Last Updated: July 26, 2021
Every day, advances in artificial intelligence (AI) are making businesses more sophisticated than ever.
This is creating new opportunities for businesses to roll out practices that can change their industries for good. Put simply, the companies that innovate and make use of this technology will succeed. The ones that do not are likely to fall behind.
However, it can be difficult for businesses to keep pace with the rapid advances being made when it comes to artificial intelligence technology, and the many myths and misconceptions out there about AI have hindered adoption.
Books, movies and television shows have all played a role in fueling misunderstandings about artificial intelligence. In addition, some business leaders have unrealistic expectations about what AI can really achieve. Therefore, when it comes to this emerging technology, we must separate science fiction from science fact.
Top myths about artificial intelligence (AI) in the marketplace:
Myth #1: AI can make business decisions.
Despite major advances in AI technology, businesses still need leaders with years of experience and wisdom. These leaders provide necessary context and strategic insight to the data provided by AI platforms.
Indeed, the AI solution a business uses is only as good as how well the business understands the AI’s methodology including its limits and strengths. AI built for natural language processing, for example, is not suited for predicting sales trends or consumer behavior.
Smart business leaders will use AI strategically to give them better information to make better decisions.
Myth #2: AI will replace existing BI tools and even render previous investments obsolete.
Because the majority of AI platforms are cloud-based and structured as a SaaS solution, they can scale quickly and be implemented in phases.
Often, AI platforms for business have a web-only implementation, allowing business users to test the benefits of the software without any replacement necessary.
Prevedere customers see the most success when integrating the software directly into their ERP or internal BI platforms. This way, they can receive the most updated predictive models, using global data, to validate their own internal plans and strategies. Artificial intelligence augments existing technology investments instead of eliminating them.
Myth #3: An AI solution will require building a team of experienced data scientists.
Many AI-based analytics solutions are geared toward the “citizen data scientist,” defined by Gartner as someone “who creates or generates models that use advanced diagnostic analytics or predictive and prescriptive capabilities, but whose primary job function is outside the field of statistics and analytics.”
These are usually existing employees in the business who understand the basics of analytics, but more importantly their business and industry. Existing employees may be the best to train on a new AI platform to augment their skills.
For companies that have large data teams, AI can still benefit them. Using AI in analytics and business forecasting, for example, data scientists will be able to spend far less time gathering, cleaning and interpreting data and focus their energies on generating forward-thinking analysis and insights. If advanced analytics skills are required, many companies often augment their existing data teams with external vendors, who are usually trained in multiple AI software platforms.
Despite the myths, AI is becoming widely adopted with each passing year. Companies that seize this opportunity now and develop internal practices that incorporate technology will have a distinct advantage over those that lag behind.
Using AI-powered tools like Prevedere to predict consumer demand is a great way to phase in the adoption of AI technology within an organization. To learn more about how Prevedere can upgrade your organization’s business intelligence efforts, download our new whitepaper, The Evolution of Business Intelligence, made in partnership with Microsoft and the University of Notre Dame’s Dr. Barry P. Keating. Read the full report >>