a greater challenge. With the old formula for generating sales broken, companies can’t go public or sustain earnings growth. Costly turnover and layoffs add to the volatility despite signs that the economy is in recovery.

The fact is that, unlike in B2C, enterprise sales are complex and require human skill. Trained to expect personalization in their consumer lives, decision makers are demanding it in their business purchases. That’s why Generative AI is essential to help companies and their sales organizations adapt and thrive in the modern economy.

Sales forecasting is a good example. The traditional process of predicting revenue involved polling sales teams to assess the amount, likelihood (aka strong commit versus commit), and timing of the deals they hoped to close. Collecting and analyzing their predictions was extremely time consuming and heavily biased.

Traditional sales forecasting apps emerged to manage this process. The best of them used an older form of machine learning to analyze sales history in the hopes of benchmarking trailing outcomes against future performance.

These apps, however, can’t compete with AI. They predict revenue from the past and don’t account for unforeseen current events. They require human input which means that productivity is lost and bias enters the process. Entire teams sacrifice an average of one day a week to collect and manipulate the data. Finally, because of the manual inputs required, updates are at best weekly so there is a delay in spotting problems and addressing risk.

Generative AI enables radical improvements to top down, process driven, gut-based sales management. Newer forms of artificial intelligence exponentially impact productivity and help people focus on higher value activities. AI advancements today allow customers to leverage a more modern form of AI called deep learning. This type of AI requires massive troves of data so that it can spot patterns and convert them into actionable intelligence that guides human behavior. The benefit is that instead of analyzing seller opinion against historical performance, our models study current buying patterns against selling activities. It’s an approach that starts with how markets are operating now to align how companies operate to be in their best interest.

With a large and diverse enough input of buying and selling activities, forecasts can be automated increasing productivity by 10-20%. Produced daily and devoid of bias/manipulation, an AI-driven forecast allows sales teams to adapt to changing conditions both within and outside of their control. Like weather and traffic forecasting, sales forecast is entirely automated, scientific, and updated as external conditions emerge.

Imagine the chasm between a company using a Farmer’s Almanac or driver opinions versus an AI-enabled weather app or Google Maps (aka Waze) to manage decisions on when to ship materials or what routes to deliver products.

An automated and adaptive forecast is a game changer for employees, management, shareholders and investors. The productivity gains mean more time can be spent generating revenue versus predicting it. (Today sales organizations operate at ~30% productivity). Daily updates enable companies to adjust operations to meet fluctuating demand, intervening faster to save deals that are at risk, improving hiring and supply chain decisions, and adjusting products based on how markets are receiving them.

And that’s just one insight. Imagine the impact on sales having an on demand army of economists, MBAs, and market analysts constantly assessing market and buying trends to produce business specific insights that accelerate margin and revenue growth

AI-enabled work is the path forward. The principle behind Generative AI for sales is to replace process and technology with automation and intelligence. People are the differentiator because they are the highest form of personalization. Sellers are athletes who should be coached and trained to enhance their success. Athletes start with raw talent but rely on AI and coaching to hone those skills. AI won’t replace selling or people but instead the companies that lack the advantages it provides.

Source: Nasdaq