While the benefits are huge for AI, this tech needs and relies on Business process automation (BPA) to truly provide quicker ROI in deployments.
Propelled by advances in computing power, the penetration of big data, and broader access to advanced machine learning, AI is likely to see massive adoption between now and 2020. In many cases, AI has been the catalyst to finally convince companies to commit to Digital Transformation initiatives. According to the report Reshaping Business with Artificial Intelligence, 84% of respondents say AI enables them to obtain or sustain a competitive advantage and 83% believe AI is a strategic priority for their businesses.
But today, early adopters of AI are reporting a disconnect between expectations and related to AI technology. These early projects are consuming time and money, with limited or lower than expected returns. According to a study by Xplenty, “30% of business intelligence professionals still spend at least half of their time cleansing and formatting raw data to make it ‘analytics-ready.’”
As a result, businesses are often slow to unlock their data’s true potential for revenue or operational improvements. One of the biggest misperceptions about Artificial Intelligence is that there is an “out-of-the-box/one-size-fits-all approach,” and it’s this misperception that seems to highlight why early attempts to adopt AI are failing.
As the Xplenty study found, aggregating and normalizing data is critical for AI success, and while many companies have created cross-application “data lakes,” this has not reached broad adoption. Not only does the fragmented data from various applications make it impossible to have an effective dataset, but it creates disjointed or disconnected processes without a full view of the outcome to train algorithms and machine learning models. According to Forrester’s latest research, 55% of firms that invested in AI, have not yet achieved any tangible business outcomes from it.
Without a complete overview of the data flows, connected to goals, AI is unable to optimize predictions or processes. This is where companies who have already adopted business process automation (BPA) technology have an advantage.
Benefit #1: Data Unification – There are numerous sayings in the data science community, some not fit to print, about how the quality of the data feeding the models determines the quality of the insights. The issue is not just data quality, but data completeness. Finding a pattern in a fragment of the data will lead to a fragmented conclusion.
