While extremely disruptive, the last two years have catalyzed existing enterprise tech trends rather than creating new ones.
For significant expenditures like artificial intelligence (AI), automation, and cloud technology, organizations accelerated multi-year technological roadmaps, finishing them in months or even weeks.
But the future is still on the way. Today’s innovations will be the legacy of the future generations of companies and enterprises. So CEOs must be aware of significant breakthroughs and capabilities expected in the coming decade in order to ride upswings, avoid obstacles, and prevent interest payments on their ultimate technical debt.
But most predictions of future technology have a terrible signal-to-noise ratio, introducing a whirlwind of buzzwords that makes people anxious year after year.
Determining the subset of upcoming technology advances that can improve customer experiences, modernize operations, and foster competitive advantage is the focus of much future research in this regard.
The next ten years will see profound changes in business due to three rising tech branches: quantum technologies, exponential intelligence, and ambient computing, which we’ll approach in this article.
A growing number of ever-smaller screens may be used to sum up the last two decades of human-computer interface. We are today surrounded by digital information thanks to the widespread use of sophisticated mobile devices and cutting-edge networks in both our homes and places of employment.
Ambient computing anticipates a future transcending mobile devices in which our connection with the digital world occurs less on displays and more through unobtrusive, intuitive capabilities that more naturally meet our requirements.
Virtual reality and streaming, for example, are not new and both technologies have been playing a big role in the entertainment industry, with immersive gaming offerings like live game shows and VR simulations becoming a huge trend in the past few years. However, companies are increasingly using these technologies as business tools rather than entertainment devices to assist tasks as diverse as training, team building, and truck driving for distant operations. These investments, paired with improved dash cams, allow businesses to make the most of new technologies and increase their operational effectiveness.
Forgoing the physics lecture, quantum-powered solutions take advantage of the peculiar characteristics of subatomic particles to enable us to solve problems that initially appear to be insurmountable using physics rather than mathematics. Quantum represents a jump as significant as the one made by digital over analog.
As quantum R&D moves from research to development, the competition between technology behemoths, authorities, and early-stage entrepreneurs will swiftly find commercial applications.
By processing massive data sets in novel ways, quantum computing can resolve complicated computational issues. Quantum computers have proven they can do complex operations that take conventional supercomputers hundreds of years to complete in only five minutes.
By producing potentially tamper-proof networks that can identify efforts at interception and eavesdropping, quantum communication is a hardware-based technique that has the potential to significantly improve cybersecurity. Emerging technologies like quantum key distribution (QKD), a more secure method to exchange encryption keys to transport data across optical networks, will be used for this secure communication.
The attractiveness of quantum to tech enthusiasts is obvious, but business leaders must still take into account its potential to provide specific competitive advantages in relation to certain business needs.
In the past, descriptive business intelligence solutions that found and exposed hidden relationships in data sets were the most extensively used. Predictive analytics, or algorithms that can further anticipate what is likely to happen next, have grown in popularity over the previous 15 years.
In recent years, enterprises powered by AI have embraced machine intelligence to supplement or automate human decision-making. In order to better comprehend and imitate human emotion and intent, next-generation AI will progressively collect human behavioral data at scale as it advances from analyst to predictor to actor. The “affective” or “emotional” AI era has now begun.
A smile, a moment of reflection, or a choice of words can all be viewed by a machine as data that, when combined, can help a company gain a more complete understanding of its clients, staff members, citizens, and pupils. Organizations can use its data to create various types of automated systems that will help them better make connections between their financial, social, and ethical goals.
The business justifications for these inventive devices are strong for caregivers, salespeople, stage actors, and customer service reps. But for leaders to effectively decrease the danger of implicit and explicit bias in training data, models, and final systems, they must commit to trustworthy AI practices.