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Ronen Joined Carmel Ventures in February 2008 and currently serves as a General Partner. Ronen's investment areas focus on SaaS, big data, cyber security and internet.
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Doron is a successful entrepreneur and the Founder and CEO of Samanage.
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What’s in store for Enterprise SaaS? Some insights following Dreamforce 2016

By and October 10, 2016

We have just returned from this year’s Dreamforce ‘16 in San Francisco. In an impressive feat, Salesforce managed to bring together 175,000 professionals for 3 days of over 2,000 sessions covering virtually every aspect of the future of enterprise SaaS.

As is usually the case with these types of conferences, it was a bit overwhelming and hard to “see the forest for the trees” but we’d like to attempt to share our insights on the future of Enterprise SaaS while they are still fresh in our minds, so here goes:

Platforms

One of the known consequences of enterprises moving to SaaS was the fragmentation of horizontal solutions to different applications. Over the past 18 years, what used to be SAP or Oracle suites have broken down to tens of SaaS applications, each solving the problem of a single function in the organization: Salesforce for sales, NetSuite for finance, Kenexa and Taleo for recruitment, Workday for employee performance, Gainsight for customer success reps, Marketo for Lead Gen reps etc.

Even some of the consolidation that resulted from M&A (SAP acquiring Concur and SuccessFactor, IBM acquiring Kenexa, Oracle acquiring Taleo, NetSuite etc.) still left those applications as standalone solutions.

What Salesforce is pushing hard, and quite successfully, we might add, is the idea of going back to a single platform which offers a single user experience and seamless integration of data between applications, unified dashboards etc.

To demonstrate this idea, SalesForce laid out throughout Dreamforce16 not only a vision but also very impressive execution of what the new enterprise would look like. For example: A call center rep might send a service request to a field service agent who identifies an upsell opportunity on the customer’s site. The information would then be sent to the account manager who would promptly release a price quote and close the deal. All of this would have been achieved using the same UX, the same data made available to 4 different departments and 5 different people. Pretty impressive!

Ecosystem

Enterprise applications are not just about the streamlining of productivity and workflow anymore. Customers expect their SaaS vendors to offer a complete ecosystem and an application through which they would be able to access knowledge (industry benchmarks, solution templates etc.), get vendor recommendation for adjacent solutions and leverage the network to get their own customers. The value creation is moving up the chain and it goes way beyond product features.

This presents a great opportunity for startups. More and more startups are using the large platforms ecosystem (SalesForce’s appexchange, Oracle’s Cloud Marketplace, ServiceNowStore etc.) not only as a technical platform but as a major lead generation tool for customers who are looking for complementary products. These leads tend to be highly qualified, significantly reducing marketing costs and sales cycles.

This has also given rise to an interesting phenomenon, whereby choosing a development platform has suddenly gone from being a technical question to a go-to-market question.

Since the key criteria by which a startup chooses a platform is based on distribution and access to customers, technology is becoming less and less important.

Bots

Bots are everywhere. At the moment they have mostly manifesting as a marketing feature than a real value-add, but they’re there, and we believe that they will slowly transform into an important communication channel.

Bots are going to be customized and built according to specific use cases, with customer service being among the obvious first. Bots are great as “first line responders”, taking much of the load off level 1 support and call centers. It seems that not going “all in” but rather implementing a “hybrid” model by which a bot starts the conversation and “escalates” to a human support rep when needed is the ideal approach, leading to immediate benefits without waiting for the technology to be fully mature.

But the fact that Bots have entered our world so quickly (in less than a year) also means that it has commoditized extremely fast. The technological barrier is less complicated than one might think and is highly dependent on the amount and quality of the data rather than on algorithmic breakthroughs.

Bots will become a must-have feature in all enterprise applications as a means of communicating with employees and customers, but we’re skeptical as to whether this will develop into a category in its own right.

In addition, bots are emerging in both the enterprise space and the consumer space, just as apps did in the app world. It will be interesting to see who pushes the boundaries faster. In the app world, it was the consumer apps, but it remains to be seen if the same will happen in the bot world.

Artificial Intelligence and Machine Learning

Salesforce’s biggest announcement at Dreamforce16 was Salesforce Einstein, its Artificial Intelligence and machine learning layer. The technology behind it seems pretty basic (although it includes nice clustering and classification features, some predictive analytics, and perhaps most importantly some automatic propagation of data and fields to replace manual entry) but again, Salesforce is clearly pointing the way. It’s not about productivity anymore, it’s about automatic data entry and automatic decision making.

Like Bots, this is more of an enabling technology rather than a category in itself, but unlike bots it is not a feature, it is a complete redefinition of Enterprise Application.

In our opinion, starting today, no enterprise application will succeed unless is offers the additional value of allowing its users to collect data automatically, and then guiding them on what to do with it. AI and insights will become the UI layer on top of complex data-driven offerings, such as benchmarking, because that’s essentially what customers want: The bottom line and the insight.

Can/should startups build it themselves, or will they expect the platform to provide the AI fabric to their products? That’s what SalesForce is counting on as a competitive advantage to attract more startups.

All in all, a Dreamforce 2016 presented a lot of food for thoughts. Your comments and thoughts are more than welcome!

 

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