Although the noise surrounding artificial intelligence or AI may seem deafening, AI requires deep mathematical understanding often only found at academic institutions or enterprise organizations like Microsoft Corp., Google and Amazon. It will take several years before mainstream businesses can create their own AI models and algorithms in real time.
As a result, AI services marketplaces will begin to emerge in 2020 much like app stores, and these new marketplaces will resell specialized AI services and algorithms that companies can instantly buy and implement within their business.
As we pointed out in our predictions last year, AI is only as intelligent as the data behind it, and we are still not yet at a point where enough organizations can harvest their data well enough to fulfill their AI dreams. So you better get moving. If you don’t have AI embedded in your applications by 2021, you’ll begin to lag behind your competition.
Enterprise, Hybrid Cloud Deployments Accelerate
In 2017, we saw an increasing number of companies shift from kicking the tires on the use of cloud and big data technologies to actually implementing enterprise deployments, with many taking a hybrid approach. In 2018, more will follow suit. “Enterprise spending on cloud services and infrastructure will be more than $530 billion by 2021 and in excess of 90% of enterprises will use multiple cloud services and platforms,” IDC predicts.
Take NTT Docomo, a leading Japanese telecommunications company, for example. The company is building a cloud based enterprise data lake on AWS to improve efficiency and collaboration across the entire portfolio of its companies. Executives across many industries are realizing they need to allow users to securely access data efficiently without having to request authorization from multiple systems and to build infrastructure so their teams are fully equipped to handle big data analytics.
A hybrid approach allows companies to obtain the cost savings of the cloud while protecting its intellectual property and data on-premises.
Use of Digital Twins Inform Business Strategy
Digital representations of physical structures, or digital twins, have been used for years in complex 3D renderings. But innovations in data analytics and IoT have pushed advances in 3D modeling to augment business strategies and decision-making in the enterprise. In 2018, more organizations will implement digital twins to visualize complex technologies and achieve new efficiencies with an increasingly digital approach.
“The adoption of, and hype around, digital twins is growing,” says Roy Schulte, vice president at Gartner. “Digital twins are the next step in the Internet of Things (IoT) driven world, where CIOs are increasingly leveraging IoT technologies in their digital business journey.”
EU Banking API Revolution
The European Union’s PSD2 directive is set to create new competition for banks, more options for consumers, and fundamentally new business models based on shared data.
PSD2 allows third party businesses like Amazon access to your bank account – with your permission – rather than having you go through PayPal or Visa, and it opens up APIs so that new players, think Mint.com, for example, to consolidate all your various accounts. PSD2 also brings stronger security for those shopping online in the EU.
Data security, data integrity and consumer trust will vault to the forefront, and APIs will become mission-critical to banking. Set to take effect in January 2018, it will help the EU banking industry shift from being slow adopters to tech innovators. Obviously, any U.S. firm doing business in the EU will have to comply with these new regulations.
Edge Computing Accelerates
For IoT, self-driving cars and the next phase of AR wearables to go mainstream, computing needs to happen instantaneously on devices in what’s called Edge Computing. After all, no one will want to ride in a car that has to rely on cloud servers for information during emergencies.
Advanced data capabilities are needed to make the data processing and analytics happen in rea- time on devices. For example, limited compute and storage capabilities will necessitate a program to examine data quality and sort through what’s needed and what’s noise in the terabytes of information now being captured. Look more companies to incorporate edge computing into their strategies. Long haul trucking and trucking manufacturers are already moving in this direction.
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