Salesforce adds Generative AI Support Across its Application Portfolio

Another AI added example.   Worth examining closely. 

Salesforce Adds generative AI support Across its Application Portfolio   By Paul Gillin

Salesforce Inc. today is jumping aboard the generative artificial intelligence train with the addition of large language model support across its portfolio of applications.

Generative AI can now be incorporated into the Einstein artificial intelligence platform, Salesforce Data Cloud, Tableau analytics, Flow process manager and MuleSoft integration toolset for queries, code generation and business process automation assistance.

Generative AI has been popularized by last fall’s release of ChatGPT by OpenAI LP. In addition to responding to queries in natural language, ChatGPT can to generate and analyze code as well as extract insights from raw data.

Trusted and extendable

An important feature of the new version of the Salesforce AI Cloud, which is also known as Einstein, is the Einstein Trust Layer, which the company said makes its cloud platform open and extensible to allow customers to choose their preferred LLMs from Salesforce and other companies. The Trust Layer is said to prevent proprietary data from becoming part of public models, improve the quality of AI-generated content and integrate generative AI responses into business processes while ensuring that data privacy, security, residency and compliance regulations are met.  ... ' 

Salesforce is Playing AI too

(Will further examine) 

Salesforce doubles down on generative AI with Marketing GPT and Commerce GPT

Shubham Sharma  @shubham_719

June 7, 2023 9:07 AM

Today, CRM giant Salesforce debuted two new generative AI products. Announced at the company’s ongoing Connections conference, Marketing GPT and Commerce GPT will power Salesforce’s Marketing Cloud and Commerce Cloud, enabling enterprises to remove repetitive, time-consuming tasks from their workflows and deliver personalized campaigns and shopping experiences, at scale. 

Want must read news straight to your inbox?

The news follows last month’s launch of Slack GPT and Tableau GPT and highlights Salesforce’s growing focus on AI, where it is moving the needle to make sure generative AI sits at the heart of its core products and services. However, it must be noted that these products’ features are not available right away and will roll out in phases, starting in summer 2023.

How will Marketing GPT and Commerce GPT help?

Driven by the Salesforce Data Cloud, which hosts customer profiles comprised of data from all systems, and the Einstein GPT generative AI assistant, Marketing GPT allows enterprise users to interface with their Marketing Cloud system using natural language.

Join us in San Francisco on July 11-12, where top executives will share how they have integrated and optimized AI investments for success and avoided common pitfalls.

Register NowTo start off, the company said, Marketing Cloud users will be able to put in natural language prompts to query the Data Cloud profiles and identify new audience segments to target. They could also ask Einstein GPT to write or modify personalized emails — complete with subject lines and body content — for campaigns, or use Typeface within the platform to create contextual visual assets.

That’s not all.

In addition to generative functions, the marketing cloud will get AI-driven segment intelligence and rapid identity resolution capabilities.

The former will automatically connect first-party data, revenue data and paid media data from Meta and Google for a comprehensive view of a campaign’s performance, relative to the audience segment targeted.   ..... ' 

How Generative AI Will Change Sales

Thinking of testing this in the real world where enough data exists.  

How Generative AI Will Change Sales    by Prabhakant Sinha, Arun Shastri, and Sally E. Lorimer, March 31, 2023

Sales teams have typically not been early adopters of technology, but generative AI may be an exception to that. Sales work typically requires administrative work, routine interactions with clients, and management attention to tasks such as forecasting. AI can help do these tasks more quickly, which is why Microsoft and Salesforce have already rolled out sales-focused versions of this powerful tool.    close 

Last month, Microsoft fired a powerful salvo by launching Viva Sales, an application with embedded generative AI technology designed to help salespeople and sales managers draft tailored customer emails, get insights about customers and prospects, and generate recommendations and reminders. A few weeks later, Salesforce (the company) followed by launching Einstein GPT.

Sales, with its unstructured, highly variable, people-driven approach, has been a laggard behind functions such as finance, logistics, and marketing when it comes to utilizing digital technologies. But now, sales is primed to quickly become a leading adopter of generative AI — the form of artificial intelligence used by OpenAI (the company behind ChatGPT) and its competitors. AI-powered systems are on the way to becoming every salesperson’s (and every sales manager’s) indispensable digital assistant.

Sales is well-suited to the capabilities of generative AI models. Selling is interaction and transaction intensive, producing large volumes of data, including text from email chains, audio of phone conversations, and video of personal interactions. These are exactly the types of unstructured data the models are designed to work with. The creative and organic nature of selling creates immense opportunities for generative AI to interpret, learn, link, and customize.

