A strategy for making the best cognitive computing investments now
Cognitive computing is making great strides in replicating many human abilities, from visual and auditory perception to making decisions using large and complex data sets. As cognitive computing capabilities grow, so do opportunities for transforming organizations, from how infrastructure is provisioned and managed to how customers are engaged and delighted. Yet it’s possible some organizations will be disappointed in cognitive computing because they will try to adopt a less mature or emerging application, or fail to anticipate the disruption cognitive computing can create in operations and processes.
A heuristic approach, customized to the needs of an individual organization, is an effective way to deploy cognitive computing capabilities where they will generate measurable business results and help pave the way for more advanced applications and technologies as they develop. Following these four steps will help organizations build a practical and yet comprehensive deployment strategy.
1. Categorize the maturity of the technologies being considered
It’s smart to deploy cognitive computing applications where they will have an immediate impact and measurable return. The most mature technologies, meaning those that are going mainstream and are commercially available, are most likely to meet these criteria.
Then assess the organization’s appetite for adopting those mature technologies. A retailer may view an application that can assess a customer’s attitude from visual or aural data as viable and vital, while a financial organization may decide the same tool is too leading edge for its immediate needs.
2. Identify immediate opportunities for adoption
Find opportunities in the organization where deploying cognitive computing capabilities will quickly realize quantifiable returns. These savings and/or new revenues can be invested in additional cognitive computing projects. Succeeding with mature, mainstream technologies also builds organizational confidence in cognitive computing. Examples of maturing cognitive technologies include advanced OCR, chatbots and applications for commercially available virtual assistants.
3. Pilot and prototype emerging areas
Identify use cases for combinations of mature and emerging cognitive computing technologies for business transformation in the longer term. These projects will have a time-to-maturity of about one to three years. A critical learning in this phase is anticipating and planning for managing the disruption cognitive computing will cause to the business. Applications to explore include those based on deep learning, natural language processing, higher level sensory perception, etc.
4. Assess moonshot projects
Continuing advances in cognitive computing’s ability to mimic human sensory perception, thinking and judgment calls make it possible for organizations to consider completely new possibilities for their operating models, core competencies and value propositions. Many of these truly disruptive advances may not be fully ready for implementation for several years such as fully functional intelligent holographic assistants.
Forming a center of excellence comprised of business, creative and technology experts will ensure the organization has a wide array of perspectives about how new research, emerging solutions and proof of concepts could contribute to transformative services, products and experiences.
Taking this heuristic approach ensures that an organization will see immediate return from mature cognitive computing technologies with minimal risk. Gaining experience with practical cognitive computing will enable organizations to envision additional uses for the technology and begin to design solutions incorporating its current, and future, capabilities. They can also anticipate a future in which cognitive applications will free humans from rote drudge work, giving them time and energy to unleash uniquely human creativity—a quality that even the most advanced cognitive applications are unlikely to mimic.