The news

During the Salesforce AI Day on June 12 as well as the Salesforce AI Industry Analyst Forum on June 20, Salesforce provided a lot of interesting information on how the company addresses the challenge – or should I say problem – of trust into artificial intelligence. Salesforce sees this gap caused by hallucinations, lack of context and data security as well as toxicity and bias. According to Salesforce, this gets compounded by the need for integrating external models into business software.

To address this problem, Salesforce has announced its AI Cloud that combines an “Einstein GPT Trust Layer”, Customer 360 and its CRM to offer AI-powered business processes that are built right into the system, based on an AI that can be trusted. The main vehicle is the Einstein GPT Trust Layer that takes care of

• secure data retrieval from business applications,

• dynamic grounding to reduce the risk of hallucinations and to increase response accuracy by automatically enriching prompts with relevant business-owned data,

• data masking, the anonymization of sensitive data to avoid its unintentional exposure of sensitive data to external tools,

• toxicity detection to make sure that generated content adheres to corporate policy, is free of unwanted words or images, and unbiased,

• creating and maintaining an audit trail,

• the external (or internal) AI not retaining, storing, any corporate information that gets sent to it via the request.

This trust layer sits in between the used AI models and the apps and the respective development environments. All requests to the models, along with their data, get routed through this layer, ensuring authorization protected retrieval of data, the grounding of prompts using it as well as data masking for anonymization. Responses by the models get routed through it as well. This enables an audit trail as well as toxicity detection. Models can be ones within Salesforce, ones developed and deployed by the customers in their infrastructure and third party models.

Figure 1 The Salesforce AI Cloud Architecture; source Salesforce

To round this off, Einstein Studio allows the building and deployment of models, their training using data within Salesforce and, at a later stage, the building of own models using a no-code environment.

The bigger picture

Although AI is not new, it is safe to say that generative AI is a game changer. OpenAI managed to get AI out of the realm of data scientists and into the hands of mere mortals. And most of us use business applications on a daily basis.

One of the most daunting problems of the use of AI is that there are a number of considerable risks involved with its usage. The one that is currently talked about most in the context of generative AI is the one of accuracy of responses to prompts, which is often referred to as hallucinations. This is not only problematic in consumer usage but even more so in business usage.

What comes on top in a business context is very much related to data privacy bias and profanity. Both have also been discussed in the consumer arena. Do you remember Microsoft’s infamous Tay bot? Or more recently of Samsung, Amazon, Apple, and other companies ordering their staff to not use ChatGPT et al.?

The management of all of these risks is of paramount importance to businesses, for regulatory reasons as well as for the need of protecting own intellectual property. No business can afford customer data and/or sensitive corporate data leak into external tools. This is doubly true in strongly regulated industries. But how to ensure this, when the models are not fully understood and when it is not even clear where and how data is stored? How to adhere in a GDPR-request to delete a customer’s data in this case? The management of these risks requires organizational, educational, and cultural measures in companies. These need to be supported or enforced with the help of technology. 

The obvious technical resolution for this is an AI security layer that I outline in my (upcoming, as of this writing) column article on CustomerThink as follows.

Figure 2 An AI security layer; source Thomas Wieberneit

This is of course simplified, not exactly trivial, but possible.

My analysis and point of view

One could, or rather should, say that trust and security are two of the most important assets in business. Customers need to trust that businesses do not collect an inordinate amount of data and that they furthermore use the data given by customers only for consented to purposes. In addition, they need to trust businesses that they keep their data safe. A multitude of regulations mandates this. Being trustworthy is even more important in times of AI as a service, when businesses cannot even tell anymore where customer data is stored, as it is learned by the AI and stored in a very decentralized manner – as part of an unknown number of parameters.

To enable this trustworthiness, what lies closer for a tier one platform vendor than ingraining an AI security layer directly into the own platform? The gateway to external services is already provided by the platform and can be reused by the AI security layer.

