6 . Artificial Intelligence Change

We are at the dawn of a new era in digital transformation, machine learning and Artificial Intelligence (AI). Technology plays a role, but people and change leadership play a bigger role.

An Agile mindset and experimental approach lay a foundation for us to design our future in a way that technology works best for humanity (and not the other way around!).

Like never before, AI machines now process information better than humans due to ”big” data sets, open platforms, increased computational speed, and machine learning (ML). Permeating much of our daily lives, AI lurks in the background every time we browse our Facebook newsfeed, search on Google, get a song recommendation from Spotify or receive a same day delivery from Amazon.

AI is rapidly growing from machines doing specific tasks intelligently like self-driving cars, detecting fraud, or understanding human text and speech. Companies have much to gain by harnessing AI’s strategic advantage yet are employees and leaders across the enterprise adopting digital/AI practices intelligently?

More so, the odds are stacked for organizations. AI has a long development cycle with high failure rate with less than 2% of proof of concepts making their way to fruition. Of those that succeed, the long-term adoption of AI post implementation is 60% at best. Regardless of where your organization falls on the capability spectrum, going digital is a sure bet in today’s world and into the future.

With that in mind, let’s explore practical ways to leverage Agile in the “people/employee side” of AI transformation, based on my first-hand experience with large organizations in both the private and public sector.

Here are seven Agile practices to effectively lead AI change for your organization:

Meet People Where They Are

In order to effectively build a bridge from current to future state, one must first assess where organizations and individuals are in their data science journey.

Some organizations are just starting to dabble with the possibility of AI. They may just want to “check the box” to comply with regulation or to get good PR press. Maybe they are starting to see the benefits of automation in some functions but still rely heavily on external expertise.

On the other end of the spectrum, organizations like Google, Spotify and Amazon were born digital with AI in their DNA. With fully integrated data pipelines fueling their AI factory, business decisions are anchored in data and smart systems.

From an individual perspective, we can assess whether a person understands basic concepts in modern technologies, has the needed technical skills, and the capacity for change and adaptability. Wherever a company or individual is today is your only starting point for progression. Expecting behavioral change in rapid pace or all at once is simply unrealistic. Therefore, our approach should meet people where they are today and allow time for steady, gradual progress towards the desired future potential.

Develop Data Literacy

Often overlooked is the need to establish a common understanding of advanced technologies (i.e., AI, Augmented Reality (AR), Deep Learning (DL), etc.) so that employees can exchange ideas and collaborate in the same language.

We collectively need a “deeper” understanding of how complex machines work and evolve in an ever-changing world.

Put simply, algorithms involve data inputs, a model and outputs which can be turned into insights, decisions, and actions. Regardless of a person’s role and technical expertise, everyone can think like a data scientist by asking key questions: What and why data matters? What story does data tell us? How can everyday work and decisions be enhanced?

Additionally, it is helpful to establish a shared understanding of key concepts such as computer vision, machine learning, supervised learning, unsupervised learning, features, data bias and privacy to name a few. Data literacy opens the realm for meaningful conversation and exploration of what is possible.