AI is transforming Sales

AI is disrupting sales across the globe in all sectors where field experience and gut feel have played a major role in the past.

AI crunches numerous variables, it enables data-driven decisions and doesn’t forget anything. Every aspect of enterprise tech is already or very soon going to be AI-driven. Think of all the common examples we already know about – recommendation engines, voice recognition, Uber, Gmail, Food Apps, Google Maps – that depend on algorithms that predict what to eat, or which road to follow.

Massive gains will be realized in industries such as risk management, genetics, sales, advertising, and marketing as these sectors need data processing and predictive technologies on a massive scale. It’s not whether AI will impact our business and processes. It already is impacting at various levels and to various degrees. The more pertinent question is how it is impacting.

AI is disrupting Sales across the globe in all sectors where field experience and gut feel have played a major role in the past.

“We are about to experience the equivalent of a major tectonic shift where the functional plates of sales, marketing, and technology will shear and, in some cases, smash against one another. Functions that were once the domain of salespeople will be transformed, subsumed, or obliterated. …Virtually every stage of the traditional sales pipeline is now ripe for disruption as companies enthusiastically invest in AI applications that have the potential to sharply enhance critical sales functions and thus aid in development of more efficient sales processes.”
– Victor Antonio, Author of Sales Ex-Machina

Let’s think of all things being done in a sales process – we are bound to conclude that almost all aspects of sales are going to be either aided or driven by AI in the coming days:


  • Creating Lead Lists
  • Account Targeting
  • Prospecting
  • Reaching Out
  • Lead Engagement
  • Social Proof


  • Forecasting
  • Sales Enablement
  • Product Knowledge


  • Pipeline Management
  • Customer Touchpoints
  • Proposal Creation
  • Price Optimization
  • Deal Management

Post-Sales Service:

  • Relationship Management
  • Upsell & Cross-sell
  • Nurturing – Meetings / Engagement
  • Customer Success

Sales Performance Management:

  • Activity Management
  • Call Analytics
  • Customer Interaction
  • Sales Process Optimization
  • Gamification

AI creates a Collective Sales Consciousness

A great salesperson is rarely born. He is a creation of his drive to reinvent himself and be a provider of solutions to his customers. He has to be a trusted advisor to the customer while keeping his company’s commercial interests at heart. Such a mission is achievable only through the experience, of calls, of meetings, of data keeping and organization, of prudent decision-making, sweat and grit.

Today, with the power of AI, the learning of all salespersons in the organization is available to one salesperson and vice versa. A collective sales consciousness can take shape. A powerful paradigm indeed. It takes a couple of months before an inside sales person could be trained. However, AI can brilliantly close this gap by helping a newbie sales rep to jumpstart performance.

AI Makes Feeding the CRM a Breeze

Jeb Blount, founder of Sales Gravy and the well-known author, identifies that salespersons cite the administrative work to update CRM as one of the reasons for making fewer sales calls. Curiously, the more successful a salesperson is, more the work with the CRM. His point is that while star performers manage the administrative work very well, underperformers cite this as the reason for underperformance.

However, AI has brought new solutions. AI-backed CRMs now scour voice recordings and emails to update the CRM, schedule meetings, send automated emails, search the web to gather customer intelligence and so on. Today some AI applications can summarize an hour-long sales call into a 5-minute brief. It saves a lot of time.

Chat-enabled CRMs help the salesperson update with ease using only the chat window. The chat interface tucks away the CRM and permits bite-sized updates using small devices and in crowded spaces. AI-supported voice-enablement now allows hands-off updates and data retrieval from the CRM. What’s more, AI can not only analyze sales calls but also give real-time feedback on ongoing conversations in sales and other sectors such as contact center conversations.

AI Helps Focusing on the Right Leads: Lead Scoring

Traditionally, marketing and sales have together decided how to rank the leads that come in. A similar practice exists for account scoring as well. Lead scoring supports many decisions surrounding sales such as how much time, effort, and what channel should be employed to convert the lead into a closed deal.

Traditional lead scoring is based on a set of rules: If a customer engages twice in a week and asks for marketing literature, the lead is scored as “hot lead.” The classification is based on the business’s experience. However, digitization, social media, and the web have all brought in a bounty of data, new customer behavior and engagement opportunities, and the attendant complexity into lead scoring. It is very reasonable to say that the traditional lead scoring system is broken.

