Many of the emerging technologies that will be entering the market in 2033 are already known in some form in 2008, according to Gartner. Many of the innovations that will unfold during the next 25 years can be found today in research papers, patents, or are in a prototype in production.

These long-term innovations, taking place in five to 20 years, go beyond the range of the typical IT project portfolio planning cycle. These innovations are classified as "IT Grand Challenges." Gartner defines an IT Grand Challenge as a fundamental issue to be overcome within the field of IT whose resolutions will have broad and extremely beneficial economic, scientific or societal effects on all aspects of our lives.

Analysts said IT leaders must be more active in researching and identifying emerging technologies that will bring about benefits not realized today.

"IT leaders should always be looking ahead for the emerging technologies that will have a dramatic impact on their business, and information on many of these future innovations are already in some public domain," said Ken McGee, vice president and Gartner Fellow. "Today, CIOs should identify which Gartner IT Grand Challenges will be most meaningful for their enterprise. Then within the next 12 months, review patents for additional IT Grand Challenge candidates. Apply logical conclusions to Gartner emerging technologies, business and societal trends research to identify IT Grand Challenges. Lastly, identify preferred sites to monitor developing academic, government or corporate research on chosen Grand Challenges. There are technologies on the horizon that will completely transform your business."

Gartner has identified seven IT Grand Challenges. They include:

  • Never having to manually recharge devices: Today, the ubiquity of portable computing and communications devices powered by battery means that many people would find it highly desirable to either have their batteries charged remotely or their devices powered by a remote source, bypassing the use of batteries altogether. Despite more than 100 years of research since the invention of the Tesla Coil in the late nineteenth century, the most notable progress to date was achieved by the Massachusetts Institute of Technology (MIT) in July 2007 in their experiment to transfer nonradiative power. By this measure, any commercial application of wireless powering still seems a long way off.
  • Parallel Programming: Rather than simply creating faster single-core processors to perform tasks serially, another way to meet the constant demand for faster processor speed is to develop multiple, slower speed processors that perform tasks serially. Simulations, modeling, entertainment and massive data mining would all benefit from advances in parallel computing. However, a challenge with parallel computing is to create applications that fully exploit a "multi-core" architecture by dividing a problem into smaller individual problems addressed by individual processors. To overcome this, key issues will need to be addressed, including effectively breaking up processes into specific sub-processes, determining which tasks can be handled simultaneously by multiple processes, scheduling tasks to be processed simultaneously and designing the architecture of the parallel processing environment.
  • Non Tactile, Natural Computing Interface: The idea of interacting with computers without any mechanical interface has long been a desirable goal in computing. Some of the many challenges that remain in this area include the ability to detect gestures, developing a gesture dictionary and the need for real-time processing. Another set of challenges relate to natural language processing, which include speech synthesis, speech recognition, natural language understanding, natural language generation, machine translation and translating one natural language into another.
  • Automated Speech Translation: Once the many hurdles of natural language processing are overcome to yield human-to-computer communications in one language, the complexity extends further when translation and output is required to a target language that is understandable to a human. Some rudimentary systems have already been created to accomplish basic speech translation,