This is what Salesforce has done in an exemplary manner with the aptly named Einstein GPT Trust Layer. Kudos for this.

Figure 3 - How the Einstein GPT Trust Layer works; source Salesforce

In my opinion, the most interesting part is the zero-retention portion. Salesforce cannot guarantee on its own that external providers do not store any data. Whenever a prompt is sent to an external vendor, this data is leaving Salesforce’s systems boundaries. This means that external vendors assume temporary control of this data to provide their services. Masked or not, this data that can potentially be demasked, is handled by them.

To accommodate for this, Salesforce has established “zero-retention policies” with these vendors. According to information given during an analyst briefing, these policies ensure that the vendors won’t store any in-flight data, including inputs and outputs, nor won’t they use it for any purposes besides generating a response to the prompt.

This is quite an important statement that also indicates GDPR compliance, if “policy” can be translated to contract. On the other hand, this makes me curious how the refinement of prompts works in this case. Obviously, for highly security-oriented customers, this also suggests the preference of Salesforce or customer-owned models over external ones.

Overall, this is a great offering that addresses important concerns  of the C-suite.

The only qualm that I have is the price tag, which is quite steep, starting at currently $360,000 US. For sure, customers can derive good value out of it, this is not the problem. Where I see a challenge is that this is out of reach for most SMBs. I’d love to see an adaptation of this offering combined with offerings like Salesforce Easyor similar.

I wait to see when other vendors come forward with a comparable offering. Especially the other tier one but also the tier two vendors need to make a move now. Salesforce truly let the Genie out of the bottle and put them in a tight spot.

Kudos again!

The hype around generative AI, in particular ChatGPT is still at a fever pitch. It created thousands of start-ups and at the moment attracts lots of venture capital. 

Basically, everyone – and their dog – jumps on the bandwagon, with the Gartner Group predicting that it is getting worse, before it is going to be better. According to them, generative AI is yet to cross the peak of inflated expectations. 

There are a few notable exceptions, though. So far, I haven’t heard major announcements by players like SAP, Oracle, SugarCRM, Zoho, or Freshworks.

Before being accused of vendor bashing … I take this is a good sign. Why?

Because it shows that vendors like these have understood that it is worthwhile thinking about valuable scenarios before jumping the gun and coming out with announcements just to stay top of the mind of potential customers. I dare say that these vendors (as well as some unmentioned others) are doing exactly the former, as all of them are highly innovative.

Don’t get me wrong, though. It is important to announce new capabilities. It is probably just not a good style to do so too much in advance, just to potentially freeze a market. This only leads to disappointments on the customer side and ultimately does not serve a vendor’s reputation. 

For business vendors, it is important to understand and articulate the value that they generate by implementing any technology. Sometimes, it is better to use existing technology instead of shifting to the shiny new toy. The potential benefits in these cases simply do not outweigh the disadvantages, starting from cost of running the new technology and extending to the added business value being marginal. Sometimes technology is a solution in search of a problem (anyone remember NFT or  Metaverse?), sometimes the new technology even turns out to be outright harmful.

Although the better is the enemy of the good, not everything new is actually better than the old. Vendors as well as buyers should keep this simple truth in mind.

Specifically looking at generative AI, it is therefore important to look at what the strengths and limitations of this technology are and to map out where business scenarios map to them. For this, I have outlined a simple framework a short while ago.

In brief, value comes out of solutions that adequately address the dimensions of fluency and accuracy. Not every business challenge needs to be addressed with equal fluency or accuracy. However, and this is important, accuracy also covers bias. Bias needs to be understood and managed. 

I have outlined a few examples in that article.

In the past few weeks, vendors like Cognigy, Microsoft, Salesforce and Google did some major announcements covering enterprise use cases of generative AI. In the meantime, Open AI announced version 4 of GPT. Let’s have a look what they were about and how they fit into the fluency and accuracy categories. Notably, all vendors emphasize on a human-in-the-loop functionality being embedded in their new AI features.

