Every second, modern trends bring the “new rules of technology games.” To win, companies are trying to conform to rushing IT environments. What a strong reason for companies to increasingly invest in digital transformation!
The future will be characterized by smart devices delivering increasingly insightful digital services everywhere. We call this the intelligent digital mesh. – David W. Cearley, Distinguished VP Analyst, Gartner
Interesting to observe that average spend has risen from $24 million in 2017–2018, to $27 million in 2018–2019, and is expected to reach $30 million in the next 12 months (as per Couchbase’s report).
Gartner has listed the top 10 strategic technology trends for 2019, among which digital ethics and privacy, autonomous things, and augmented analytics.
Many companies store backups of huge volumes of personal data that is both sensitive and vulnerable without the intention of using it. Clients are concerned about how much of their personal data is being collected and how that data is being used.
In nowadays world of privacy, every company should ask, “Just because we can gather all information about a person, should we?”
Discussing privacy must be grounded in the broader topic of digital ethics and trust, which moves the conversation from “are we compliant” to “are we doing the right thing.” The switching from the compliance-driven to ethics-driven approach should be on top of intent.
At the essential level, autonomous things use artificial intelligence (AI) to automate tasks or functions usually performed by humans. Autonomous things use AI to smoothen interaction between environments, though they vary in capability, coordination, and intelligence levels.
They may be successfully implemented and effectively used in the following cases:
Google Maps, which can analyze the traffic speed at any time.
Ridesharing apps like Uber and Lyft, which determine the price of your trip, minimize the wait and optimally match you with other passengers to minimize detours. Uber’s use of machine learning for ETAs for rides, estimated meal delivery times on UberEATS, computing optimal pickup locations, as well as for fraud detection.
Using an AI autopilot in commercial flights. The New York Times revealed, “In a recent survey of airline pilots, those operating Boeing 777s reported that they spent just seven minutes manually piloting their planes in a typical flight.”
Fraud prevention. AI is used to create systems that learn what types of transactions are fraudulent. Factors that may affect the neural network’s final output include the recent frequency of transactions, transaction size, and the kind of retailer involved.
Intent detection in social networks (for example, Wit application identifies and manages users’ intent on Facebook).
Machine learning in surgical robotics. Suturing — the process of sewing up an open wound or incision. This important part of soft-tissue surgery can also be a time-consuming process. Automation could potentially reduce the length of surgical procedures and surgeon fatigue.
Targeted advertising — recommendations for products you’re interested in as “customers who viewed this item also viewed” and “customers who bought this item also bought.” This strategy could be implemented, for example, via personalized recommendations on the home page, bottom of item pages, and through email.
Smart personal assistants. The first iteration presents simple phone assistants like Siri and Google Now (Google Assistant), which could perform internet searches, set reminders, and integrate with your calendar. Amazon has enriched this range with Alexa, an AI-powered personal assistant that performs voice commands to create to-do lists, order items online, set reminders, and answers questions (via internet searches), and Echo (and later, Dot), smart speakers that allow you to integrate Alexa into your living room and use voice commands to ask natural language questions, play music, order some meal, call Uber, and integrate with IoT devices. Microsoft has followed this range with Cortana, the AI assistant that comes preloaded on Windows computers and Microsoft smartphones.
Beauty applications, which use facial recognition technology to let users virtually try cosmetics products from lipstick to mascara.
AI also contributes to creating digital twins, which mirror real-life objects, processes or systems. Companies collect real-time operating data from product-mounted sensors, and then, use these data to create an exact replica of a working product, process, or service. This exact replica called a digital twin is a synthetic model in a virtual space that performs under real-world conditions to help companies find performance issues, schedule predictive maintenance, reduce downtimes, and minimize warranty expenses. Because digital twins can give real-time status of equipment or other physical assets, they are very helpful in manufacturing to reduce maintenance issues and ensure optimal production output.
The new age of AI strategies indicates an increasing interest in the potential benefits and costs of AI (see CIFAR report):
North America led by the US dominates the global AI market. Over the last five years, San Francisco’s Bay Area, in particular, has attracted 41% of all global investments in AI.
Europe ranks second in the global AI market attributed to the growing demand for cybersecurity, medical informatics, marketing, fraud detection, and national intelligence. The EU is targeting $24.4b of investment in AI by 2020 to compete with the US and China.
China is investing heavily in AI technologies and start-ups to become a global leader. The country’s 2030 plan envisions building a $150b AI industry.
Augmented analytics is using machine learning (ML) and natural language processing (NLP) to strengthen data analytics, data sharing, and business intelligence. The concept of augmented intelligence, a surrounding concept to augmented analytics, was mentioned by the research firm Gartner, in their 2019 edition of the “Hype Cycle for Emerging Technologies.”
Gartner Hype Cycle for Emerging Technologies
Collecting and analyzing data is the engine of digital transformation. Businesses are boosting their data to processing in order to improve users’ experiences, optimize operations, and quickly create new products and services.