Worried about what your sales organization is missing? The modern AI-enabled sales stack will put those fears to rest.
BY DAPHNE HANSEN ILLUSTRATIONS BY LIVIA CIVES
“What is the airspeed velocity of an unladen swallow?” asks a guard to King Arthur in the classic film, Monty Python and the Holy Grail. “An African or a European swallow?” retorts the knight, because, well, you have to appreciate these esoteric facts when you’re a king, you know? He is on the hunt for the literal Holy Grail, and along the journey Arthur encounters all manner of unimaginable obstacles. His quest, and the impossible barriers he con- fronts as he presses toward his goal, is a light-hearted proxy for every company’s pursuit of the ultimate toolkit that will support revenue predictability, management, growth, and more.
To take the metaphor further, what the guard poses to King Arthur can have at least two different answers. (In reality, there are 74 types of swallows, so it’s not a fair question.) Ask sales professionals to define the makeup of their perfect sales stack, and the answers are as varied as the people to whom the question is asked. For some, building a sales stack means implementing a patchwork of technologies that produce reports and enable the basic functions of sales: Salesforce or Microsoft Dynamics for CRM; Tableau or Qlik to visualize data; WebEx, Zoom, or Skype to host conference calls; Google or Outlook for email and calendar; and so on. For others it’s a question of streamlining how sales operates with finance, legal, and marketing, like using Adobe Sign to manage contracts, and ex- pense management software like Concur or Expensify to keep track of expenditures; or of teamwork, like purchasing Anaplan to manage territory planning, Outreach for inside sales, and workflow management tools like Slack. The common thread is that everyone is trying to build a bespoke toolbox of technology that enhances agility, collaboration, predictability, and the buying experience — all leading to the holy grail of growth.
The very notion of building a perfect sales stack is challenging enough. Companies have had to suss out and license a set of tools that (in theory) helped their organizations manage pipelines, forecasting, and other basic functions of sales. But then along comes artificial intelligence and machine learning to raise the bar even further. How is one to keep up? Which also begs the question: Is AI/ML worth all the hype?
The promise of AI/ML has been widely touted; however, in practice it is often implemented as a secondary piece of the stack, something to simply tack on to existing functions. To date, this piecemeal adoption has limited the transformative impact its proponents laud. Companies appear to be choosing from two approaches: what we’ll call the “classical” sales stack, one where “AI” (often in the form of using predictive technologies on isolated internal data) is an add-on to the tool provider’s existing features; and the “modern” sales stack, in which functional tools act as sensors that contribute data, which can be aggregated and analyzed using AI/ML to infuse bigger and more meaningful insights into every sales activity.
Each approach lends itself to different priorities. In the classical stack, the goal is to help the organization make better decisions using data humans provide. The focus on “rigor,” the process whereby sales professionals are expected to enter their contacts and sales activities into CRM, for instance, is motivated by the desire to have people put more of what they know into technical tools that will be used to create reports or be manually manipulated for analysis. The theory is that by capturing what the humans in the organization know (as logged into CRM), every tool that’s added on can provide reports for managers that help them operate. Rigor has been the primary method of data collection in the classical stack, limiting productivity and the kinds of analyses that are possible. By extracting knowledge and contacts from the sales team, organizations can only learn from eyewitness accounts of the past and try to use history to plan for the future.
And therein lie the seeds of disruption. Past may be prologue, but it’s rarely predictive. John Wooden, the legendary college basketball coach, reportedly said, “Things turn out best for those who make the best of the way things turn out.” In sales, like sports, outcomes are not predetermined by past wins or losses — practice, training and in-game performance are the real secrets to success. Performance, resilience, and agility convert opportunities into wins.
Which brings us to the “modern” sales stack. Where the classical stack captures random snapshots of historical data, the modern stack is designed to be more dynamic. It starts from the premise that the world is constantly changing, so the goal of humans is to find optimal outcomes based on what possibilities exist at any given moment. Modern sales organizations ask how they can evaluate everything that is knowable, analyze it in real time, and provide relevant intelligence, answers, and guidance. The human’s role is to execute based on the best possible intelligence machines can deliver. Under the modern approach, human skill, agility, and empowerment are the secret sauce. The machines point people in the right direction.
“We see AI adding value across the entire revenue engine, from marketing to sales to customer success,” says Steve Silver, Service Director, Sales Operations Strategies at research and advisory firm, Forrester. “At a high level, AI-driven tools help sales by automating routine processes and tasks; capturing more data about customer and buyer interactions, even while reducing data entry workload; and improving decision making by providing analysis and insights. Combined with human oversight, context, and experience, these applications can help sales reps, managers, and leaders make better decisions about individual opportunities, next best steps, sales resource deployment, and pricing optimization.”
