ChatGPT will transform the Sales Process

Generative AI models like ChatGPT have been making waves in the field of artificial intelligence, and their applications are not limited to just the consumer world. In fact, ChatGPT and other similar generative models have the potential to revolutionize the B2B sales process in several ways.

  1. Lead generation and qualification: Generative models like ChatGPT can be trained to analyze large amounts of data and identify potential leads that match the profile of your ideal customer. This can save sales teams a significant amount of time by automating lead generation and qualification processes.
  2. Customer profiling: ChatGPT can also be used to create customer profiles based on data such as demographic information, company size, and purchase history. This information can be used by sales teams to target their outreach more effectively and improve the chances of closing a sale.
  3. Content creation: ChatGPT can also assist in the creation of sales-related content such as proposals, emails, and presentations. The model can generate high-quality content in a matter of minutes, freeing up sales teams to focus on more critical tasks.
  4. Chatbots and virtual assistants: Generative models like ChatGPT can be integrated into chatbots and virtual assistants to provide human-like interactions with potential customers. This can significantly reduce the workload of sales teams and allow them to focus on more complex tasks.
  5. Predictive analysis: ChatGPT can be used to predict the likelihood of a sale based on various factors such as the customer’s behavior, purchase history, and demographic information. This information can be used by sales teams to prioritize their outreach and allocate resources more effectively.

In conclusion, ChatGPT and other generative models have the potential to transform many aspects of the B2B sales process, from lead generation and qualification to content creation and predictive analysis. By automating repetitive tasks and providing sales teams with valuable insights, generative AI models can significantly improve the efficiency and effectiveness of B2B sales teams.

How current and near-future Artificial Intelligence technologies will help Salespeople to find New Customers

Artificial Intelligence (AI) is revolutionizing the way salespeople find new customers. AI-powered tools are helping sales teams to automate repetitive tasks, gain insights, and personalize their approach to selling. As a result, salespeople are able to spend more time focusing on high-value activities such as building relationships with new customers.

One of the most significant ways that AI is helping sales teams to find new customers is by automating lead generation. AI-powered lead generation tools can scan the internet for potential customers and identify those that are most likely to be interested in a company’s products or services. By automating this process, sales teams can save time and focus on nurturing the most promising leads.

Another way that AI is helping sales teams to find new customers is by providing insights that can inform the sales process. AI-powered tools can analyze large amounts of data from a variety of sources, such as social media, website activity, and CRM data, to gain insights about potential customers. This can help sales teams to understand the needs of potential customers, and tailor their approach accordingly.

AI also allows for personalization in sales process by using various Machine learning algorithms like clustering, decision trees and other algorithms to segment and personalize communication, they can analyze a prospect’s behavior and past interactions with the company, and use that information to personalize their approach. This can help sales teams to build trust and establish a more meaningful relationship with potential customers, which can increase the chances of closing a sale.

Finally, AI-powered tools can help sales teams to predict which customers are most likely to make a purchase, and prioritize their efforts accordingly. By analyzing data such as purchase history, social media activity, and website behavior, AI can predict which customers are in the market for a product or service, and which ones are most likely to make a purchase.

In conclusion, AI technology is helping sales teams to find new customers by automating lead generation, providing valuable insights, personalizing the sales process and predicting which customers are most likely to make a purchase. As AI continues to advance, we can expect to see even more powerful tools that will help sales teams to close more deals and grow their businesses.

Words Used for AI are Really Different

Artificial intelligence, or AI, is a rapidly advancing field that has the potential to revolutionize various industries and aspects of our daily lives. However, there is often confusion around the terms that are used to describe the capabilities and characteristics of AI. Here, we will explore four key characteristics that are often discussed in relation to AI: intelligence, self-awareness, sentience, and consciousness, and how they differ from one another.

Intelligence: Intelligence is the ability to acquire and apply knowledge and skills. There are various ways to measure intelligence, including through aptitude tests, problem-solving ability, and learning speed. AI can be programmed to perform tasks that require intelligence, such as playing chess or recognizing patterns in data. In the early 2020s, AI has made significant progress in areas such as natural language processing, image and speech recognition, and decision-making, but it is still limited in its ability to understand and adapt to complex situations.

