According to an article in Venture Beat, the focus of many companies with artificial intelligence platforms is to use machine learning and predictive analytics to extract meaning from captured data. Meaningful analysis of this data has the potential to open up new business models, products and services.
Be specific, work on the design and unify teams
There are three elements to this approach. First, artificial intelligence needs to be tailored towards a specific business. There are no off-the-shelf platforms applicable to any business. This involves a careful examination of existing business processes and tasks to determine which aspects of the organization would most benefit most from AI automation. Second, smart, practical design is critical to artificial intelligence adoption. Most platforms will be accessed by non-IT experts and the use of systems needs to be straightforward and intuitive.
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Third, artificial intelligence should lead to the unification of information across teams and facilities, enabling collaboration and the pooling of company information. With each of these in place, the data analyzed is representative and likely to be free of bias.
Keep it simple, use real-time data and be adapatable
Success also needs to go hand-in-hand with organizational development, according to the Venture Beat guidance. Again there are three factors to consider. The first is to keep things simple, such as selecting artificial intelligence that can fit with current infrastructure and systems and automate with existing methodologies.
The second is to collect and analyze data in real-time. This includes using machine learning to evaluate and rank new data upon collection. This requires designing artificial intelligence to fit existing workflows collecting information as it happens to allow for an accurate representation of what might happen next.
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The third factor is making sure the system put in place is adaptable so that it keeps pace with the fast changing world of business. This not only keeps things in tune with customer expectations, lowers development costs; reduces deployment time; and shrinks infrastructure requirements.
These considerations are important for businesses, given the predicted growth in relation to different types of artificial intelligence. As a sign of growth, analysts 451 Research indicate that the total data market could reach nearly $140 billion by 2021.
