Setting the Stage Now for Artificial Intelligence Success in 2019
Are your investments in Artificial Intelligence doomed from the start?
The latest projections estimate that the market for Artificial Intelligence (AI) will exceed $190 billion by 2024, driven largely by the explosive growth of information. With good reason, organizations are clamoring to invest in the technology. AI has the potential to significantly change the way we live and work for the better. However, and this is an extremely important caveat, without the proper infrastructure in place, efforts to implement AI within an organization will almost certainly fail.
As with many trending technologies, all too often companies invest now and ask questions later for fear of being left behind or not being viewed as “innovative”.
We saw this with Big Data; organizations invested millions in acquiring data but then lacked the tools and infrastructure to make real sense of that data in a way that led to actionable business insights. This “invest now, ask questions later” mentality not only often leads to failure, and it also breeds an organizational environment that’s change adverse; if previous investments in new tech have failed, each subsequent project garners less and less buy-in.
For organizations looking to integrate AI into their technology stack in 2019, the following four elements must be in place before a single dollar is invested in order to yield any meaningful insights.
Read More: Why Manufacturers Are Turning To Seebo For Process-Based AI
PEOPLE, PEOPLE, PEOPLE
While this may seem counterintuitive, since AI is so often framed in the context of replacing jobs, it’s imperative to start with the right people to guide the technology. Most often, the ‘right people’ means a mix of data scientists, software engineers, and data engineers.
Critically important, however, are leaders within the organization that understand the value and the limitations of AI. These visionaries will champion the benefits of AI while setting realistic expectations for what it can achieve. Collectively, this mix of experts will help glean the right insights from AI and avoid the all too common, “junk in, junk out” situation.
More on AI: How AI Will Disrupt Retail Brokerages
Culture is inextricably connected to the people. Organizations need to make sure AI is defined and understood by the people that are going to interact with or receive insights from, the technology.
Educating these users around both AI’s strengths and shortcomings will enable them with the necessary skill set to work with AI “in the wild”; this means not being afraid to speak up when they perceive a red flag, while also not throwing up a red flag because they’re uncomfortable with or don’t understand the technology.
Train Your Tech
AI has massive potential for cost savings, increased efficiency, and unparalleled predictive accuracy across industries. However, the technology is only as powerful as you teach it to be.
For maximum ROI, take the time to teach AI any data and insights that are already known, thoroughly understand how the modeling is working, and then continuously recalibrate and tweak the models accordingly to make sure the results are as accurate as possible.
Recommended: The Top 5 “Recipes” That Give AI Projects A Higher Likelihood Of Success
Start Small with Artificial Intelligence
AI is not a magic bullet. It’s unwise to expect AI to solve an organization’s highest-value strategic problems from the jump. Begin by identifying small, yet impactful, business challenges where AI can be applied, and iterate from there. Perhaps the most critical: don’t adopt AI for the sake of adopting AI.
Adopt AI to solve for business challenges.
Adopt AI to solve for business challenges. This may seem obvious, but there are literally thousands of people sitting in offices right now, having forgotten this tenet, pushing for AI without understanding what business challenges they’re really solving. Not having a clear plan for what business problem AI is solving for is what most often leads to the scenarios where the results from AI are “bad” or not usable.
With these foundational elements in place, the good stuff can begin. As many businesses struggle with how to keep pace with a rapidly evolving business landscape, setting up the proper infrastructure for AI now will pay dividends in the future.
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