Self-awareness: Self-awareness refers to the ability to recognize oneself as an individual entity and to have a sense of one’s own thoughts, feelings, and actions. While some animals, such as dolphins and primates, are believed to possess a degree of self-awareness, it is a highly debated topic whether AI can achieve this characteristic. Some researchers believe that it is possible to build self-aware AI through advanced machine learning techniques, while others argue that self-awareness is a uniquely human trait that cannot be replicated in machines.

Sentience: Sentience is the ability to experience and perceive the world around us, including through our senses. This characteristic is often used to describe the subjective experience of being alive and aware. While AI can process and analyze sensory data, it is not currently able to experience or perceive the world in the same way that humans do.

Consciousness: Consciousness is a complex and multifaceted concept that refers to the awareness of one’s own existence and surroundings. It is closely related to sentience, but it also includes the ability to introspect, or to be aware of one’s own thoughts and feelings. While the nature of consciousness is still not fully understood, and it is a controversial topic whether AI can achieve consciousness, it is considered to be a highly advanced characteristic that is beyond the current capabilities of AI.

In the next 10 years, it is difficult to predict how AI will progress and if it will achieve these four characteristics. Some experts believe that AI will continue to advance and become increasingly sophisticated, potentially even surpassing human intelligence in certain areas. However, others argue that there are fundamental limitations to what AI can achieve, and that it will always be bound by the programming and data it is given.

In conclusion, intelligence, self-awareness, sentience, and consciousness are four distinct characteristics that are often discussed in relation to AI. While AI has made significant progress in areas such as intelligence and decision-making, it is still limited in its ability to achieve self-awareness, sentience, and consciousness. It is difficult to predict how AI will evolve in the next 10 years, but it is certain to continue to have a significant impact on various industries and aspects of our lives.

How LinkedIn uses AI

LinkedIn is a business-oriented social media platform that connects professionals from around the world. One of the key features of the platform is its ability to help users find new customers and business opportunities through the use of artificial intelligence (AI).

LinkedIn uses a variety of AI-powered tools to help users connect with potential customers and grow their businesses. One of the most important of these tools is the platform’s search function, which uses natural language processing (NLP) and machine learning (ML) algorithms to understand user queries and return relevant results. For example, when a user searches for “marketing agencies in New York,” the search algorithm will use NLP to understand the intent of the query and return a list of marketing agencies based in New York City.

Another important tool that LinkedIn uses to help users find new customers is its “People You May Know” feature. This feature uses machine learning algorithms to analyze a user’s network and suggest new connections that might be useful for the user’s business. For example, if a user works in the tech industry and is looking to connect with potential customers, the algorithm might suggest other tech professionals in the user’s local area who may be interested in their products or services.

LinkedIn also uses AI to personalize the content that users see on the platform. For example, the platform’s news feed algorithm uses machine learning to analyze a user’s interests and browsing history to show content that is most relevant to the user. This way, users can discover relevant content and stay updated with the latest industry trends and information that can help them to find new customers.

In addition to these features, LinkedIn also offers a feature called Sales Navigator, it is a paid add-on service for professionals in sales and business development role. Sales Navigator uses artificial intelligence to help sales professionals prioritize leads, build and maintain strong relationships, as well as generate new leads. It helps user identify the best lead opportunities and reach out to the right decision makers in companies.

Overall, LinkedIn uses a variety of AI-powered tools to help users find new customers and grow their businesses. By using natural language processing, machine learning, and other AI technologies, the platform is able to provide users with highly relevant and personalized information that can help them to connect with potential customers and close new deals.

Myth: Artificial Intelligence (AI) in Business is Expensive and requires Expensive Experts

In uncertain times, business executives are starting to focus their video-chat enhanced abilities on driving efficiency in their organizations. For many of them it is – all of the sudden – do or die time. Many have been hearing about Machine Learning (ML) based AI in recent years, but simply haven’t had the time to explore the topic. Now, for many execs, AI is part of their contingency plan going forward.

