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Data Science, Machine Learning and Artificial Intelligence in Organizational Change Management

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Is Change Management using the latest technology? There are so many exciting opportunities for Change Management Practitioners to drive increased adoption with AI, data science and machine learning!  While I have not used any of these yet, and haven’t found anyone who has, I’m looking forward to them.

This article will:

  • define change management, AI, data science and machine learning
  • cover the benefits
  • discuss when can most of us expect to see it

What Data Science, Machine Learning and Artificial Intelligence Are (And How They Work For Change Managers?

Let’s start with defining our topics to make sure we understand the meanings I’ll be using.

Change Management

Prosci, a global leader in change management solutions, has been sharing insights on change management since 2009. According to their site, “Change management is the discipline that guides how we prepare, equip and support individuals to successfully adopt change in order to drive organizational success and outcomes.”

I like to think of change management as simply helping increase people’s adoption of the change, while reducing their resistance. You can find details in this change management article.

Data Science

Data Science (DS), as defined by Alan Bostakian in a recent presentation to Change Managers who belong the the Association of Change Management Professionals®, is extracting data to answer a question.  IT uses tools (algebra, programs), methodologies and techniques to understand data for driving insights and values from data. “Data scientists have changed almost every industry. In medicine, their algorithms help predict patient side effects” (Mae Rice).  See Mae’s article for more examples of industries change by data science. Data science, which differs from data analytics, is about looking into the future, while data analytics looks at the past.

Investopedia explains data science’s popularity to “advances in technology, the Internet, social media, and the use of technology have all increased access to big data” and data science can enable us to make decisions from the overwhelming about of data that is now available.

Artificial Intelligence (AI)

In Alan Bostakian’s presentation “The Application and Impact of Data Science and Artificial Intelligence on Change Management” on August 27,2020, he defines AI as the “development of computer systems that have a level of intelligence (mostly focused on problem-solving) as well as learning capabilities. An example of AI is Apple’s Siri or Amazon’s Alexa.

Machine Learning

Machine learning uses AI to deliver a task and make decisions, per Alan Bostakian. He says machine learning gives computers the ability to learn from data and environment which improves accuracy, recommendations and efficiencies. An example of machine learning would be when Netflix suggests movies you might like.

Benefits of Applying Data Science and Artificial Intelligence in Change Management

Applying data science and AI could improve risk identification, risk mitigation and predict failure probability. Imagine not having to spend countless hours having to pull, clean up and organize human resource data! What if simulations could show the results of changing certain variables in a mere minute?  For example, we could prioritize better if we knew that focusing on Sponsor activity with the project team would improve adoption faster than focusing on building organizational change management maturity.

Another area that would benefit would be our survey process. Chatbot surveys, can create a more comfortable experience for the adopter vs. email. It could more like an interview or a conversation instead of static survey (and it would save us the hours of collecting all those survey responses). The results would be a better understanding of where the adopters are in the change process.

According to “AI-Powered Surveys: Hyped or Helpful?” (Marctech’s Mark Szabo’s article), “unless we’re dealing with highly structured data (e.g., Net Promoter Score), we still need human intervention to make sure the two types of data are speaking the same language. That said, AI can create incredibly fast access to the types of quantitative and qualitative data that surveys often take time to uncover, which does indeed bode very well for increased speed to insight.”

Other areas that would benefit would be:

  • Stakeholder Analysis
  • Virtual Instructor
  • Content Development
  • Smart Content
  • Gamification
  • Virtual Mentors
  • Real-time Monitoring
  • Dynamic Dashboards
  • Adoption Planning
  • Stakeholder Adoption
  • Sponsor Effectiveness
  • Corrective Actions

The benefits are certainly numerous.  When do we think we’ll see this?

When Can We Expect To See Benefits?

Naturally, it depends.  We are in the middle of a pandemic where some companies are cutting back on resources, while others are moving quickly to gain market share by utilizing state-of-the-art technology.  Prior to COVID-19, the article AI Success Relies on Strong Organizational Change Management, written in April 2019 indicated implementations were on the rise. “AI deployments have tripled in the past year and 37% of organizations surveyed said they have implemented AI in some form. Moreover, the number of organizations implementing AI grew 270% in just four years. Usage numbers like those, paired with another Gartner finding that organizations are planning to increase IT budgets by 2.9% in 2019, signal that leadership teams are serious about committing resources to AI-powered solutions.”

The future is coming and the benefits are exciting!

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