Over the past year, we’ve seen major software companies, both B2B and B2C, start to say they’ll incorporate generative AI into their products. But as we enter 2024, it turns out these companies aren’t actually launching new AI “products”; they’re just giving names to AI technologies.
These companies are telling the world they’ve launched their own AI, giving it a name, but not introducing any new products. Instead of launching new products, they’re upgrading all their existing products with AI.
For example, Twilio announced CustomerAI in October. CustomerAI isn’t a product itself, but it’s used in Twilio Segment, Twilio Engage, Twilio Flex, etc. Similarly, Salesforce launched their “Einstein” back in 2016, and now they’re applying this name to generative AI, but you won’t find a product called Einstein; it’s integrated into various Salesforce applications.
Shopify also has “Shopify Magic” used in several areas like Sidekick, App Review Summaries. It’s not standalone but integrated everywhere. The same applies to Tableau, which has “TableauGPT.”
What does this trend tell us, besides reminding software giants to name their AI?
1. Generative AI is about widespread integration into many areas
After research, these companies find AI very helpful and want to join in, giving AI a name. However, their use of AI isn’t limited to a single product but spans across multiple, even dozens of areas.
2. Deploying AI in several areas requires a standalone team
Initially, companies might hold seminars or bring in instructors for each department, but soon they find that everyone is already busy, and it’s challenging to take on additional AI tasks. Since AI is crucial for competition, the best way to implement AI is to establish a dedicated team. That’s why AI has its own name and department, aiding multiple departments in integrating AI.
3. AI development brings significant organizational and communication challenges
AI teams with distinct names but integrated into various departments must blend well within the company, posing considerable organizational and communication challenges for large corporations.
4. Emergence of a new era of AI Agency services
If internal execution of such AI teams is ineffective, outsourcing becomes necessary. This new type of outsourcing includes more careful execution, more human communication, and deeper contract bindings to ensure the agency can coexist with the company. Perhaps the agency offers a specific AI kit along with services.
So, companies will continue discussing whether certain products should use AI or not. The decision to use or not use AI hides different considerations, and perhaps compared to complex human collaboration, AI might become the easiest part.