But how do organizations best apply AI? Is it like butter, just spread it on?

Not really. In fact, there have been many well-documented enterprise AI projects that have failed miserably to net results and were also quite expensive. But it doesn’t have to be that way. A bit of forethought and sound choices before embarking on AI projects can prevent disasters. Many times, you don’t have to start from scratch – there are pre-done packages for AI.

In fact, one of the biggest decisions you may want to drive your team towards is “do we need to develop the AI ourselves from scratch or are there pre-done solutions we can more easily apply to our business?”

The breakdown of those two extremes will be reviewed here.

1. Custom developed AI projects for business problems

Even if a company is willing and able to hire an expensive Data Scientist to analyze the business problems and develop Machine Learning Models, getting the ML Models into production is non-trivial. A Machine Learning Model is a combination of the program coded algorithms and the data-set that is used to train the model to perform a certain way when used on other data. An example could be a model pointed at existing HR data trained to find the characteristics of high performing employees. Hopefully a model that will continue to learn as new data comes in.

That new Data Scientist you just hired will probably have success in getting to know your company and finding potential business problems to solve. Any of them worth their salt will also be able to develop and train a model that runs successfully for a given solution, on his or her laptop, within a few months. But after that, things often fall down. When the model is put into production on company servers, it rarely performs the same way as that stand-alone pilot. Often this is due to other constraints and interactions in the computing environment or just because the company does not have enough of the right kind of data to prove the model out in the real world. The amount of relevant data and data cleanliness (the right, usable formats) are the most often cited reasons AI projects fail.

Another consideration for implementing AI is where it will be deployed. Computer hardware architectures for AI are different than traditional Database or ERP hardware solutions. Computer servers with Graphical Processing Units (GPU’s) deployed with MANY servers (nodes) are often required to run these deep learning models in training and production mode. An alternate to buying hardware that may save money is “renting” AI enabled resources in the cloud, from places like Amazon (AWS) or Google (GCP). But as AI becomes a core part of your company’s efficiency (hopefully it will) your rental costs can get quite high as well.

When you get through those challenges, many companies realize too late that AI models will have to be carefully maintained, which is difficult. Even if the AI problem-solving model works well for a month, that doesn’t mean it will continue to perform well. Environments change, your original problem elements change. Enterprises job titles that maintain developed computer programing code are known as Developer Operations – or Dev Ops folks. The trouble is most Dev Ops people are not trained on ML Models and their maintenance – which is quite different than standard software development environments.

This is why Machine Learning Operations – or ML Ops – is a very new, very expensive and a very highly in-demand employee position for you to try to hire. Between acquiring Data Scientists, expensive computer hardware or cloud time and ML Ops people – this can all truly amount to multiple millions of dollars spent.

Given the above, you may be surprised to learn that in some circumstances, Custom Developed AI is actually an acceptable and required risk. But only embark on Custom Developed AI if your business’s entire usefulness is driven by the AI, i.e. if AIis your main competitive advantage. Uber or Lyft come to mind. Those two companies are more than just about the recent innovative concept of riding in a stranger’s car, they are about unique custom AI capability. Or, if AI is not your core business, but you still need unique AI solutions that your competitors cannot easily buy off the shelf – Custom AI may be acceptable. There are ways to manage projects to avoid failure and minimize the highest costs. Custom AI can succeed with the proper planning and budget.

2.  Pre-done AI Solutions – there are good ones that are simple and lower cost

It is extremely important to first understand what your goals are in applying AI to your business – what outcome do you want? Have other companies solved this problem with AI? Is there anything on the market that already fills this need, either within an existing software suite you already license or with an AI niche company?

