Although Greek mythology hinted at AI, it wasn’t until the 1980’s that it was introduced for commercial application. Public opinion then was somewhat skeptical, if not negative, about AI. However, advancements over the years and the current pandemic have created an environment of growing support and acceptability both from marketers and consumers.
A pre-pandemic survey by Demandbase, a B2B targeting, and personalization platform, revealed that 84% of sales professionals and marketers were either employing AI for their business operations or already planning and applying AI strategies. Today’s advanced algorithms provide great potential to be invaluable assets, but marketers also need to be constantly and personally engaged in the process. Here are other findings and recommendations to make AI successful.
Change is coming. 63% of nearly 2,300 global leader subscribers polled in the summer of 2019 by MIT SMR Connections said they expect AI to drive significant or dramatic change in their organizations. The important component to drive such success is collaboration, and 75% said they also expected to see increases in collaboration across departments. Another 70% anticipated more cross-training, while 55% said they would increase efforts to organize staff into multidisciplinary teams.
MIT SMR’s findings also confirmed Demandbase showing 23% already using AI and 55% en route to employing it. 62% also said they were spending more on AI in 2019 than they had in 2018.
Brands implementing AI can expect to see huge impacts on their technology leadership, particularly CIOs and chief analytics and data officers. This is where cross-training will be invaluable. Adopting Ai will place higher demands on IT staff and a need to be more agile. Software development and deployment are also likely to be affected. 61% of MIT SMR’s respondents expect AI to drive significant changes to software development processes. Respondents also agreed that as more data is collected, human oversight is important to review the results and initiate changes, where necessary.
In using AI, the top two issues driving data governance are compliance and security. However, only 46% of those polled by MIT SMR had a data governance program. Brands must install programs that ensure that data is secure and managed in compliance with regulations and that the data is used ethically and that its quality is trusted by those evaluating and using it.
Consumer trust toward brands using AI is important, and some brands have taken proactive measures to get in front of such concerns. 42% of those already using AI told MIT SMR they formed an ethics-focused AI review board. Another 40% have adopted a code of AI ethics, and 21% hired ethicists. It’s equally important that brands adopting one or more of these measures communicate this to their target audiences to allay any anxieties and nurture trust.
While some brands may elect to trust their algorithms totally, it would be wise to conduct regular A/B tests that will either confirm the strategic plan or suggest that adjustments need to be made. A hybrid plan of both AI and human oversight will help ensure the delivery of meaningful and measurable results.