How to Successfully Implement AI for Managing Organisational Change

Why AI Requires Organisational Change

Let’s take a moment to consider why companies moving towards becoming AI-driven must undergo structural change in the first place. In our digital age line-of-business teams (such as marketing, HR, finance) have plenty of business problems they want to solve, yet they run into all kinds of hurdles when trying to make AI a reality, including:

●        Lack of or incomplete data.

●        Data projects relying on limited statistical models (as opposed to more sophisticated machine learning models).

●        Trouble deploying and automating models caused by complex links with frontend systems.

●        Insufficient tools or easy access that allow them to dig into data themselves.

Furthermore, even when projects prove successful, their continued maintenance can result in ongoing challenges for non-technical teams (in case you’re unaware, machine learning models aren't like software - they require significant time and monetary investment). 

Talent, Tasks, Technology

Things don’t stop there - hiring data scientists or business analysts and putting them on a team together to create AI projects is a great way to start; however, don’t forget that data use needs to be pervasive and democratised throughout the business, so that all staff has a baseline ability to use data to make informed decisions. This is why huge organisational change is so important: the truth is most teams and larger organisations are not built to be data driven from the bottom up.

Although the road to data democratisation doesn't mean all employees need to transform into data scientists overnight. Rather, the answer is:

●        Accessibility with one another, for instance instead of  setting the expectation that all business people should be data science experts (or that each business team is assigned with its own data scientist or data expert), it's essential to nurture an environment that permits people of various data abilities to collaborate to solve problems and build AI solutions.

●        Creating processes that enable staff at all levels to use data to make decisions. Incorporating simple (but controlled) links directly connected to data sources - which avoids back-and-forth data requests for IT and budget around spreadsheets - as well as easy ways to share projects between people for validation and cross-checking.

●        Spending money on technology and solutions that not only allow these employees and processes, but are also sustainable, ensuring a firm investment in the future. As a result, companies are not just equipped with open source, which is cutting-edge, but considering proprietary software on top as well to offer a simple user experience no matter what level of technological skill the person has.

Get Ahead of Organisational Change Management

There’s no doubt that companies are experiencing rapid change with AI. What does this mean for business professionals - and broader companies – and what steps can they take now to prepare themselves for the data-driven times ahead?

1. Educate: Know and understand the basics of machine learning, deep learning, and AI. Executives or managers need to be able to empower their teams to have a baseline level of knowledge on these subjects. A few suggested resources are Machine Learning Basics - an Illustrated Guide for Non-Technical Readers, Introduction to Deep Learning, and Data Architecture Basics.
2. Collaborate: Invest in technology (such as data science, machine learning, or AI platform) that can be leveraged by data experts and data beginners alike, for everything from managing data projects to connecting to data themselves. Why Enterprises Need Data Science, Machine Learning, and AI Platforms offers insight into what they can provide.
3. Confidence: Begin driving change by opting for at least two or three simple data projects that would offer more insight or efficiency and work together with data experts to get started. Why not just one project? Data science isn't really an exact science, so it's possible that the right data doesn't exist for an individual project. Or it is executed, but the results aren't helpful. Kick-start with a few low-hanging fruits to up the chances that at least one is a success. Get more tips for running a data science POC.