The easiest way is to implement AI, if you can, is a pre-done, packaged service from a focused company or an extension of your existing enterprise software. An example of an existing solution suite that has added AI recently is the Einstein modules from (See previous Blog).
Licensing a cloud-based, off the shelf AI solution is often simple, comparatively inexpensive and very common now. Company departments are able to budget for this on their own as of late. (Make sure your IT department approves the security and privacy standards, of course). This is exactly analogous to the way software procurement has always been. Buying a packaged software solution was almost always preferable to creating your own custom program, except for the above stated exceptions.

There are many business functions in many business horizontal activity areas that can benefit from AI.
Taking one corporate department as an example – a company’s Sales Dept – here are examples of cloud-based pre-done AI capabilities you may be able to make use of now:

  • AI powered Account Based Marketing (ABM) platform
  • Smart calendar scheduling
  • AI filling in CRM actions for you while you are on the run
  • AI that helps targeting prospect companies
  • Tracking all communications and the relationship strength in your account interactions
  • AI capturing and outlining meeting notes without you typing
  • Automating lead generation and scheduling follow-ups with AI-as-service
  • AI platforms for data-driven customer engagement using deep machine learning
  • Sales Acceleration by speeding up dialed customer phone calls
  • AI accessing the right people at the right accounts, at the right time, with the right messages
  • Website messaging using AI to scale conversations for more Sales Accepted Leads (SALs)
  • AI finding Ideal Customer Profiles (ICP’s) and Accounts
  • Ai driven Sales Playbooks and the execution of them. Like a remote AI SDR
  • Lead Nurturing via Email, Web Chat and SMS
  • AI generating personalized marketing messages in email, social accounts, advertisements and other channels
  • Finding hard-to-find B2B Data with AI. Penetrating the right target accounts with differentiated data
  • Multi-Industry Assistants using voice, text and any channel to keep the service interaction going

For more detail see AI for Sales


Artificial Intelligence assists humans with business in ways that can surprise and delight us. Putting some thought into the Make or Buy AI decision will save money and accelerate time-to-benefit. Before considering the tougher road of custom AI development, first exhaust looking for the low hanging fruit by considering pre-done, packaged AI services. There are a number of pre-done AI services for many business horizontals including (but not exclusive of) the Sales Department.

Salesforce Einstein

Bolster is not a CRM consultant or CRM purveyor but CRM does play a key role in the services that are discussed with client companies. Some of those conversations are centering around Salesforce Einstein, because Artificial Intelligence (AI) and Machine Learning (ML) strategies are a core focus for Bolster. ML driven AI is the technology that the optional Einstein extension modules are constructed of.

These are AI services that snap into different versions of All Einstein components do something slightly different and all are additional money per user.

Last year at Dreamforce (’s uber-conference), I spent a sizable amount of time jawing with the Einstein specialty group. Einstein seems to really take advantage of the part of AI that is bolstered by Predictive Analytics. I discuss the role of Predictive Analytics in prior blog entry – AI Simplified. Many of the Einstein modules analyze what has happened at your company and predicts what will or could happen in the future.

SFDC Einstein can do that kind of predictive work in the following areas of your business:
Sales (of course)
Analytics (stand-alone)

Some of the modules require data – meaning you have to have a certain number of contacts, closed opportunities or cases in Salesforce. Others do not.
We will mostly address thoughts on Einstein capabilities in Sales and a bit on Marketing functions as well here.

Lead and Opportunity scoring are two key predictive capabilities for Einstein. The Leads in your Salesforce CRM are analyzed based on your data and criteria to help reps decide who are the strongest customers in Accounts to work with over time. Opportunities that you have in Salesforce are deals that feature Leads, where the goal is moving the Opportunities over the goal line. Einstein will score Opportunities based on the shorter-term reward of revenue generation now, whereas a higher Opportunity score means reps work on those first if quota is to be made. For the long term, finding the best Leads to work with, at this moment, using Lead Scoring creates future Opportunities.

Similar to Scoring, Insights deals with Accounts and Opportunities. And while Opportunities wouldn’t exist without Accounts, Einstein separates them to help salespeople understand the information around developments, people and potential deals. With Account Insights, Einstein pulls in events in the external world with relevant news articles every day about your target Accounts. For Opportunities Einstein crunches fields that you have in Salesforce plus organizational email and calendar data (See Activity Capture). Einstein’s Machine Learning models will offer you what they call Predictions, Key Moments, and Smart Follow-Ups to apply to the Opportunity to move it towards close.

