GPT is a productivity model and OpenAI is disrupting productivity.
Apparently this is making productivity SaaS anxious, and Clickup AI's promotional video is a misguided pointer to the current AI strategy of these "old" companies, which is to race to roll out similar use cases in their own product scenarios.
While the use cases themselves are largely homogenized, we have observed some unique value propositions - for example, around "data," SaaS that truly serves enterprise customers does not, as one might first think, "train LLMs on data retained within their own software. train LLMs on data that resides within their own software", but rather emphasize the trust of cloud delivery, datasets free of copyright and forensic risk, and a better ability to help customers make data work for their models.
So while it is still impossible to identify exactly which SaaS will benefit in the long run and which SaaS will suffer, it is already possible to see which companies have truly differentiated strategies for dealing with this. Along these lines, this article has selected the 3 approaches and multiple case studies at Level 2 and Level 1, respectively, that stood out to me the most. But even with these strategies in place, productivity SaaSs still have to contend with pricing, competition with ChatGPT, and a host of challenges on their long-term product roadmaps, and the battle for the productivity portal is in the midst of a fierce knockout stage.
It's important to note that there are two definitions of productivity SaaS, one broad and one narrow. The narrow one usually refers only to the core scenarios around documents (e.g., Notion), tasks (e.g., ClickUp), videos (e.g., Zoom), and forms (e.g., Airtable), while the broad one encompasses the majority of SaaS that can improve the efficiency of a company's operations, ranging from huge CRMs to nifty products like Zapier. to nifty automation tools like Zapier. The productivity SaaS discussed in this article is more on the broad side of the latter.
Summary:
Value propositions and business strategies of Tier 2 giants "Overseas Unicorns' Search for a Place to Stay 5 challenges with AI capabilities in SaaS today As we noted in our Kick article, we have observed that the percentage of so-called "All in AI" investors in Silicon Valley is much lower than we expected. One common perception is that Generative AI doesn't bring the same new user base and corresponding customer acquisition channels as the mobile internet and the cloud, and therefore the established players with a Go-To-Market advantage have the upper hand. Another mainstream perception is that "it is too early to invest in AI Native applications, it is more cost-effective to let your existing Portfolio use GenAI first".
From the business perspective of "old" companies, we have fully experienced the three waves of PC, mobile Internet and cloud, and almost every professional manager and outstanding entrepreneur has learned from various cases of falling behind, so we will not let go of the big things in our mindset. And GenAI is on the rampage, the consensus is full, no company should feel that this wave of opportunity is too small, so they are all special FOMO and clear entry.
However, at this stage, the actual use cases of GenAI launched by various companies are not very different from each other, which are the combination of "understanding", "generation" and "reasoning" in various scenarios, and it is very easy to get tired of it after reading more. It's easy to get tired of it. Putting aside these specific use cases that have been discussed enough, and combining the experience of visiting and researching in Silicon Valley, we will pick 3 value propositions and strategies that we think are more interesting from each of the secondary giants and primary market unicorns to talk about in detail.
- Value propositions and business strategies of the second-tier giants
Playing with Data from Multiple Perspectives
First angle: don't touch customer data
"Data can be a very powerful value proposition, but it's not the first thing we think of when we say, "There's a lot of CRM data on Salesforce, or a lot of prospect meeting records on Zoom, which can be used to train a powerful model! ". Rather, ensuring that you don't use customer data to train a model or automate a task on behalf of a customer without authorization is one of the most important value propositions for these types of companies, and the first way to play with data - creating trust.
GenAI may be a productivity tool for large enterprise customers, but it is also a new technology that is difficult to trust, and SaaS providers that specialize in privacy and security for large enterprise customers will need to help GenAI cross this gap.
When it comes to trust and privacy these days, "open source + local deployment" is a no-brainer. On top of this obvious approach, delivering privacy and security in the cloud is a fundamental skill that SaaS providers who can serve large enterprise customers have built up over the past 10 years. Take Slack, for example -- in order to expand its customer base from SMBs and Mid-Markets to large enterprises, it's gone to great lengths to customize its standard data encryption scheme in a more sophisticated way by integrating with AWS's Key Management Service, which gives customers control over keys, and Slack then invokes the service to deliver the keys to the customer. By integrating with AWS's Key Management Service, Slack lets customers control the keys, and Slack then uses the service to encrypt user data by invoking the keys set by the customer. This level of "security and control" is also very common in Microsoft productivity tools.
With "hands-off" customer data as the baseline for trust, a customized privacy and data security solution for GenAI has the opportunity to become a differentiated value proposition in the face of homogenized use cases. Microsoft is deep in this area, but has not pushed this selling point externally. Instead, Salesforce's Einstein GPT is using this value proposition more, and after announcing Einstein GPT in March, the biggest recent development has been the launch of Trust Layer, which attempts to drive home the message that it is the "most trusted" GenAI Offering. The most recent development since the announcement of Einstein GPT in March has been the launch of Trust Layer, which attempts to reinforce its image as the "most trusted" GenAI Offering.