The media buzz that surrounds Big Data, artificial intelligence (AI) and machine learning (ML) has never been higher, so much so that it can overshadow the real applications and actual outcomes companies are working on. But larger than life promises or hype might have an eclipsing affect around the actual, realistic benefits it provides to almost any organization, in a wide variety of industries, that are generating a large volume of data.
The use case benefits are real and it’s time for your company to start harnessing them. But before your organization can receive the value from AL and ML, it must get a thorough understanding of the role it can play at your business, the problems it can solve, and how it can align to your company’s objectives or intended outcome. The problem is that this is where a lot of AI and ML proof of concept (POC) initiatives have stalled and not made a lot of headways. To overcome this, companies need a place to start from. Smaller companies can take a cue card from tech giants such as Google which has started to make bets in solving large scale healthcare issues.
When enterprises take on digital transformation initiatives, they may underestimate the power to which digital has been altering industry landscape dynamics, consumer behaviors, economic fundamentals and what it means to stay competitive and relevant with its customers. People often forget how far we have come in a short period of time. The processing power of the latest iPhone is thousands of times faster and stronger than all of NASA’s computers were (combined) during the Apollo moon landing days. These mini computer devices are starting to connect to most of the world’s population, and this all happened since 2007.
Nowadays, iPad tablets and mobile devices have become almost symbiotically interwoven into many facets of our lives. In cities, most people can’t go anywhere without relying on navigational and ridesharing apps such as Google maps or Uber. Planning trips and vacations often come with using apps such as Airbnb, Trip advisor or friend recommendations on things to do, see and eat on Facebook. And even on vacation as long as there is a Wi-Fi connection, you can use your phone to check your email, listen to music, post and share experiences to Instagram, check your investment portfolio, automate and monitor your home, facetime your relatives, search the internet for movie or gift recommendations.
My job as Big Data Strategy professor and tech entrepreneur entails coaching and consulting with CIOs, CDOs, CTOs of enterprise organizations. But, when I look at these magical little smart phone devices, and all of the digital changes and technology innovations it can and will enable going forward, it usually makes me want to have a ‘come to Jesus moment’ with them. Some executives might have the belief that having a few technology initiatives in the works
such as AI pilot programs, automation and the integration of physical supply chains with digital technology is enough, well it’s not.
You can’t seem to get away from seeing tech headlines about how many companies want to adopt or use AI for their Business Intelligence applications and technologies. From a Forbes article titled ‘“Preparing Your Business For The Artificial Intelligence Revolution” to a Martech advisor one called “How to Reshape Your Business Strategy Around AI.”
But how does a company plan and go about implementing Artificial Intelligence technologies into their business? The first step is to figure out the reason for wanting to use AI in the first place. Digital organizations are beginning to experiment and incorporate different types of AI applications such as Artificial Neural Networks, or Machine Learning into their processes and products for tasks such as image recognition, NLP, sales forecasting, autonomous driving, robotics, patient data processing, fraud detection, personalized recommendations, and chatbots. Forbes contributor Terrence Mills, wrote an article called “Machine Learning vs Artificial Intelligence: how they are different” which provides good overview summary of the what each does.
MIT Sloan review discussed why AI implementation ischallenging. The path to implementing AI can be a costly and a long-term investment. It is not as easy as moving your business into the cloud or implementing a new Big Data Analytics tool. One of the main reasons that implementing AI can becost prohibitiveand complicated is due to the fact that every use case or application of it needs different algorithms and technology tools for it all to work within the system or processes to make it successful. There is no one size fits all AI algorithm.
If your company is one of the 84 percent that believe that investing AI will lead to competitive advantages and you are looking to explore your options for adopting cognitive technologies such as AI into your business, then here are a few tips:
Asha Saxena discusses the importance of healthcare leader cohesion with Chief Data Officer at John Hopkins Healthcare, Nick Minale.
In this episode, we explore the emergence of coordinated, mutually aligned partnerships created to maintain constant healthcare for the entire population. From the care continuum of care data to the role of facility standardization, the episode will pinpoint the uses—and risks—of our ever-growing information world.
Data, today, drives everything. Let us equip you with the modern knowledge you need to understand the healthcare world’s newest opportunities—as everything from AI to data governance suites are changing the way providers conduct business. Continue reading