Einstein uses all of a company’s historical CRM data to reduce the manual motions and inaccuracies that takes place in sales forecasting. Using machine learning to spot problems, it can notify management when irregularities are uncovered. This kind of pattern spotting can also help spot best practices from the best, most effective reps. Models are rebuilt each month, and the forecast is recalculated through the model approximately every six hours. It’s very easy to see what revenue has already closed, what new revenue is expected, and how much revenue is expected to fall out of the forecast. Previously, this has all been guesswork.

Milk your Inbox / Calendar
Einstein Activity Capture is a key piece of this overall AI strategy. This is where AI meets Robotic Process Automation (RPA), in a way. I won’t delve deeply into RPA here; it may be a good future blog discussion. But the short story is RPA code is automating repetitive tasks in a company, like opening emails or spreadsheets, extracting the order details and filling out a sales order – on its own. It is more like keyboard macros than AI, but is viewed by many as a stepping stone to AI. In this regard, Einstein Activity Capture similarly scrapes relevant information out of connected email and calendar accounts at an organization and matches external info with Accounts, Leads and Opportunities. Activity Capture is actually required if several of the benefits of Einstein are to be used. Activity Capture will allow Automated Contacts to be added to your CRM and Activity Metrics relies on Activity Capture to analyze the effectiveness and timeliness of your rep’s activities.

Einstein Voice – Talk to Salesforce – Beta 2020
As of now, Einstein Voice is an umbrella product branch being tested in beta that allows a capability to use human voice to interact with Salesforce. It is divided into three components; this may change as it rolls out in 2020. Voice Assistant will be quite familiar in a world of Siri and Google Assistant. From within Salesforce, this Einstein feature lets folks view and manipulate Salesforce content by talking on a device such as a smartphone or speaker. For corporate custom developers, Salesforce offers Einstein Voice Skills – a kit allowing them to customize voice commands and actions for each role in the organization. This is similar to Alexa Skills for Amazon’s smart speaker. Call Coaching listens to sales rep’s conversations in order to spot patterns that are successful in winning business and gives management the ability to implement best practices.

Analyze this – Einstein Sales Analytics
Sales Analytics is just as it sounds. Using the overall capabilities that software has for Analyzing, this particular Einstein module provides customizable, built-in dashboards to relay the state of Sales in many ways. There’s something for everyone to be analyzed on – Sales Reps, Sales Managers, Sales Operations and VP’s of Sales. How are they doing on their KPI’s? Quota attainment, pipeline generation, bookings, sales next steps and sales cycles are laid out in easy to digest format.

Marketing Insights
The Marketing department gets some Einstein as well, although I won’t dive deeply here. The simplified overview is AI is applied to Customer Journey mapping, Content generation and many marketing Analysis tools are included too. Also, an organization’s Sales Engagement Scoring and Frequency are enhanced with AI by Einstein for Marketing.

Knowing AI for Sales and the solutions that live there very well, Bolster has a final take on the progress of Salesforce Einstein. The fact that the AI assisting technology is coming from the most established company in the CRM business is significant. The theory is that it will work well and integrate well because the AI is not from a dozen different companies that may or may not integrate well with Salesforce. Way back when, there were separate spell checkers that you could buy and run in your word processors. But when it was well integrated into Word by Microsoft, well that was a good thing. One less separate thing to run.
With that said, you have to ask: Are we at the point where the most useful, cutting edge AI is being done by the king of CRM? There are many AI services that work very well with and integrate smoothly into any CRM including Salesforce, so you don’t have to put all your eggs in one software basket. Also be aware that The King in a category can always charge a premium and Salesforce is no exception. Each of the components discussed here from Salesforce have very good competitors and many can be had for less money. AI for Sales shows a small part of these capabilities and in the next Blog post we will look at the difference between doing AI yourself as an end user (huge task) vs. off-the-shelf AI services.

