There’s plenty of hype surrounding AI and machine learning, but does it really help businesses? Or is it all just noise?
The answer can be seen in the following statistics:
- Integrating workplace systems with AI has increased productivity by 54% of surveyed executives.
- Thanks to AI, Global GDP will grow by $15.7 trillion as of 2030.
These numbers indicate that businesses who haven’t already, should find a way to adopt AI and machine learning for its vast ability to streamline operations and improve efficiencies by automating repetitive tasks, process data analytics, and to create natural language processing.
However, artificial intelligence isn’t a magic bullet. There are ways to incorporate the related technologies, and there are mistakes to avoid.
Data Scientists and AI Teams Must Work in Harmony With IT
An article from ZDNet explains one crucial hurdle that needs overcoming for AI to thrive in workplaces.
Namely, there’s a disconnect between the teams of data scientists immersed in the AI tech and IT teams.
Without harmony between these entities, AI implementation will likely become disjointed and can’t offer much value.
That’s why many businesses are relying on AI integration technology similar to ModelOps, which allows IT teams to bridge gaps with production and analytics. Using this method in your workplace ensures AI projects are more streamlined, focusing more on delivery, usability, and sustainability for your business.
Here are the four elements of AI integration that businesses must focus on:
For an ecosystem to be AI-ready, it must be “open,” as in Open Neural Network Exchange Format (or, ONNX). From there, it can evolve.
As such, companies must develop next-generation integration architecture. Doing so will enforce open standards, to which external parties can adapt with ease.
Businesses must fully grasp the crucial role data plays in AI and how to make AI enhanced data available for production and training.
There’s then a need for data tagging and labeling for future usage. This includes even the data you don’t know you’ll use later. The ultimate goal is to create an enterprise inventory of data. This way, future projects will benefit from a single source of aggregate data that is easily searchable.
Successful AI implementation hinges on platforms that allow you to flexibly swap out pieces of the platforms depending on the needs of the business.
When sourcing the appropriate platform for your cloud strategy, considering the following elements:
- How many cloud service providers (CSPs) will you use?
- For different initiatives, would you use different CSPs?
- Will some workloads run on-premises while others use a CSP?
Outside of data, AI, and analytics teams, your workforce must be on board with the new AI technologies. They should take a sense of ownership in the new processes and procedures of AI. Without the experts, your business won’t be able to leverage the powerful advantage of AI tech and deliver results for your company.
Are you in need of top-performing data, AI, and analytics talent to help execute a successful artificial intelligence initiative?
If so, contact Synergy Systems today for the highest tier candidates on the job market.