If your enterprise isn’t already thinking about your AI-driven transformation, what are you waiting for?
84% of digital transformation executives say that DSML is the single most important factor for the future success of their organization—and that figure is expected to rise to 90% in the next 2-3 years. Having an AI strategy in place is essential to turning your existing data into a competitive advantage.
But, being successful requires more than just selecting the right data science platform and identifying impactful use cases. Getting all your people on board and ensuring a smooth transition to a data-driven, AI-powered enterprise is perhaps the biggest hurdle to overcome.
So, what’s the secret to executing your AI-driven transformation without a hitch? Change management. Read on, and we’ll explain how all the pieces fit together.
How Change Management Fits into Your AI Implementation
Bringing AI into your organization is daunting. Incorporating it successfully into your existing processes may seem nearly impossible. Here’s how change management can help make the transition easier.
What is Change Management?
First off, what exactly is change management? Put simply, it’s a framework that’s in place to successfully transition to new processes, initiatives, or goals in the organization. It’s an ongoing process, typically executed by leadership, that starts with the inception of an organization-wide change and continues as that change is deployed, executed, and monitored for years to come.
Change management typically involves:
- Getting stakeholder buy-in and approval—Are members of the board and leadership team committed to an AI-driven transformation?
- Planning responsibilities + training—How are you going to educate your organization, and how will you adapt training for members of different teams?
- Ongoing communication—How will you announce the transition, and how will you communicate during training, etc.?
- Project management—Do you have a plan in place for delegating tasks, optimizing the use of the company’s resources, and ensuring you stay on budget?
5 Step Process for Effective Change Management
If your enterprise decides to move forward with an AI-driven transformation without a change management framework in place, you risk a disjointed, unorganized transition that leads to low employee buy-in and an even lower success rate.
We recommend approaching change management in these five steps.
1. Define Scope
Embarking on a business transformation without first defining its scope would be like setting off on a road trip to unfamiliar territory without a GPS—you’re guaranteed to get lost.
Start by scrutinizing every team and brainstorming ways that AI could impact their daily role. Can the marketing team use your new data science platform to segment customers more effectively and create campaigns with higher conversion rates? Can your IT team leverage AI to detect malware and better protect your assets?
This process helps devise an initial list of potential use cases, team members you’ll need to get involved, and a timeline for execution. This is a foundational step that most organizations neglect, and it’s critical for success.
2. Identify Potential Roadblocks
Despite your best efforts, it’s unlikely that your transition will go 100% smoothly. By proactively identifying possible sticking points, you can develop solutions before they evolve into uncontrollable, urgent problems.
At a base level, make sure that any new platforms and tooling you’re evaluating to support the transition will integrate with your existing tech stack. Ensuring old systems can talk to new ones will save you a lot of headaches down the line.
Another common roadblock to implementing AI solutions is employee resistance from those without “data” in their job title. While AI is transformational, many employees might not be as excited as you’d hope about changing how they work. By prioritizing how your employees will react, and showing them the positive impacts AI will have on their roles specifically, you run less risk of opposition.
On the other hand, teams who are working directly on AI use cases often operate in a silo with little-to-no visibility into how their work impacts the rest of the organization.
If demand forecasting can help your sales team get a better picture of market demand, this might influence upcoming marketing campaigns and store inventory. AI-powered improvements will save your teams’ resources, but they’re more complex than they first seem–don’t forget to account for that.
3. Develop a Training Plan
Now that you’ve identified whose job will be affected and how it will change, the next step is figuring out what skills they need to learn to ensure AI adoption goes seamlessly throughout the enterprise.
In many cases, data science is viewed as being the job of, well, data scientists. But, if you truly want to integrate AI throughout your organization, you’ll need to empower everyone to make an impact with data science—this is where upskilling comes into play.
Rather than spending valuable resources on hiring new data scientists or external consultants who know nothing about your operations, why not teach your existing business experts how to manipulate the data they encounter every day to produce real business results?
Enabling users across the organization, whether they’re domain experts, analysts, or managers on the shop floor, ensures that every area of your business is experiencing the benefits of data science.
A wealth of online learning resources exists, but many are constructed for data scientists and neglect secondary roles. It’s important to prioritize a system that meets your workforce where they are and provides them with foundational data science knowledge (like RapidMiner’s skills-based Academy).
4. Communicate Change Effectively
When you’re plotting a game-changing AI-driven transformation, communication might not be the first thing that comes to your mind—but it should be a top priority.
How can you expect your employees to comply with and be enthusiastic about an initiative you’ve told them next-to-nothing about? Why would someone want to take a data science training course if they don’t understand why it’s important for their role, or how it’s going to improve their work?
When thinking about how to communicate this major change, break it down into two categories—how and why.
How? Will you send out a company-wide email or host a meeting (whether in-person, virtual, or hybrid)? Will you join the daily stand-up meetings for individual teams? Or, will you ask leadership team members to communicate the message to their departments individually?
The why is a bit more nuanced. Why is AI necessary? What benefits will it have for their role? This answer requires more thought, as it’s different for everyone across the enterprise.
Each organization is different, so there’s no silver bullet approach, but your communication surrounding the change should always be intentional and well thought out.
5. Oversee Long-Term Success and Continue Improving!
Project management is as essential to change management as AI is to your organization’s future success—make sure you stay organized and continue monitoring the transition even after implementation has taken place.
The eventual goal of your initiative is increasing the adoption of AI throughout the organization and building trust in the outcomes. As a part of your change management process, you should always know if you’re on target to meet your goal—develop metrics that track organization-wide adoption rates, number of hours spent in your data science platform, and percentage of processes that have been altered with AI.
You’ll also want to keep track of the impact AI is having on your business—how many employee hours have you saved? How much money? What are you now capable of that your competition is not? Develop a few key touchpoints that easily demonstrate the value of your work.
Keeping track of these metrics, as well as having regular conversations with key stakeholders, will also let you know when things aren’t going smoothly, so you can quickly course correct.
When done correctly, change management can and will bring out the best benefits of leading a successful AI-driven transformation—your employees learn new skills, are more connected to the changes, and feel they’re contributing to something exciting and innovative. On top of that, you’ll be experiencing all the wonderful benefits of data science you’ve read about in the news—increased revenue, reduced risk, and lower costs.
Sounds like a win-win, right?
If you’re on board with the change management process, but unsure of who should lead the charge, check out our A Leader’s Guide to Successful DX ebook. We break down what’s expected from a leader and how to learn from some of the best (and worst) examples of AI-driven transformation leadership.