Cognigy

Cognigy is a vendor of conversational AI, currently focusing on the call center market, as both, agents and customers can receive a lot of benefit from a conversational AI system. Part of any conversational AI system is the ability to design and implement conversations. As such, the company implements technical as well as business scenarios with the help of generative AI.

The technical scenarios range from seemingly simple ones like the creation of seeding sentences to train the system’s intent detection to the development of conversation flows from written commands.

Both of these scenarios are in the high fluency area, while the creation of conversation flows also requires high accuracy. Generating seed phrases as a training set needs a solid understanding of synonyms and language use while the generation of whole conversation flows also needs precise formulations. After all, code is a precise language, even if visualized by symbols.

The business scenarios are all about improving the understanding of the customer and providing well worded responses, written or spoken – with the spoken ones obviously being more impressive. Cognigy basically combines the strengths of the conversational AI system – keeping focus on the task to be accomplished and connectivity to business systems – with the ability of the generative AI to formulate human-like sentences and to exhibit empathy by reacting on statements that deviate from the core task to be accomplished. Two examples for this are here and here.

Google

Google is an example that shows the hype that is generated by ChatGPT. The company is using generative AI features in its workspace products for quite a while now. For emails, smart reply exists since 2017, smart compose that suggests sentences of fragments thereof since 2018, auto summarizing of documents and spaces since 2022. Spam and phishing filtering is basically available forever and gets continuously improved. On March 14, Google announced the rolling rollout of additional features for Gmail, Docs, Slides Sheets, Meet and Chat. These features will base on Googles own PaLM LLM. Google will start with text generation from topical prompts or a rewriting of a given text.

In contrast to the other vendors, Google is focusing on collaboration efficiency, which is reasonable as Google is not a business applications vendor in the traditional sense. As these features will be made available only in the course of 2023, it remains to be seen how good they really are. However, all of these features need to score high on the fluency scale, with the drafting, replying, summarization and prioritization of texts also requiring high accuracy.

Microsoft

No need to explain what Microsoft in general does. Being the main investor into Open AI, it naturally has a front runner role when it comes to the integration of generative AI by Open AI into business and other applications. Microsoft did two main announcements in the past weeks that stretch the range of business applications. First, it announced an integration of generative AI into its Viva Sales product, already in February. On March 6, the company then announced an “AI copilot for CRM and ERP”.

In February, Microsoft announced the integration of GPT into Viva Sales, with the ability to automatically formulate emails for specific scenarios, like replying to an inquiry, or formulating a proposal, also utilizing data coming via Microsoft Graph. 

In March, this got enhanced to cover not only Viva Sales but functions covering sales, service, marketing and supply chain, followed by an announcement covering Microsoft 365 (formerly known as MS Office) on March 16. These are now being dubbed the Dynamics 365 Copilot and Microsoft 365 Copilot. The functionality in Viva Sales gets enhanced by some scenarios, an additional feedback loop and the capability to create meeting minutes including action items. These scenarios require a high fluency and varying degrees of accuracy, with the summarizing of meetings being at the higher end.

The same capabilities are available in Dynamics 365 Customer Service email and chat. Incoming information is analysed and used to create an answer that includes information from the knowledge base.

These scenarios basically require the same degree of fluency and accuracy as the summarization of meetings to lead to fast and efficient issue resolution.

Marketing scenarios include the prompting of data in natural language, for example to create a target group. The system basically generates the query to fetch the corresponding data, which requires good knowledge of the database schemata, i.e. has high demands to accuracy.

The second scenario is the creation of the text for a marketing email to support the campaign. Notably, there is no support for the creation of a landing page yet. In this scenario, the requirement to fluency is higher than the one to accuracy.

Microsoft Dynamics 365 Business Central gets enhanced by the ability to generate product descriptions based upon product title and product attributes. 