The ability to convert static data into real-time intelligence using technology is part of the modern stack’s flywheel effect (conceptualized in the book Good to Great, this effect captures the idea that individual decisions don’t create transformation, but rather fuel the momentum that creates a breakthrough), which explains why it is gaining acceptance. Dramatic sales trans- formations come from automating tasks (like rigor and forecasting), which radically improves productivity, but more importantly, provides the dynamic data stream that produces real-time, relevant insights. Companies that mesh AI/ML into their stacks adopt tremendous advantages: They are massively more productive, informed, and effective, which leads to exponential improvements in outcomes.
The Foundations of a Stack
The concept of a “stack” began with the introduction of CRM technology designed to replace the Rolodex and siloed knowledge (mainly held by individual salespeople) by recording basic information about leads, prospects, and customers, and the activities performed around them into a shared database. Since CRM’s arrival as a foundational technology, sales organizations have layered on myriad processes and tools to provide consistency in execution and insight into how revenue is produced collectively referred to as the sales stack. Until recently, the toolset required to be competitive was relatively standard: CRM and some basic reporting tools. The biggest strategic choice was whether to access functionality via the cloud or on premise software. Sales professionals self-reported activities and contacts by logging data into customized implementations of CRM. Given the challenge of manually collecting data, often rife with human errors, biases, and omissions, the classical stack is inherently limited by what insights it can generate using historical data and human opinions.
Companies that mesh AI/ML into their stacks adopt tremendous advantages: They are massively more productive, informed, and effective, which leads to exponential improvements in outcomes.
The modern stack requires much higher quality and breadth of data. That’s because in order for a machine to learn, it must be fed a constant stream of activities unencumbered by human bias. That feed combined with the right algorithms is the foundation for creating dynamic intelligence. As any sales operations team knows, data entered manually is not accurate, timely, or complete. Nor is internal data sufficient to provide context. AI offers the solution to human error and the time constraints of traditional rigor with something called Robotic Process Automation (RPA). Consider that the average sales professional spends almost one full workday a week inputting data — an activity that has negative ROI both in time lost selling and on the quality of the activities captured.
Removing the need for humans to record activities leads to an infinitely superior alternative a precise and efficient AI-enabled process. To make CRM really work, the modern stack starts with populating the database using RPA — a tool that is embedded into the operating system of the sales stack through the platform provided by Collective[i]. Collective[i]’s starter application, Intelligent WriteBackTM, not only automates the process of collecting data uploaded to CRM, but also captures it in a way that makes it AI-ready. Think of it as analogous to GoogleMaps: Today GoogleMaps uses your GPS to provide a near-perfect understanding of where you are. But imagine if a human could enter the wrong location on a highway — say, a wrong exit number. Every decision that flowed from that misinformation would be flawed whether made by a person or machine.
“AI will enable sales organizations to transition from linear to hockey stick/ex- ponential lifts in revenue and productivity that most organizations haven’t even dared to imagine,” says Mary Mellino, Senior Manager, Advisory Services, at EY. “For the same amount of effort and resources you can yield ten times improvement in performance. AI will enable sales organizations to employ a data-backed human-capital optimization strategy to maximize outcomes of its sales force by shifting where sales people spend their time and informing a new, more efficient division of labor across sales activities. Where sales reps spend their time and how they do their job will be fundamentally transformed.”
So while classical sales organizations require people to log data, managers to review and cleanse the data, and sales operations and finance to analyze the data (all before anyone starts to sell), modern sales organizations use their stack to create a flywheel effect. In this model, work tools generate data and an operating system like Collective[i] collects, augments, and applies AI/ML to convert and deliver in- telligence, which in turn fuels a more and more effective organization. As a result, the technology provides sales professionals with intelligence, managers with transparency, and operations and marketing with real data to formulate the strategies that lead to higher win rates, better marketing campaigns, and more.
The Next Level Stack
In classical sales, an organization is willing to sacrifice productivity to provide non sales functions with insights into the selling process. Consistency and predictability of revenue growth is the goal. Process takes priority, necessitating the adoption of technology solutions to solve how work flows to other business functions, like finance and marketing, which in turn requires yet more technologies that simply automate the workflow of imperfect processes. Forecasting is an example. Producing a weekly forecast requires huge human investments after which predicted outcomes are still only correct on average 46% of the time. Technology can replicate this process, but can only make incremental improvements to whatever correlations exist between past and future performance.
The modern sales stack focuses on optimization. It assumes that the future is unpredictable and outcomes are dependent on adaptation. Agility, collaboration, and productivity are key and supported by tools that create velocity — such as Outreach, Intelligence.com, Marketo, SalesLoft, Yesware, Salesforce Marketing Cloud, HubSpot, and many others designed to make the most of whatever the market presents.