But to realize the true potential, there are hurdles and challenges to overcome. Generative AI must be non-intrusively embedded into sales processes and operations so sales teams can naturally integrate the capabilities into their workflow. Generative AI sometimes draws wrong, biased, or inconsistent conclusions. Although the publicly accessible models are valuable (hundreds of millions of users like us have already used ChatGPT to query the knowledge base on practically every topic), the true power for sales teams comes when models are customized and fine-tuned on company-specific data and contexts. This can be expensive and requires scarce expertise, including people with significant knowledge of AI and sales. So how can sales organizations harvest the value without wasting energy on heading down unproductive pathways?

What’s Possible

Before addressing the how, consider what generative AI can do for sales organizations.

Reversing administrative creep. Almost every sales organization we touch is cursed with the gradual increase of administrative work over time. As selling complexity grows, so does the need for documentation, approvals, and compliance reporting. Unwittingly, the increasing use of sales technology is also a large factor. New technologies often lead to more training, more data entry, and more reports to peruse. Generative AI can reverse administrative creep, for example, by helping salespeople write emails, respond to proposal requests, organize notes, and automatically update CRM data.

Enhancing salespeople’s customer interactions. The use of AI in sales has been progressing of late. We have helped many companies deploy AI-powered systems that recommend personalized content and product offers, along with the best channel for salespeople to use to connect with customers. Recommendations are based on data about the preferences and behaviors of the customer and similar customers, as well as past interactions with the customer. Salespeople accept or reject the recommendations and can rate their quality to improve the algorithms.

By layering on generative AI, the models can produce better recommendations. One example would be considering customer sentiments gleaned from the nuances of language and subtle signals of customer interest or distrust — in emails, conversations with salespeople, posts on social media sites, and more. Further, the salesperson can collaborate with the system to improve recommendations in real-time. For example, after receiving a suggestion to approach a customer with a new offering, the salesperson can dig deeper — both vertically into the customer’s own needs and horizontally to find other customers who might benefit from the same offering. An interactive, conversational user interface makes the application easy to use. In a truly collaborative seller-buyer environment, even the buyer can be part of the dialog.

Assisting sales managers. Sales managers spend a lot of time studying reports and analytics on sales performance. Recently, most sales reports have progressed from passive, backward-looking documents to more interactive, diagnostics tools with drill-down capabilities. With generative AI, reporting systems can become even more powerful and forward-looking. Managers can pose questions to get insights for helping salespeople improve and for delivering more pointed and motivational coaching feedback. Sales planning tasks that took weeks can be performed in an hour, as managers dialog with the system to discover opportunities, formulate key account strategies, and determine how to allocate effort to geographies, customers, products, and activities. ... ' 

Summary.   

Sales teams have typically not been early adopters of technology, but generative AI may be an exception to that. Sales work typically requires administrative work, routine interactions with clients, and management attention to tasks such as forecasting. AI can help do these tasks more quickly, which is why Microsoft and Salesforce have already rolled out sales-focused versions of this powerful tool.close  ... ' 

Tableau Adds Big Data Analytics

We were early users of Tableau.  Now part of Salesforce.

Tableau gains new big data analytics features

Kyle Wiggers, @Kyle_L_Wiggers  in Venturebeat

Salesforce today debuted new features in Tableau, including data and analytics platform capabilities available with new enterprise subscription plans. Key enhancements and improvements focus on managing data and enabling scalable data governance, as well as ensuring that analytics adapts with changing demand.

“Tableau has long been a favorite to help individuals see and understand data,” Francois Ajenstat, chief product officer at Tableau, said. “We’re making it easier for IT leaders to make it a favorite across the entire enterprise and deliver an end-to-end solution to leverage the full power of data analytics.”

Big data analytics

The pandemic has underscored the need to increase data self-sufficiency. According to McKinsey, 92% of companies are failing to scale analytics. And new data from MuleSoft and Coleman Parkes Research found that 87% of IT and business leaders are concerned that security and governance are slowing the pace of innovation.

Among the features heading to the latest version of Tableau (2021.3) are revamped data prep tools, which make it easier for customers to reduce loads and server costs automatically while mapping out data trends. New governance features alert users of potential issues and provide visibility into the type of data they have, and where it came from. And enhanced management controls let admins centrally configure which users and groups have access to which slices of data.