Last, but not least, Dynamics 365 Supply Chain Management now allows to generate emails to suppliers, carriers, etc., based on intelligence surfaced in the news module that gets correlated to existing orders. 

The capabilities of the Microsoft 365 Copilot are largely equivalent to the ones described above, tapping into the Microsoft Graph.

Most of these Copilot features are available in what Microsoft names a limited preview only.

Salesforce

Salesforce, the undisputed leader in CRM, announced support for generative AI, named Einstein GPT, on March 7, as part of its TrailblazerDX event as “the world’s first generative AI for CRM”. The functionality shall be able to create content across sales, service, marketing, commerce and IT. Similar to what Microsoft announced, Einstein GPT is able to generate personalized emails for salespeople, generate specific responses in service scenarios or to generate targeted content for marketers, which includes landing pages. To do so, Einstein GPT extends Salesforce’s proprietary AI models, takes advantage of Salesforce’s Data Cloud and offers out of the box connectivity to Open AI’s AI models. Alternatively, it is possible to utilize other models, e.g., Salesforce partners Anthropic, Cohere, Hearth.ai or You.com.

Salesforce will invest in these companies via its investment arm. Additionally, Einstein GPT is capable of auto creating code from user prompts.

Salesforce’s focus is slightly different from Microsoft’s. No support for ERP and supply chain tasks is obvious. Differences in sales, service and marketing functionalities are more subtle. Where Microsoft concentrates on meeting minutes in its sales functionality, Salesforce supports scheduling of meetings.

What is interesting is Salesforce’s ability to generate kb articles from case notes as part of its service capability. If this functionality also includes improving existing ones, it could be a real game changer in customer service.

Einstein GPT for Marketing supports the generation of content supporting email, mobile, web and advertisement engagements.

With Einstein GPT for Slack Customer 360 apps it is possible to deliver insights like generated summaries of opportunities and other information to Slack. This supports the increasing importance of conversational user interfaces and is a good fit for a generative AI.

Last, but not least, Einstein GPT for Developers enables the generation of code. This functionality uses a Salesforce Research proprietary LLM.

Out of these scenarios, the generation of kb articles and code certainly have the highest demands on accuracy.

At this time, Einstein GPT is in a closed pilot.

My analysis and point of view

In brief, I see much more of a future for these technologies than I have seen in the past major hypes: Web 3, Blockchain, and Metaverse. This is mainly because these three appear to be solutions in search of a problem while we see clearly described enterprise use cases for generative AI.

It is quite obvious that these vendors that I selectively chose are using a land and expand approach. They are starting with very specific scenarios which get extended over time. Many of these use cases extend existing conversational, or in general, AI based scenarios. From a technology point of view the new solution might even replace the older one, which is of no consequence if there is a transparent migration.

The selected scenarios are regularly in the high accuracy and high fluency quadrant, which caters to one of the main use cases of a generative AI.

It is also quite obvious that there is a tremendous struggle for mindshare. All these announcements are coming at about the same time and they are mostly talking about limited availabilities, i.e. betas or trials, indicating work in progress. The actual releases are at some time in the future. As much as I do not like this, this seems to be the way the business works.

Not surprisingly, the vendors are mainly focusing on similar capabilities. This is partly due to my selection. The good news is, that all vendors argue with business benefits instead of promoting technology for technology sake. To provide these capabilities, and this is important, all vendors connect the generative abilities with data that is available in the organization, be it from databases, file repositories, business applications, or chat- and email conversations. 

In my eyes, all these functionalities are helpful in a sense that they take tedious work away from persons; so, they are definitely worthwhile to be trialled.

The challenge

For now, all these features require a human in the loop, i.e., they need active confirmation of a user. Which is a good thing, given that a generative AI still has a tendency to hallucinate, even though e.g. Open AI claims that GPT 4 has a 40 percent higher likelihood to produce factual responses than GPT 3.5.

