If you’re still pondering over the adoption of AI in your organization, you’re already running behind the league. And that is because the right time to start implementing AI is today, which means you should have started planning for it yesterday. But that doesn’t mean you should panic. If you start your planning today, you may well be on the road to implementing it within your enterprise soon, and that’s what matters. And regardless of how many write-ups warn you of the challenges and risks associated with AI, the truth is that if you wish to tap the endless potential of technology and its benefits, you need to capitalize on AI and its capabilities, starting NOW.
AI has dawned; it is here in the present, not the future. Whether you wish to enable new offerings, improve existing products, or simply explore and innovate, the use of AI has become the “in thing” for achieving enterprise goals. Even recent studies point to the fact that more and more enterprises are going the AI way with every passing year. A quick search for “AI Adoption in the Enterprise” will lead you to a survey for an O’Reilly report which highlights that as many as 75% of businesses are evaluating AI and its adoption. If that doesn’t point to a growing trend, what does?
Enterprises have several options when it comes to AI adoption
There’s never a single way to do things. And that’s true in terms of enterprise AI adoption as well. Businesses today have multiple options to execute their AI strategies:
- They may choose to build different applications for different use cases,
- They may choose to buy an AI vendor’s product for each different vertical
- They can also select a central cross-functional AI Platform to build all their enterprise-wide applications on a single platform
While the first two options seem fairly straightforward, the third one encompasses a rather broad perspective. Let us try and explore it a bit.
What is a Cross-functional Enterprise AI platform?
An enterprise AI platform allows business organizations to create structured yet flexible AI solutions for the present and future. It derives learning from the domain as well as functional knowledge and enterprise data to arrive at enterprise-wide intelligence in real time. At its surface, it assists AI services to transition smoothly from PoCs to production level systems.
Cross-functional enterprise AI platforms, a little differently, extract intelligence from real-time enterprise data and available knowledge and also continually learn and create knowledge for future intelligence. They heavily make use of cross-functional pattern recognition to derive knowledge from various business functions like marketing, IT, operations, finance, customers, etc. What’s more, when designed well, such platforms facilitate quicker and more efficient collaboration among AI scientists and engineers.
Enterprise Value in using Cross-Functional AI Platform
Although the benefits of a cross-functional AI platform seem largely evident, here they are in a summarized manner to make things more implicit:
- Such a platform can be used to design and develop multiple AI applications across different business functions
- It can offer unsupervised time series intelligence out-of-box
- It can combine time series data with knowledge to give real time intelligence
- It can prove to be a self-learning, self-healing, automated and efficient AI solution building haven
- It can deeply analyze information and uncover new revenue opportunities by giving a set of targeted recommendations and predictions
- It can quickly sense changes in market conditions and adapt its predictions accordingly
Additionally, such a platform can be highly successful in substituting expensive digital transformation projects which attempt to replace existing business systems. Cross-functional AI platforms allow you to retain your existing enterprise structure while offering the agility to respond to real time market requirements.
Rounding it up
A cross-functional AI platform can have huge ramifications for any enterprise. It helps contain costs, avoid duplication of efforts, automate low-value tasks, and improve reusability of work. What’s more, it can help in tackling skill gaps. Since it acts as a center point of all AI capabilities and more, such a platform can assists enterprises to be well positioned for advancements in AI use cases and supporting technologies