A new enterprise reference architecture, Enterprise Deployment Guidelines, provides customers with an approach to achieve basic availability, scalability, and security requirements. Meanwhile, dynamic scaling and resource management — which are also new — helps enterprises define limits on resources for their deployments, ensuring that enough software containers are available during peak-demand times and scale down during low demand.

Doug Henschen, VP and principal analyst at Constellation Research, said, “IT leaders are facing unprecedented challenges today, and the pandemic has underscored the need to increase data self-sufficiency and empower everyone to do more with data. It’s no longer a luxury for businesses to have a data analytics platform, it’s a necessity that could make the difference between surviving and thriving.” ... ' 

Salesforce and Marketing AI

 Overview of Salesforce and Marketing AI

Using AI to Automate Marketing Processes Using Salesforce Tools

Salesforce relies heavily on AI technology to help companies dramatically expand their market share. By Diana Hope

Artificial intelligence has become incredibly important in the field of marketing. The massive applications of big data in the field of marketing is one of the reasons that the market for AI technology is growing at a rate of 39% a year.

In recent years, marketing automation has been a topic of utmost interest. It is one of the biggest gamechangers for the marketing profession brought on by AI.

How is AI Changing the Future of Marketing?

This magic tool promises to attract qualified leads to your product and increase revenue exponentially. But what lies behind this AI-driven technology? What does it offer its users? And how can companies implement this sought-after solution into their workflows?

Marketing automation is a form of artificial intelligence technology that automates repetitive marketing activities. It helps marketing and sales departments to run campaigns across various channels: email, text messaging, social networks, and websites. In addition, the platform provides an individual approach to each client, based on the data of their purchasing habits. This is why AI is particularly useful in the field of e-commerce.

There are various providers of marketing automation solutions that rely on complex advances in AI and machine learning. Products of these providers differ in scope, price, and the size and goals of user companies. In this article, we are going to take a closer look at one of the world’s largest platforms for marketing automation – Salesforce.

What is Salesforce Marketing Cloud and What AI Features Does It Bring?

Salesforce has a lot of AI features embedded in it. One of the biggest is Salesforce Einstein, which the company describes on this page:

“Bring the power of artificial intelligence to everyone with Salesforce Einstein.

Salesforce Einstein is a layer of intelligence within the Salesforce Platform that brings powerful AI technologies to everyone, right where they work. And with the Einstein Platform, admins and developers have a rich set of platform services to build smarter apps and customize AI for their businesses.” .... ' 

Salesforce Builds an AI Platform

Interesting example of the effort and its building ... 

Building a Successful Enterprise AI Platform  By Arpeet Kale

Introduction

In 2016, I started as a fresh grad software engineer at a small startup called MetaMind, which was acquired by Salesforce. Since then, it has been quite a journey to achieve a lot with a small team. I’m part of Einstein Vision and Language Platform team. Our platform provides customers with the ability to upload and train datasets (images or text) to produce models that can be used for generating insights in real time. We serve internal Salesforce teams working on Service Cloud, Marketing Cloud, and Industries Cloud, as well as external customers and developers.

If you’ve ever interacted with a chatbot on a leading e-commerce apparel store, financial institute, healthcare organization, or even a government agency, then it’s likely that your request was processed on our platform to understand the question and provide an answer. For example, Sun Basket customers are able to track orders or packages, report any issues with delays or damage, and get a credit or refund. AdventHealth is able to provide patients with an interactive CDC COVID-19 assessment and educate them about the disease.

I’m frequently asked about my experience building an AI platform from the ground up and what it takes to run it successfully, which is what this blog post explores.    .... " 

Salesforce Doing Advanced Metric Analysis for NLP

Good to see interesting AI things in the sales-marketing domain, a place we played early on.

Salesforce researchers release framework to test NLP model robustness

Kyle Wiggers, @Kyle_L_Wiggers, January 13, 2021 6:00 AM in VentureBeat

In the subfield of machine learning known as natural language processing (NLP), robustness testing is the exception rather than the norm. That’s particularly problematic in light of work showing that many NLP models leverage spurious connections that inhibit their performance outside of specific tests. One report found that 60% to 70% of answers given by NLP models were embedded somewhere in the benchmark training sets, indicating that the models were usually simply memorizing answers. Another study — a meta analysis of over 3,000 AI papers — found that metrics used to benchmark AI and machine learning models tended to be inconsistent, irregularly tracked, and not particularly informative.