The goal is to gain trust while the human is still in control. But what do humans do, when they trust, or trust enough? All of the sudden, the suggestion implicitly becomes a decision. This is OK, as long as it is really sure that these “decisions” are good. And that includes that a number of things are guaranteed.

The models must be trained to have minimum bias. There must be a verifiability of this. This does not jibe well with news like Microsoft laying off an ethical AI team while not explaining how this job is done in future. Principles are good, control is necessary. Similarly, Open AI’s turn to not disclosing anymore how the training data got created nor how many parameters it has, etc., citing the competitive environment. As PROs chief AI strategist Dr. Michael Wu recently said in a CRMKonvo, it is impossible to avoid bias, but it must be understood. Then the AI can be a real helper. I’d like to add that the user must be able to understand.

This leads to the second point. The system’s “reasoning” needs to be explainable by the system in a way that not only data scientists understand it. This is an area that many AI’s sorely lack and actions like the ones above are not helpful in this endeavour as they may raise the impression of delivering a black box that is not subject to strict governance.

Lastly, the data that gets used in training and operation must be clean enough. This is a job for not only the vendor but also for the organization that runs the AI. One reason is the reduction of bias, the other one going forward is good recommendations/decisions. While data cleansing is a lost cause, frameworks and regular clean-up work needs to be in place to be sure that the used data is good enough – not perfect, but good enough.

My advice

Given all this, I advise cautious use of these tools in a controlled environment, and to measure the results, both in terms of effort reduced and quality of the output. This is not an easy, but a mandatory, task to fully assess the value of these tools and to establish the necessary trust level. 

Additionally, have the vendors demonstrate which AI principles govern the development and what procedures they have in place to effectively make sure that these principles are baked into the product.

In that sense, the vendors’ early announcements and long closed trial periods are very helpful. 

The News

• Dynamic job scheduling
• Using Einstein Recommendation Builder to ensure that service technicians have the right spare parts available
• Asset management capabilities that allow companies a detailed view into the installed base at their customers. This is developed in cooperation with ServiceMax
• Improvements to the appointment assistant to provide as accurate as possible information to the customer about the arrival time of the service technician.

• Einstein Recommendation Builder enables organizations to rapidly deploy machine learning models to enhance service, including AI-powered recommendations to ensure mobile workers always have the right parts for the job. Einstein will scan similar past work orders for previous, similar jobs to identify which parts will be needed for the current one.
• Asset 360 is a new set of asset management capabilities that ServiceMax is building in partnership with Salesforce. With Asset 360, companies will have complete visibility into their install base, service contracts and asset performance (e.g. machinery, medical equipment) to maximize the uptime of complex equipment and reduce operational costs.
• Appointment Assistant uses live status updates and GPS to automatically update customers on the technician’s arrival time. This keeps customers informed, and gives them a chance to vacate the premises and/or adequately prepare before the technician arrives - increasing safety for both technician and customer while social distancing is advised.

The Bigger Picture

My Analysis and PoV

The News

• The diverse interests and objective of users of enterprise search make it hard to be as good as a consumer search as delivered by Google or Bing, or the other consumer search engines, especially if in an ecommerce environment. In an enterprise setting, objectives can vary between closing a deal or solving a case, or creating new campaigns. This creates hidden complexities. There are no safe assumptions.
• Data is residing in different silos and frequently not linked. Further, there is no one size fits it all as Salesforce as an application normally is customized to suit an individual customer’s needs
• Third, the data simply does not belong to Salesforce, with the consequence that Salesforce cannot look into the data, even not with the objective of improving search. This makes it impossible to use traditional machine learning approaches.

