7 Steps To Operationalize AI In Your Organization
By Monika Mueller
Softensity’s EVP Consulting Services and Head of LATAM Monika Mueller is a Forbes Technology Council member, and this article originally appeared on Forbes.com.
A lot of people are anxious about AI. How long until it replaces my job? Can a robot do my work better than me? Will adopting AI at my organization lead to a reduction in headcount? These fears are understandable for a disruptive technology that’s moving forward at a blistering pace. While most organizations are eager to tap into the capabilities of artificial intelligence, knowing where to start can be overwhelming.
The key to success lies in slow and deliberate implementation. Here are seven steps to operationalize AI in your organization.
Adjust Your Organization’s Mindset
Is AI a friend or foe? Even if no one is voicing this concern, it’s likely on employees’ minds. Your team may be resistant to adopting AI because it brings to mind layoffs and reduced headcount. It’s your job to address the elephant in the room.
When used correctly, AI can be an enabler and a career accelerator. It’s a valuable tool that helps free up team members’ time so they can do more, accomplish more and be more strategic in their roles. Let AI handle the simple, mundane and repetitive tasks so they can focus on more impactful work that can help drive growth within an organization, impact a company’s revenue or drive down costs.
AI can help employers get more value out of their team by enabling both their personal and organizational growth. So rather than seeing AI as a replacement, employees should think of the technology as an ally—a personal helper, consultant or assistant to do the tasks they’d rather skip.
Understand What AI Can Do
Acquiring fundamental knowledge about both what AI can do and how it may be applied to your organization is a crucial step. At this point, we’re all collectively getting up to speed on the technology’s applications and capabilities. Fortunately, there are plenty of resources out there to help.
Find credible sources of information, educate yourself and become literate on all of the diverse aspects that make up AI, from machine learning and algorithms to predictive analytics and chatbots. You must first learn what’s possible before your organization can truly benefit from the technology.
Identify Your Organization’s Pain Points
Now that you know what AI can do, it’s time to see where it can help within your organization. Start by analyzing your operations to identify weaknesses. Are there areas where you have bottlenecks? Processes that are not set up to scale? Do you have issues with the quality of your code that makes development slow and buggy? Think in terms of business context and how the issue is impacting your organization’s business goals. You’re looking for those big, throbbing pain points that you can’t seem to find a solution for—every company has them.
Clearly Define Your AI Objectives
Once you’ve identified the nagging pain points where AI can help, you need to get very specific about how to solve them. Don’t try to boil the ocean; rather, start with a small, achievable goal. It’s important to make sure the technology works as expected and impacts the pain point you’ve identified before making a large-scale investment.
A little win for AI will help you build energy within your organization and get your team excited about the possibilities of the technology—which has nothing to do with replacing jobs. It’s important to define your objectives clearly, publicize the action plan and be fully transparent about the process so your team is engaged in the endeavor.
Deliver A Proof Of Concept
Do you have the expertise to implement the AI project that you’ve prioritized? If not, you’re not alone, as subject matter experts are still few and far between in this emerging field. Not every team is going to be equipped with the knowledge to leverage AI technology to successfully implement a proof of concept.
If you don’t have the expertise in-house, it’s well worth the investment to bring in an advisor, consultant, mentor or even a new hire to help with implementation. Winging it, on the other hand, could lead to a false failure that kills your momentum simply because you didn’t have the proper knowledge to leverage AI technology in the first place.
Create An AI Roadmap
A successful proof of concept will lay the foundation to grow and expand your investment in AI. The next step is to create and prioritize a list of potential AI use cases to solve those pain points you defined in step three. Prioritize this list based on what delivers the most bang for your buck, and use this list as your AI road map.
Be sure to have people participate in this process who are not afraid to push back, challenge and dig deeper than surface solutions. Don’t fall into the trap of simply going with the obvious solution, such as putting a chatbot in a call center. A deeper investigation here will help you tap into the true value of the technology.
The Feedback Loop: Evaluate, Scale, Repeat
Treat each proof of concept as a unique project until your organization gains confidence and experience with the technology. It’s crucial to document, communicate the results and evaluate every step of the way.
Based on the results and your evaluation, scale as needed. Some things won’t work as well as others, and that’s okay. The key is to start small and let your successes dictate how and where to implement AI on a larger scale.
Like any new technology, the adoption of artificial intelligence will be a journey that’s at times intimidating, frustrating and filled with unexpected challenges. Taking implementation step by step is the best way to navigate the process and set your organization up for success.