It's no secret that A.I. is everywhere these days.  It's behind Waze telling you a best route, Google suggesting a typo correction, and those really personalized ads on your Facebook newsfeed.  

But what you may not realize is that it's not just massive consumer facing platforms that are part of the revolution.   Big corporations and B2B companies that focus on data solutions and AI--like Palantir, Oracle, and new divisions within IBM, Google, and Apple--are multiplying in numbers and success stories.  

Whether you are a business looking to improve content management, use data for more strategic decisions, analyze market reports, or even generate sales and client leads--it's becoming clear that big and small companies need A.I. and cannot afford to trail behind competitors that are quicker to leverage automation.  

But figuring out how and where to use A.I., let alone executing on a plan, isn't exactly easy.

While a lot of consulting and B2B companies will claim to give you all the answers, very few of them can explain what A.I. is and how works to solve your specific problems. 

I did find Shore Group, though--one B2B A.I. and data company that has its finger on the pulse.  They seem to have figured it out, and they can explain how to get the most out of new technologies in terms that actually make sense. 

Shore Group is unique in that they offer solutions that focus on how to use A.I. and what needs to be done to raw information to make it leveragable before A.I. is employed.

At Shore, Ian Sax and his new partner, Ariel Katz lead their team, serving clients who range from fortune 500 companies to small businesses looking to utilize A.I. "AI isn't black magic, contrary to what's being said," Sax told me on that point.  "Most of what you can get out of machine learning techniques or any algorithms boils down to the forethought, architecture, and normalization going into the data structures that the algorithms work with."  

All of this is great in theory, but why does Shore Group really stand out when it comes to execution, because they know that the key to future success lies within leveraging A.I. and this is what they want us to understand:

You must develop the right team:

Shore understands that it takes a the right group of people, to make up a great team.  Their projects have informatics scientists, research analysts, and software developers working to execute on the business requirements of a dataset being prepped for use with AI.  Katz, explained that for businesses, "Computer programs are only good as last the last domain expert to set up the protocols.  You actually need to really understand the data's real-world significance and constraints--and then A.I. can speed up processing and aggregating times."

You must define your source of information:

After getting the right people, and placing them on the right teams, the second step is always research. At Shore Group, they spend a lot of time identifying the best sources for the information needed, then documenting how they store that content. The important part, is knowing where the information is and how how each source is likely to update -- essential steps to maintaining any dataset that relies on dynamic, changing records.

You must map connections and distinctions:

At Shore Group, experience has taught them that it's critical to map out how these various sources communicate information in their own unique formats and languages, because they often deal with the same themes in different words. If, for example, you are merging your are contact records and notes from five different email accounts, it's critical to specify upfront how sources might conflict or complement each other, from both a technical and logical standpoint.

You must create a common language:

Each project Shore Group undertakes is unique to their client's needs.  Katz explained that "goals should shape the data categories and methods for collection and upkeep.  Choosing the optimal methods to collect, combine, and standardize it all--the "common language" the sources are translated to--is often the hardest part of any data and AI project.  It has to factor in the use-cases of the end-result, which dictate how the data will be delivered and used--RSS feeds, Excel, SQL databases, and the list goes on.

You must be flexible to function in the future:

Through so many years of experience, Shore Group realized that it is essential to set up quality control and maintenance protocols that work with that "common language" directly.  This means you don't have to reinvent the wheel every time sources change or records need to be edited, and your organization will be able to benefit from the data and AI processing in the long term.

As I have found in my research, A.I. is not a phase, it's not a fad, it's a tool that is here to stay and most of the successful well known industries are already using it. Are you? And if so, are you using it right?