At the recent Cisco WebexOne conference in Fort Lauderdale, I was struck by something that featured guest Alexandr Wang, founder of Scale, said during the opening keynote with Jeetu Patel. When Jeetu asked Alex to share his thoughts on how companies should be thinking about AI, Alex responded (I’m paraphrasing) that every enterprise is sitting on data that is unique, specialized, and differentiated, and that the most exciting area is how companies will use their data to advance AI.
This view highlights the evolution of AI in the enterprise. The last year or so largely focused on bringing AI into organizations in somewhat limited fashion, with a great deal of attention paid to how companies can restrict what is shared with large language models for training purposes. Companies in the collaboration space have introduced opt-in policies to assure customers that no company data is being used for training purposes. This paradigm is now changing.
Metcalfe’s Law states that value of a communications network is directly related to the size of the network. That is, as connections on the network grow, so too does the value of the network itself. In AI, the same is true but for data. Companies that successfully feed their “unique, specialized, and differentiated” data into their AI LLMs will gain an advantage over those who can’t (or won’t). This is because the value of the AI increases as it provides insights from, and acts on, company proprietary data.
In the enterprise collaboration space, the need to broaden data access to AI is changing how companies view their collaboration applications as well as the capabilities that vendors are bringing to market. Those responsible for implementing AI solutions, especially in best-of-breed shops, are faced with the challenge of providing useful AI tools that may live within their application siloes. For example, at Metrigy we use a variety of collaboration apps including Google Workspace, Zoom Workplace, Slack, Microsoft Teams, Notion, and more. Each of these apps now offer their own generative AI tool, but AI virtual assistants within each one of these apps only has access to the data within the app itself. That is, if I ask Google Gemini for insights about my conversations, it won’t know about meetings in Zoom, or chats in Teams, and so on. This new reality led us to question if the growing availability of generative AI, and the need to broaden its access to data, would drive a consolidation of collaboration vendors in the enterprise.
In Metrigy’s soon to be published Employee Engagement: 2025 global research study of 400 companies, we asked IT and collaboration leaders how AI would impact their go-forward vendor strategies. About 23% said that they thought AI would lead to vendor consolidation in their environment. Another 46% were trying to gauge impact. Just 25.6% said they would make no changes to their best-of-breed vendor strategies.
Obviously, consolidation is a threat to smaller vendors given the oversized presence of Microsoft in the enterprise collaboration landscape. As a result, vendors are moving rapidly to introduce openness to their AI offerings, and to broaden their data reach. Witness recent announcements including:
Beyond integrations and data sharing among collaboration vendors, several companies that we study are also deploying AI platforms from companies such as Anthropic, Google, Microsoft, OpenAI, Perplexity, and more. All these platforms have the potential to augment, or supplement generative AI virtual assistants (or copilots) resident within the collaboration apps themselves.
Indeed, a few research participants that I interviewed told me that their long-term strategy around generative AI was much more focused on these large independent platforms rather than on leveraging the generative AI capabilities directly within their collaboration apps. I expect that as large-scale AI platform deployments increase, so too will the demand for collaboration vendors to share data via APIs with them.
For enterprises to differentiate themselves with AI, they must provide their AI algorithms with greater access to their proprietary company information – obviously doing so in in a manner that ensures governance, compliance, and security.
About Metrigy: Metrigy is an innovative research and advisory firm focusing on the rapidly changing areas of workplace collaboration, digital workplace, digital transformation, customer experience and employee experience—along with several related technologies. Metrigy delivers strategic guidance and informative content, backed by primary research metrics and analysis, for technology providers and enterprise organizations