Artificial Intelligence (AI) Simplified

The front page of has a definition of Artificial Intelligence (AI). Interesting to see how people change the way the term AI is applied over time, as things change. The best way to illustrate this is to consider that most people don’t think they’re “using AI” when searching for information on the web or using Google Maps. Those services both do use lots of AI. If you showed the current incarnation of Google Search and Maps to a computer scientist in 1995, they would be screaming “It’s AI! AI I tell you!”

There are many overly complicated definitions of what AI is and does. Too clarify up front, Machine Learning is the main way to achieve what people consider AI today. I’ll discuss the general importance of Learning here, among other things. That is part of breaking down a simplified understanding of AI. This diagram is for the simplification:

Diagram: Artificial Intelligence (AI) Simplified

Outcomes – Autonomy and Human Co-communication
Taking it from the top first to discuss Outcomes. Why are we using AI? Many of the overall highest-level outcomes that we talk about getting out of AI boil down to Autonomy and Co-Communication (between computers and people). Autonomy in AI means the system can operate and make decisions on its own. While safety is a strong consideration with Autonomy, understand that many of our uses of AI today safely make autonomous decisions. In our example, a Google search partially uses AI in the system to hunt and deliver relevant information for you – not a person behind the curtain. That is an example of Autonomy. Co-Communication between people and machines is not the only other high-level output of AI, but it is a very important one. We exchange information today with systems by text, voice, images, video and even Virtual Reality. Natural Language Processing (NLP) and Natural Language Understanding (NLU) are examples. Accuracy in that Co-communication and new ways to communicate are improving rapidly.

Pattern Recognition
Now going straight to the bottom, the core computer capability that starts creating theses AI outcomes is statistical Pattern Recognition. It really starts with Math. AI IS Math for the most part. AI needs a pattern to grab on to and pattern recognition creates the comparisons for grabbing on. An example lies in Vision, where there has been much AI progress and excitement over the last 10 years. When a Machine Learning Model needs to detect a cat’s face, it first looks for edges in the photo. An edge is easy to spot because there is a contrasting pattern with an edge. The cat’s ear might be dark, for instance and the background behind it might be light. That is an edge. The edge is how it begins to recognize the ear then the cat (or a number or a letter – simplified). Ideas found in text or video have edges that computers can grab onto as well. The idea of an AI computer winning in a video game has more than played out in labs and on our phone apps. The AI in a game is not fighting the guy with a gun, it’s fighting the pixels and math that make up that guy.

Analytics and Prediction (Predictive Analytics)
Past and Future
Much AI doesn’t even get to the level of Autonomy and Co-Communication. In fact, much of what companies call AI today is really either Analytics or Prediction. This is fine, whether it’s true AI or not goes back to that discussion about the definition of AI moving over time. Analytics and Prediction probably would have been called AI a few years ago. Typically, they both use a lot of data (Big Data, in fact). Analytics tells you what happened in the past and Prediction, well, tells you the future. Using Pattern Recognition to find trends in data, like sales over time or the location of your best customers, companies can judge the past. Similar techniques can use that history to make decisions about the future. Most importantly, Artificial Intelligence services or capabilities are often built on top of Analytics and Prediction programing.

Learning / Understanding is crucial
In order to get the Outcomes displayed in the diagram without someone programing every step, every word, every decision the machine makes – you need Learning and Understanding. Learning through machine learning (ML) and deep learning (DL uses human-like neurons) is a good indicator that a technology is actually AI today vs. just computer programing. It is crucial and the goal of Learning is Understanding. But can machines really understand today? They can in a mathematical, pattern recognition sense. It will be years before they have enough sensory inputs, memory and mental models to really have context. Although they can tell you which one is a cat today, they don’t know what a cat really is. Does that mean AI cannot be useful? Absolutely NOT! AI in Business has already paid huge dividends for companies willing to try. For instance, AI for Sales has increased efficiencies all over the world and uncovered huge opportunities, even in these early days.