The bigger Picture

My PoV and Analysis

The News

Expanding our Digital Customer Service Capabilities with New Channels and Bot Innovations

• For more information about how Service Cloud can help you reach customers across digital channels, go to salesforce.com/digital-channels
• Learn about Trailblazers for the Future, our training program that gives contact center managers and agents the soft skills to keep the human side of service alive in the digital era: https://www.salesforce.com/events/trailblazers-for-the-future/

The bigger Picture

MyPoV and Analysis

The News

The Press Release

• Einstein Reply Recommendations uses natural language processing to instantly suggest the best responses to agents over chat and messaging, so they can save time and improve the quality of replies to customer inquiries. And using a machine learning model that learns what has worked over time, Einstein Article Recommendations automatically recommends the best knowledge articles to agents, giving them the information they need to solve cases quickly.
• Einstein Next Best Action leverages business rules and predictive intelligence to suggest the best course of action at the point of maximum impact during agent-customer interactions, helping to increase customer satisfaction and uncover cross-sell opportunities.
• Einstein Case Routing fully automates the routing process with machine learning that filters cases to the right queue or agent based on preset criteria, such as who is best qualified to solve them based on expertise or past outcomes.

• Quip for Service enables agents to co-author documents, bring in subject matter experts across the business to swarm on complex problems, and have live collaborative conversations directly within the case record.
• Admins can build and easily publish flexible Quip templates in the agent console, as well as customize them based on different use cases and specific organizational needs.

• “We are living in a new age of service where today’s customer expects great experiences at every stage of the buying cycle and across any channel, making the agent’s role more critical and more challenging than ever before,” said Bill Patterson, EVP and GM, Service Cloud, Salesforce. “With these innovations we are empowering agents to rise to the occasion with a console built for modern customer service that is intelligent, collaborative and connected.”
• “At Overstock we are constantly looking for new technologies that will help us continue to adapt to our customers’ needs and deliver personalized experiences,” said Kamelia Aryafar, Chief Customer and Algorithms Officer, Overstock.com. “Einstein AI has shown in our pilot tests that it has the potential to help us meet those needs by recommending offers and perks that our care associates can offer customers based on their shopping history and past interactions. Ultimately, Salesforce is helping us provide our customers a more personalized, human experience.”
• “Tapping into Einstein helps us get maximum value from the information stored in our Salesforce CRM, and with that data we have been able to optimize all of our customer service department’s internal processes,” said Zenconnect CEO Yann Mercier. “After implementing Einstein AI to automatically classify cases, our customer service agents saw 25 percent productivity gains, freeing them up to focus on higher level projects.”

• Get more details on the State of Service report at: salesforce.com/blog/2019/03/customer-service-trends.html
• Learn more about Quip for Salesforce in this blog post: https://www.salesforce.com/blog/2019/03/introducing-quip-for-salesforce.html
• To learn more about Service Cloud, go to: https://www.salesforce.com/service-cloud/overview/
• To learn more about Quip, go to: https://quip.com/quip-for-service

• Einstein Next Best Action is generally available and included within Service Cloud Einstein, an add-on to Service Cloud, for $50 per user per month.
• Quip for Service is generally available and included within Quip Enterprise when added to Service Cloud for $25 per user per month.
• Einstein Article Recommendations, Reply Recommendations and Case Routing are currently in pilot. Pricing information will be made available at general availability.

The Bigger Picture

• Agents need the right tools that help them concentrate on the tasks where humans excel by offloading those that are dull and repetitive. Solving these is something the machine excels in.
• The second thing, an agent needs is ready access to education and knowledge. This means training and an available network of knowledgeable colleagues.

My PoV and Analysis

• It would be interesting to add the ability of suggesting the best matched expert to be contacted via Quip. Based upon knowledge ranking Einstein should be easily able to do this. Sources for this could include answers given by the persons, amongst others. All in all this could result in something that is similar to the ranking that is used in communities. Combined with an availability status the quality of replies could even increase while the whole inquiry process becomes less disruptive to the workforce.
• Based upon the constant creation of new codified knowledge it might be interesting look into the ability of creating micro articles, which then are combined into an individualized whole article that then is given back to the customer. This way, the relevance of replies increases, which in turn results in higher customer satisfaction.

