In my last post I explained what big tech companies mean when they talk about artificial intelligence in CRM. Today, we’ll see what aspects of their AI strategies Microsoft and Salesforce have implemented, and how their visions compare for the future.
Now that we know what Microsoft and Salesforce mean when they refer to AI in their CRM, there’s still a lot to consider about how their different artificial intelligence platforms work, what they’re capable of doing and where they’re capable of doing it.
Both the Salesforce product and Dynamics CRM 2016 already include some machine learning aspects that are capable of surfacing opportunities for users, but Salesforce and Microsoft both recently found opportunities to refine their offerings and reinforce their AI messaging.
Salesforce Works to Align its Acquisitions
For Salesforce, this came at Dreamforce, the company’s huge, hype-generating annual conference in San Francisco. That’s where Salesforce formally introduced Einstein, which incorporates the company’s many AI-related acquisitions under one brand. Einstein is currently in the early stages of its public rollout, and many of its most promising features are as many as six months away (or more).
Salesforce is planning to introduce Einstein in its winter release, with 35 tools available by February across its stack. In addition to providing functionality in traditional aspects of CRM (sales, marketing, customer service), Einstein includes tools like product recommendations in Commerce Cloud, Salesforce’s rebranding of ecommerce platform acquisition Demandware.
Einstein is being positioned as “everyone’s data scientist,” which is why the company is opening Einstein up to developers building on the Salesforce platform in 2017. Once this happens, software vendors will be able to integrate Einstein’s machine learning and artificial intelligence capabilities without even needing to use any APIs.
When it comes to Salesforce’s own tools, Einstein amounts to more of a grab bag of features than the unified vision the company’s marketing materials promise. As ZDNet points out, this is primarily the result of Einstein being built from technology acquired from as many as nine separate AI focused companies.
While there’s no doubt that Salesforce will eventually be able to integrate Einstein as thoroughly as they promise, given the company’s clear commitment and engineering talent, it will likely be a while before the results in anything truly transcendent. In the meantime, we have a variety of time savers, many of which do seem quite useful.
Microsoft – Decades of AI Research Coming to Fruition?
Just days before the public got their first extended look at Dynamics 365, Takeshi Numoto, Microsoft’s Corporate VP, Cloud + Enterprise, posted an introductory blog that included rather pointed language aimed at Microsoft’s competitors in the AI for business technology industry.
With so much attention on artificial intelligence and the promise it holds, it can be hard to tell what’s real and what’s not. When it comes to business process – and your business – Dynamics 365 delivers the intelligence you need to transform, now, backed by decades of research and investment.
Microsoft’s not-particularly-subtle argument is that, because most their AI and machine learning capabilities were developed internally (rather than via acquisition, which is what Salesforce has had to do), they’re
- more integrated
- more intelligent
- part of a singular vision for AI
Of course, until Dynamics 365 becomes available to the public in the beginning of November, it’s difficult to tell what effect Microsoft’s history of AI development will have on the usability and power of its CRM.
While Microsoft has namechecked Cortana intelligence in presentations about Dynamics 365, the company hasn’t been as eager to place all its AI capabilities under the umbrella of a single entity, à la Einstein. Instead, Dynamics 365’s capabilities are powered by a combination of Azure Machine Learning and Cortana Intelligence that are embedded into Dynamics as a whole.
Understanding the macro vision is only a part of the story, though – it’s products and capabilities that will or won’t sell users on Microsoft’s AI initiatives. For now, that vision can be summed up in two powerful individual tools that are all about using AI to get more out of relationships.
Accordingly, the first of these tools is Relationship Insights. Relationship Insights uses machine learning, natural language processing, sentiment analysis and data that it can call upon from Dynamics and Outlook to give salespeople reports about the status of their customer relationship at any given time. In addition to providing information about relationship health, it’s capable of offering actionable solutions, from suggesting that it’s time to pursue another sale or that immediate intervention is necessary to maintain a customer.
The other big tool is Customer Insights, a stand-alone service that integrates internal and external sources and promises to give organizations a fuller picture of their customers (and even offer insights about how to improve engagement).
Is Either Company Ahead Right Now?
Microsoft and Salesforce have each boasted about the power of their AI products using similar terms. For one, they both claim to be democratizing AI and, of course, they both claim that their platforms are the industry’s most powerful. Unfortunately, we (and the rest of the market) won’t get time to fully these claims for ourselves until the companies finish their rollouts in 2017.
For now, we can probably give an edge to Microsoft in terms of sheer machine learning and artificial intelligence capacity – the company has spent more time than almost anyone else on AI research and development, both inside and outside of the business solutions industry.
Salesforce, on the other hand, is still many months away from totally integrating the technology of companies like BeyondCore, which are poised to form a significant part of Einstein. What Salesforce does have, though, is its passionate evangelists and development partners. If the independent vendors who make the apps that populate AppExchange embrace Einstein, then it will have an early advantage in terms of the breadth of its applications across industries and verticals.
This leads to the one prominent issue standing in the way of both companies’ ultimate success (and one that deserves its own post) – the willingness of their customers and partners to share their own data with Salesforce or Microsoft. Without customer data to feed to their machine learning models, it would become much more difficult for either company to improve their AI products over time.