The world of business is rapidly changing with new challenges daily presenting themselves for organizations to overcome. Companies face evolving stakeholder needs, uncertain economic conditions, increased regulations, and other unforeseen circumstances, such as a global pandemic.
Companies adopting emerging technologies, however, such as artificial intelligence (AI), hope to benefit from the competitive advantages provided by these technologies. For example, cost savings and heightened revenues are made possible through increased efficiency and enhanced customer products and experiences, all of which are made possible through the deployment of emerging technologies.
Emerging technologies are a huge part of the present and significant drivers of the future. AI, virtual reality, augmented reality, and blockchain have all evolved to find their niche applications. Each technology is maturing while overcoming challenges.
There is, however, another side to emerging technologies. Companies adopting emerging technologies must not only consider the benefits, but also the risks, associated with these technologies. One risk is that these technologies will not meet companies’ expectations. A clear vision needs to be established from the beginning and supported by defined objectives with realistic targets.
Even as most of the world talks about disruption in terms of mobility, consensus, and connectivity, technologists are focused on the emerging innovations that will shape what’s next.
Sustainability and related issues may once have been thought to be concerns important to younger generations, but over time, it’s becoming increasingly clear these are universal concerns. Emerging technologies are being utilized to enable the development of new products and services that use less energy, chemicals, and water and reduce waste from operations.
Choosing the best data preparation tools for emerging technologies like machine learning can be a challenge. Fitting the needs of the organization makes choosing the right vendor and solution a complicated process that requires in-depth research and often comes down to more than just the solution and its technical capabilities.
Most organizations have little knowledge regarding how AI systems make their decisions or how the results are applied. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Melody K. Smith
Sponsored by Data Harmony, harmonizing knowledge for a better search experience.