The News

• Einstein Bots for Service
• Lightning Flow for Service
• Einstein Next Best Action

The Bigger Picture

My PoV and Analysis

The News

1. Based on the strategic alliance that Google and Salesforce entered into in November 2017, the Salesforce Marketing Cloud will get a deeper integration with Google Analytics 360. Starting now it will be possible to combine Google Analytics 360 data and Salesforce Marketing Cloud data in a single customer journey dashboard within Marketing Cloud. Conversely, Google Analytics 360 can now leverage Marketing Cloud campaign data to better deliver targeted content to consumers. Both integrations enable a deeper understanding of customers and their behaviours. Later, in Q3 this year, Salesforce plans to offer a beta release of an integration that enables marketers to create audiences in Analytics 360 and to activate these audiences for engagement within the Salesforce Marketing Cloud.
2. Marketing Cloud Einstein gets a segmentation and a split capability. The segmentation ability enables the uncovering of patterns in consumer behaviour and the discovery of new audiences that then can get reached with personalized messages. The split capability enables marketers to create unique personalized journeys for each customer with simple means, getting an optimized path for them, based on the marketing objective.
3. There are a number of innovations to enable engagement across touch points. First, Salesforce announces their B2B ecommerce ability, second the new interaction studio that enables the creation of contextually relevant engagements and experiences in real time and third, the broadened availability of Service Cloud LiveMessage in 17 more countries. Live Message enables companies to communicate with customers through bi-directional mobile messaging.

The Bigger Picture

My PoV and Analysis

The News

1. Salespeople do not find the time to do their job, which is selling. Instead they are spending an inordinate amount of time entering data that supposedly only helps their management controlling them a little more.
2. Sales managers do not have enough visibility into what is going on in their area of responsibility, what their team is doing (and why), whether they are doing the right thing. The same problem, of course, applies to the Head of Sales, just at a bigger scale.
3. Sales operations is charged with creating meaningful reports that tell the one single truth. This they need to do using data that resides somewhere, data that is distributed, instead of some central consolidated place. Data that is essentially not fully trustworthy.

• The Salesforce resident AI: Einstein to help sales persons identify the most promising opportunities to work upon
• The Salesforce Inbox that increases productivity by attributing emails to the right accounts as well as connecting to the calendar
• Sales Analytics to help salespeople and their management to visualize, interpret, and use the available data

The Press Release

The Bigger Picture

My PoV and Analysis

Now that the major waves of Dreamforce 2017 have settled, the announcements and a good part of the running commentary has been delivered, it is time for me to have a look at my pre-Dreamforce predictions.

Having been briefed before the event but unluckily not been able to attend (nor having had the time to write this piece earlier, I now have the advantage of having had more ‘thinking time” and can put the main announcements that we were briefed on into a bigger picture.

On the backdrop of an IDC study (sponsored by Salesforce) that postulates 3.3 million new jobs and an overall GDP impact of 859 billion dollar by 2022 in the “Salesforce economy”, the announcements basically revolve around one single topic: How to enable the employees (of Salesforce customers and partners) to deliver to this magnitude.

They were around

• Easier consumption of AI technology with Einstein, and improved IoT support,
• Opening up Trailhead to Salesforce customers in order to support company specific learning maps
• Enabling Lightning, as the platform to become fully themed, i.e. embrace the customers’ brands. Although technologically different I club the ability to create and easily upload branded mobile apps to the app stores into this
• Collaboration using Quip, the software that Salesforce acquired about a year ago, and a new partnership with Google

And in order to emphasize on the fact that they are serious about enabling people individually, Salesforce resuscitated the dot com prefix “my”. Thus myEinstein, myTrailhead, myLightning, mySalesforce, myIOT and mySalesforce got born.

