AI is important for three simple reasons. Learn what those are now.
Artificial Intelligence will be a big deal in 2018. But for those of us who aren’t PhDs or data scientists, how do you separate hype from reality?
Why is AI important, and how can we articulate this importance within different industries and organizations?
I’m neither a PhD or data scientist, yet I know AI is important for three simple reasons:
#1 – AI Requires a Product Mindset
Even with all the advances in natural language processing and machine learning algorithms, AI isn’t something you can buy off-the-shelf. Why? Because AI is rather primitive when compared with the human brain’s ability for contextual learning and predictive inference.
To be successful with artificial intelligence requires us to have a product mindset. Unlike projects, products have a longer lifecycle. Products require fixed teams which focus on outcomes instead of outputs.
This fosters innovation, leading to greater freedom and responsibility to deliver AI-enabled “products” that provide the best possible experience for customers, employees and business partners.
#2 – AI Rationalizes Big Data Investments
For years, we’ve been spending big dollars on ig Data. We’ve got data lakes which are fed by raw data from websites, mobile apps, social media, marketing campaigns, back-office systems and third party vendors. However, the typical human brain – and our busy lives – doesn’t permit us the time and energy to extract insights in this real-time, always-connected digital world.
So now what?
Artificial Intelligence has a limitless appetite for data. AI is built on expectations and probability that inform underlying predictive models and associations between raw data and likely intent. By combining big data and artificial intelligence with human-centered design thinking practices, we can begin to finally demonstrate return on investment of big data investments.
#3 – AI Improves Collaboration Between Marketers, Technologists and Operations
We’ve been fortunate to work with clients on their AI journey. While every organization is different, we have observed some initial best practices: Marketing, IT, Operations and Product teams are getting in a room, building and executing a plan together.
Getting to success with AI requires a digital ecosystem approach. You can use platforms like Facebook Wit.ai, Microsoft Luis.ai or Amazon Sagemaker, but you’ll need technical staff to code for the platform, software vendors who are flexible, and consultants who keep everyone honest and the product development lifecycle from getting bogged down.