According to Collective[i] co-Founder and Vice Chairman Stephen Messer, the key to the modern stack starts with changing the sales mindset from one that assumes fixed outcomes to one where outcomes can be optimized at any given moment.
“Where the classical stack starts with the question, ‘How can I predict outcomes?’, the modern stack asks, ‘How can I achieve the best outcome?’’ Messer says. “Collective[i] is core to the modern stack. We take a live stream of data from applications people use to work — email, calendar, conferencing, etc. and convert it into intelligence around opportunities and buyers. Our goal is to create a virtuous cycle of increased productivity and revenue growth. The more teams sell, the smarter the machine becomes and, by association, the people who receive it. The better the intelligence, the better the outcome.”
Collective[i]’s applications and network have pioneered the modern approach by offering online collaborative deal rooms to coordinate complex sales; an engine that surfaces useful connections at the moment they can provide value; and a continuous stream of intelligence in the form of alerts, relevant news, automated research, insights about sales talent (including predictive leaderboards to enhance coaching), and daily forecasts that leverage AI/ML to remove uncertainty and place a laser focus on optimizing possible outcomes.
A modern sales stack’s AI kicks in at the beginning of a sale, when a predictive model looks at all the data — past, present, and from a network — then makes the most accurate prediction. According to Messer, “Our goal is to be an enabler of success wherever and whenever possible. Our clients win based on a combination of science, knowledge, and human skill, not guesswork, gambles, and luck.”
Therein lies the key difference between classical and modern stacks. In the former, reports were the product of human opinions and past results used to predict the future. In the latter, the stack is engi- neered to dynamically analyze actual activities alongside changing market conditions to maximize revenue. The goal is to free humans from tasks and focus sales teams on closing the right deals at the right time.
Managing Cultural Change and FOMO
In C3.ai CEO Tom Siebel’s new book, Digital Transformation: Survive and Thrive in an Era of Mass Extinction, the same entrepreneur who created the market for CRM describes the transformation behind the modern stack that’s been replicated across every major industry and driven by data, AI, IoT, and other market forces. In the forward, former Secretary of State Condoleezza Rice writes, “History teaches that those who take the lead in technological revolution — which today’s digital transformation most certainly is — reap the greatest rewards.”
She’s right, but once you’ve modernized your stack and taken the leap, with full transparency of what your team is working on, accurate predictability, and a roadmap of where they are headed, you still have the challenge of getting people to embrace this new technology. Adoption should be logical, because so much is automated through AI, but if someone believes that 20% of their job is inputting data, change can be tricky. Consider the sales organization where rigor is required to get paid com- missions. Or the manager who has every Friday blocked to review pipelines. Or the sales professional who “knows” a deal will close only to find out the odds of closing are less than 20%. Implementing the mod- ern stack requires leadership to manage change on many levels — from actual activities performed by human talent to creating a metric of what it means to be successful as a sales professional, manager, operations leader, and executive.
In the past decade, a whole host of applications have hit the market to help companies produce basic reporting, enable communications, and manage finance, legal, and HR functions such as pricing, contract management, and compensation. In addition, the stack has been expanded to include marketing tools to score leads and distribute sales collateral consistently. Thus, the complexity of managing and integrating these tools has increased. Larger sales organizations often require an army of administrators and consultants to operationalize a growing number of technologies and applications. It’s safe to say that, at least in sales, the robots will not replace the humans.
It might sound simplistic, but so much of sales today is based on unknowns. There are so many variables that cause people to miss opportunities; in many ways, a sales person is like King Arthur’s knights on the hunt for something that may or may not exist. (If only those knights had AI and ML instead of coconuts and talking trees.) Adding AI to the sales stack and then retraining your team to trust and embrace new systems should foster better in-house relations and closure success.
Which leads us back to, what is the speed of the unladen swallow? With a little bit of AI (fine, some Googling), one can learn that the European swallow flies at about 24mph, but most of the other 73 swallow species travel between 21-40mph. Likewise, there should be far fewer unknowables on your quest for success, and having the ideal, customized sales stack wrapped in AI can bring that number closer and closer to zero. Which is approximately the number of closes a classical sales stack will eventually deliver. And the exact speed of a dead unladen swallow.
Collective[i]’s Stephen Messer predicts that in the future the idea of a missed opportunity will be distinct from a lost one. The former will only happen to companies who haven’t invested in optimizing data and the tools they use for working and basic reporting. Those companies are the most at-risk. For everyone else who have rid themselves of the uncertainty of the classical sales stack, the game will be about skill and coordinated execution. And they will win; they will find the holy grail.
Put another way, FOMO will be a thing of the past.