A topic that might be slightly overlooked is covered by two sentences in the announcement of the Google partnership. “Also, Salesforce has named Google Cloud as a preferred public cloud provider to support the company’s rapidly growing global customer bases. Salesforce plans to use Google Cloud Platform for its core services as part of the company’s international infrastructure expansion.” With no further elaboration I can only interpret this as Salesforce following SAP’s multi cloud strategy.

What did I predict?

Following OOW17 and the SAP Hybris Summit in Barcelona I wrote that Salesforce is in a tight spot and that it is not nearly enough to just tick off the accomplishments that were delivered to promise. Looking at the current state trend of business systems I looked at Salesforce strengthening the eco system, adding power to the platform, and leveraging Einstein.

My point of view and analysis

While it seems that I was right on some level, there are some interesting rabbits that Salesforce pulled out of the hat.

Technology needs to serve the users and not the other way round and there are tasks that every user needs to get optimal support for. These are the tasks that need to be done often and the dull tasks that no one really needs to do. Plus, probably the tasks that no one comes around doing because of the previous two. Salesforce seems to have understood this.

Salesforce, in best Microsoft manner, enables this by creating point and click interfaces around complex technologies like (advanced) predictive analytics or IoT. The Salesforce platform also seems to benefit from Einstein permeating it more thoroughly. To me it appears like the days of separate clouds are numbered and that we are moving closer to a suite again – which makes sense as business processes extend across the boundaries of silo’ed functionalities.

I also think that Salesforce did a great move further embracing and empowering the ecosystem. There is a very clear message that Salesforce wants to stand for making it easy for their customers to focus on their customers’ needs. Whether this needed the “my” prefix or not…

By enabling developers, admins, and users, to easily create the applications that they need to more effectively and efficiently meet customer needs they are giving a clear message. On top of this there is an increasing amount of industry specific (Bolt) solutions. Extending the learning platform Trailhead to embrace customers, with branding and content, augments this. It offers Salesforce customers to build and monitor their own learning maps in a corporate environment.

Improved support for collaboration is another aspect of this. Having a platform like Chatter is one thing, leveraging an integrated platform like Quip is on an entirely different level. Quip live apps offer the promise of having all relevant data and documentation for a task in one place – kind of a Slack on steroids. Here also the new integration with the G Suite and Salesforce that will help making data available where it is needed will play its role.

With all these new and enhanced features Salesforce showcases that customer happiness is the end and that the employee is the means to make the customer happy.

The (long overdue) move to allow customers to make Salesforce applications fit into their own brand is only the icing on the cake of this message. While this capability sounds like a little thing it is actually a biggie as it fosters commitment and, so far, was a distinguishing factor in favor of Salesforce competition. It is good to see this now; and it appears to be simpler than in other tools.

All in all this is very good news for Salesforce customers and for their customers. I am looking forward to seeing more of this in action.

Salesforce seemingly starting a multi cloud strategy is an interesting development. While offering customers the in their eyes best cloud alternative (Salesforce, AWS, or Google) is a good thing, it will be interesting to see how these two competing ‘preferred’ clouds will be offered. Both parties have a genuine interest in attracting serious business workloads. It remains to be seen whether there will be a regional overlap or a distinction. It also remains to be seen how AWS reacts to this announcement.

Some words of caution

Salesforce is historically very good at showcasing complex scenarios. In sales processes against competition (especially SAP) Salesforce proves this times and again. Tools like the Einstein Prediction Builder are perfect examples for this. Being able to build a custom AI model on any field or object to predict business outcomes is a great thing. However, it is unclear how the underlying algorithm got trained or how the training got tested. Further, there are still quite some limitations. Some of them one can find out using the Trailhead Einstein Prediction Builder Trail.

As usual, and as with other companies, too, customers are well advised to look under the shiny hood that sales demos are.

Edit 12/28/2020: Changed Einstein link and removed IoT Link in first enumeration as old links were broken.