This motivated Nazneen Rajani, a senior research scientist at Salesforce who leads the company’s NLP group, to create an ecosystem for robustness evaluations of machine learning models. Together with Stanford associate professor of computer science Christopher Ré and University of North Carolina at Chapel Hill’s Mohit Bansal, Rajani and the team developed Robustness Gym, which aims to unify the patchwork of existing robustness libraries to accelerate the development of novel NLP model testing strategies. ... '

IBM and Salesforce Collaborate

New efforts underway to address verification of health status.

IBM and Salesforce Join Forces to Help Organizations and Individuals Verify Vaccine and Health Status

ARMONK, N.Y. and SAN FRANCISCO, Dec. 18, 2020 /PRNewswire/ -- IBM (NYSE: IBM) and Salesforce (NYSE: CRM) today announced that they are partnering to help organizations as they strive to safely reopen public places and provide individuals with a verifiable and privacy-preserving way to manage and share their vaccination and health status in the wake of the COVID-19 pandemic. As part of this offering, IBM Digital Health Pass will integrate with the Salesforce Work.com platform. Integration of the  ... "

On Salesforce Buying Slack

Salesforce buying Slack.  Is this Deeper collaboration? Can see some interesting things coming out of this when combined with Salesforces' work in AI. 

Salesforce + Slack mashup signals the rise of Deep Collaboration in Venturebeat

Jake Saper, Emergence Capital  @jakesaper

News this week that Salesforce is acquiring Slack has a lot of people opining on the pros and cons of such a merger. But one thing is clear: combining the collaboration functionality of Slack with the sales productivity tooling of Salesforce represents a monumental step forward in the history of enterprise software.

My team at Emergence Capital have been students of the cloud since we invested in Salesforce in 2002. Ever since, we’ve exclusively focused on enterprise software, partnering with productivity leaders like Box and Veeva as well as collaboration leaders like Yammer and Zoom.

However, an unfortunate truth has emerged as the cloud has matured: Collaboration software is increasingly at odds with productivity. As the number of apps in both categories has exploded, there’s been a huge uptick in data loss (and frustration) as workers pivot across apps to get their work done. It’s tough to stay in flow when you’re constantly forced out of it by your tools.

The solution: Deep Collaboration

Collaboration and productivity tooling evolved separately. The move to remote or hybrid work is the catalyst we’ve needed to fuse them. We used to rely on collaboration tools for lighter weight tasks — updates, check-ins, gif sharing, etc. — and reserve deeper, more substantive collaboration for in-person meetings. The old stack (mostly) worked for that approach.

In a world where deeper collaboration is being done remotely, we need a new stack. Collaboration tooling can’t be a destination. It must be embedded in the work itself.

My team calls this stack Deep Collaboration. The term refers to software that combines productivity and collaboration functionality in one place to get a specific job done. In a Deep Collaboration future, a person doing a specific task doesn’t have to leave a single piece of software to get that job done. All the productivity and collaboration (both internal and external) features they need to accomplish a task live in the same place. .... ' 

Salesforce: Mapping Algorithms for Office Reopening

Depending on how well it works could be interesting to try.  By Salesforce.  Further calibration with data and results? How different is this than using methods like those developed more generally for transmission tracking?  Like the integration with mapping aspects of the office.  Integrating risk analysis?

The Algorithm That Could Get You Back in the Office  in Bloomberg, By Andrew Zaleski

As company offices reopen, some are using software to help them shuffle employees, schedule meetings, map office hot spots, and practice social distancing. Maptician detects areas of high transmission risk, such as desks placed too close together, and allows co-workers to view the map to see if they were ever seated near a colleague who tested positive for Covid-19. Employees of commercial real estate brokerage SquareFoot developed an algorithm to determine work rotations for a company’s teams. Salesforce's Work.com has developed a software platform that includes a shift-management algorithm that calculates potential building bottlenecks based on the number of projected workers.... '

Article Summarization by Microsoft

Had just mentioned this classic AI problem, was pointed to work underway by Microsoft, which points to this.   Pointd to the article below,  which after the abstract gets technical.    Like I have mentioned before, its what humans do well,  and is a subtask of common conversation.  Absorbing what others say, summarizing it based on context and what we understood, asking for clarification and formulating some measure of our understanding.

STRUCTURED NEURAL SUMMARIZATION
Patrick Fernandes, Miltiadis Allamanis & Marc Brockschmidt
Microsoft Research
Cambridge, United Kingdom  

Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a
range of summarization tasks.