Enter AI. AI can collect and evaluate innumerable parameters, score leads, prioritize allocation of resources to these leads and suggest actions for conversion, follow up, or long-time nurturing. Thus, the entire marketing and sales system is geared to convert the best leads that are highly likely to convert in the immediate buying windows without losing sight of long-term prospects. Also, the AI-driven platforms can detect and alert Sales when once dormant leads start actively engaging with the business.

AI Enables Intelligent Price Optimization

Unwanted, deep discounts extended to win large deals are often not well justified beyond the account size and customer brand value. Therefore, organizations cannot experience the lost sales value that can’t be measured or minimized which are far worse than the lost sales values.

Pricing is highly complicated if it has many moving parts that are interrelated. Producing variable quotes with complicated pricing requires skills of a higher order. However, AI can help the sales teams produce appropriate variable quotes while rationalizing pricing. Price optimization with AI considers the number of competitors, time of the quote (Q1 vs. Q4), territory of the customer, client’s annual revenues, and any number of variable factors with dependencies.

According to McKinsey Research, price optimization alone can improve return on sales by 4 – 10%.

AI Automates Sales Forecasting

Sales forecasts are often viewed with some amount of suspicion and skepticism. A poll by Clari revealed that only 14 of sales reps meet more than 70 of their quotas. The over-optimism or subjectivity built into sales forecasting has always been a challenge to address. But not anymore.

With AI-based tools, a salesperson can always compare his forecast against what AI shows and adjust accordingly. This facility will also level the playing field between star performers and other salespersons. If there is enough data, it can be crunched to give more realistic forecasts by AI and remove human bias.

AI Ensures 100% Follow-up and Personalization

According to DestinationCRM, only 12% of sales people make more than three follow-up calls whereas a customer needs a minimum of 7 reminders to remember the salesperson and the service. AI-based CRM solutions can follow up all leads, and with appropriate content. It will automate drudgery and notify when action needs to be taken.

AI Helps Hyper Segmentation

The traditional CRMs offered broad categories of segmentation based on demographics, income levels, purchase history and so on. However, AI-assisted CRMs, with their capabilities to crawl the web, read social signals, and predict customer actions, allow for hyper-segmentation. Companies can build data clouds that can provide thousands of attributes to build the segmentation from. The hyper-segmentation capabilities are expected to lead customer acquisition, retention, and discover and expand into new market segments. Today the world knows how Harley-Davidson created history the end of 2016 by increasing sales exponentially through the adoption of an AI platform. The platform builds look-alike customers by automatically detecting the characteristics of the existing customers. The results were a lot more than heartening. The leads increased by 2,930%, and 50% of the leads are from look-alike customers.

AI Adoption is Rapid but is Still Work-in-Progress

With more solutions appearing on the horizon across various sectors, AI adoption is rapidly spreading. However, the path to adoption and usage has some dependencies:

  • AI solutions are data dependent solutions. The data presented to an AI solution should be acquired, scrubbed and modeled. Without this preparation, we can assume that AI can only be of moderate or little help
  • Also, human resources in AI are highly sought. Small and medium-sized companies may have challenges in setting up their AI-driven marketing and sales internally. While this is an opportunity for cloud-based SaaS solutions, the companies who are required to put up in-house teams are bound to face many challenges. And only the resourceful ones will survive in the long run.
  • When a new sector or service is taking shape, the whole sector has few data points to share. In such scenarios, AI-based solutions may not be of much help. For example, while the whole world knows where Industry 4.0 will lead us to, it is not yet mainstream, and hence customer data available is limited as well. So, AI solutions will be forced to stay lean.
  • While AI offers to make sense of data, often organizations have limited sets of data. So, the challenge often is not the volume of data, rather the lack of it. Now, will and can the AI solutions rise to the occasion each time they are used?
  • A new salesperson can be trained in selling as it is specific to a sector but not in what makes sales originate. That is, AI cannot point to sales pipeline development.

    Pipeline development by AI is possible only when the data from many organizations, technically known as data lakes, merge. This is of the next order of collaboration among organizations. For example, if you sell interior solutions, the data from a real estate company would be the perfect input for your business’s pipeline development. Some companies are actively working in this space developing technology to normalize the incoming data.

AI and Business Niches

While so many different aspects of sales can be AI-powered, today we are at a stage where each niche in the sales process has been identified as a potential business by providers of AI-based Sales technology. So, instead of a one-size-fits-all blanket AI solution, companies should pick and choose AI-based solutions for the parts where they have the maximum pain-points. This allows them to multiply the impact of new technology in a way that is meaningful for them.

Eventually, an entire stack of AI-powered tools will be built, radically changing the skill-sets and the nature of the sales workforce forever.

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