 INTRODUCTION
Summarization, the task of condensing a large and complex input into a smaller representation that
retains the core semantics of the input, is a classical task for natural language processing systems. Automatic summarization requires a machine learning component to identify important entities and
relationships between them, while ignoring redundancies and common concepts  ... "

Summarized in a Venturebeat article.

See also Google's work in this at Summarization tag.   See also work by Salesforce in this area that slipped my mind,, will be reviewing that.

AI for Boosting Online Training Business

Good thoughts, though ultimately we need better ways to create and maintain conversations to make training work well.   Moving in that direction, but lots of work yet to do.

Huge Benefits of AI For Boosting Your Online Training Business  Via Gibb Bassett
There are many benefits of AI (artificial intelligence) when it comes to boosting your online training business. Here's what to know about it.

By Sean Mallon in SmartData Collective

Countless businesses are using AI to revamp their operating models. AI is being used for a variety of purposes:

Predictive analytics tools can help
 them forecast future revenue, which helps with tax and inventory planning
AI helps them identify future consumer trends, so they can adapt their marketing strategies
AI has made it easier for companies to optimize chatbots to provide better customer service
There are countless benefits of using AI in business. One of the industries that is benefiting from AI the most is the online training industry. .... " 

Tableau Conference Nov 13-15

Particularly interested in seeing how they are integrating with Salesforce, providing new AI methods,  New kinds of data handling, Better visualization and prescriptive methods for decision makers.  I will be there. 

Via DSC: See the Tableau conference, free from anywhere.

Tableau Conference Livestream

November 13 - 15, 2019 | 8:30 AM - 6:30 PM PST daily
REGISTER NOW 

Get notified when the event goes LIVE

Don't miss a moment from Tableau Conference

Tableau Conference is right around the corner, and we are ready to get the data party started. If you aren't able to attend in person, we've got great news. We're bringing the excitement, inspiration, and learning to you, LIVE — Wednesday, Nov. 13 through Friday, Nov. 15, 8:30AM - 6:30PM daily. Access our amazing keynotes as they happen, including Iron Viz and Devs on Stage, along with our most popular sessions. And be sure to check out Tableau Conversations to take a deep dive into some of our newest features and offerings. You won't want to miss it. Register for our free Livestream and get notified by email when we go LIVE. Prepare to geek out over all things data.  ... "

Salesforce to Acquire Tableau

Worked with Tableau from the very beginning.   Seen impressive things from both companies.  Indications from both for linking to learning AI.

Salesforce is buying data visualization company Tableau for $15.7B in all-stock deal   By Ingrid Lunden  @ingridlunden / 6 hours ago

On the heels of Google  buying analytics startup Looker last week for $2.6 billion, Salesforce today announced a huge piece of news in a bid to step up its own work in data visualization and (more generally) tools to help enterprises make sense of the sea of data that they use and amass: Salesforce is buying Tableau for $15.7 billion in an all-stock deal.

The latter is publicly traded and this deal will involve shares of Tableau Class A and Class B common stock getting exchanged for 1.103 shares of Salesforce  common stock, the company said, and so the $15.7 billion figure is the enterprise value of the transaction, based on the average price of Salesforce’s shares as of June 7, 2019.

This is a huge jump on Tableau’s last market cap: it was valued at $10.79 billion at close of trading Friday, according to figures on Google Finance. (Also: trading has halted on its stock in light of this news.) ... " 

What is Salesforce Einstein Discovery?

Have been looking at some of Salesforce offerings.

Video of of tech of basics of Salesforce Einstein:

By Mark Tossell:

Einstein Discovery is a curious enigma in the Salesforce ecosystem. Most people in the Salesforce world have heard of Einstein, as it has been widely publicised, but the majority have little understanding of what it does and how they can use it. In fact, even a good number of thought leaders and consulting partners are in the dark around Einstein.

I think that three factors have contributed to this scenario:

Lack of understanding around the general subject of artificial intelligence and machine learning, especially in the context of business deliverables.

Confusion about the rather complex family of Salesforce Einstein products – including Einstein Analytics, Sales Cloud Einstein, Einstein insights, Einstein Prediction Builder, and Einstein Discovery. Phew!

The challenge that Salesforce Account Executives have to effectively communicate a  product family that is becoming increasingly diverse and complex.

This lack of understanding is unfortunate, because Einstein Discovery (ED) is an extremely powerful tool that can offer tremendous business value if correctly implemented and effectively employed.

The purpose of this post is not to publish a detailed technical treatise about the ED platform; I’ll leave that to others. Rather, my goal is to distill the jargon, bypass the hype, and introduce a very capable piece of kit to those who might benefit from it.

1. What is Einstein Discovery?
Supervised machine learning. Those thee words succinctly define the ED platform. However, what does this mean? How does a machine learn? And why does it need supervision, as if it were some delinquent child? Let me explain.

Machine learning (ML) is analytical model building that can be automated, where the systems “learn” from data and identify patterns, resulting in meaningful conclusions and predictions.

I like this definition of ML from the SAS institute web site – “Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.”

There are two types of machine learning: supervised, and unsupervised. They are fundamentally different, so let’s briefly set them apart.

In supervised machine learning you have a set of features, or variables, similar to the column names in a spreadsheet. You also have a response variable that you use the features to make predictions for. A simple example would be looking at the brand, age, size and condition of motor vehicles in order to make a prediction about their value. Supervised learning is what Einstein Discovery does, and it is very good at it.  .... " 

IBM and Salesforce

For AI Style applications you must connect with experts in the domain area.    Here is an example of doing that,

IBM expands AI partnership with Salesforce    By Mike Wheatley
 IBM Corp. and Salesforce.com Inc. are expanding their partnership on all things artificial intelligence by integrating Big Blue’s Cloud and Watson services with the Salesforce’s Quip and Service Cloud Einstein products.

The company’s alliance was struck last March, when the pair announced they’d be integrating Watson with Salesforce’s relatively newer Einstein AI tech. At the time, analysts said the partnership effectively meant IBM was becoming a kind of AI consulting partner for Salesforce, allowing it to sell new services across both Einstein and Watson. .... " 

Text Summarization

A good AI challenge that has useful applications.

An Algorithm Summarizes Lengthy Text Surprisingly Well
Training software to accurately sum up information in documents could have great impact in many fields, such as medicine, law, and scientific research.

by Will Knight  May 12, 2017  in Technology Review.

Who has time to read every article they see shared on Twitter or Facebook, or every document that’s relevant to their job? As information overload grows ever worse, computers may become our only hope for handling a growing deluge of documents. And it may become routine to rely on a machine to analyze and paraphrase articles, research papers, and other text for you.

An algorithm developed by researchers at Salesforce shows how computers may eventually take on the job of summarizing documents. It uses several machine-learning tricks to produce surprisingly coherent and accurate snippets of text from longer pieces. And while it isn’t yet as good as a person, it hints at how condensing text could eventually become automated .... " 

Machine Learning for Summarizing Text

Accurately and efficiently summarizing text has been a classic goal of AI systems, of interest that Salesforce, known for doing AI experimenting in this space, is working on it:   In TheVerge:  (with demo):

Salesforce created an algorithm that automatically summarizes text using machine learning  by Andrew Liptak   @AndrewLiptak   .... " 

IBM and Salesforce Partner with Watson

I agree with the statement that cognitive systems will continue to touch increasing numbers of people.  If this will be by Watson, and how soon it will become universal, is still unclear.   In TechCrunch:

IBM and Salesforce Partner to Sell Watson and Einstein

" ... The new partnership amounts to a way for IBM to sell consulting services across both Salesforce’s Einstein and IBM’s Watson AI-branded businesses.

Insights from Watson will now be available directly in Salesforce’s Intelligent Customer Success platform, mixing Einstein’s customer relationship data with Watson’s stores of structured and unstructured data that include weather, healthcare, financial services and retail, the companies said.

“Within a few years, every major decision — personal or business — will be made with the help of AI and cognitive technologies,” said Ginni Rometty, chairman, president and chief executive officer, IBM, in a statement.

Rometty said that IBM expects Watson to “touch” 1 billion people — through everything from oncology and retail to tax preparation and cars (no word on whether they’re also including advertising in that figure, but they may be… IBM runs a lot of Watson ads).   ... " 

Salespeople and Executives

Worked on executive information systems that linked sales people and executive decisions.

Executives and Salespeople Are Misaligned — and the Effects Are Costly
Frank V. Cespedes, Christopher Wallace

U.S. companies spend over $900 billion on their sales forces, which is three times more than they spend on all ad media. Sales is, by far, the most expensive part of strategy execution for most firms. Yet, on average, companies deliver only 50% to 60% of the financial performance that their strategies and sales forecasts have promised. And more than half of executives (56%) say that their biggest challenge is ensuring that their daily decisions about strategy and resource allocation are in alignment with their companies’ strategies. That’s a lot of wasted money and effort. .... "