4 Critical Questions to Ask Before Starting an AI Project
If it feels like everyone is implementing artificial intelligence, it’s largely because they are. AI projects are poised to double this year with 40% of companies deploying AI Underneath all the optimism and hype around artificial intelligence lies a harsh truth. A study 1. Where can AI provide a quick win?
People hear every day how artificial intelligence is revolutionizing business. While that’s true, starting a revolution shouldn’t be the goal of your first AI project. Instead, target a small project that can deliver a quick win. Success breeds confidence and can set you on a path for continued success.
With that first project, you are looking to cut your teeth 2. What does your data look like?
AI and machine learning hinge on data — lots of it. We need to analyze our data store to see what limitations might hinder our project. Is our data skinny? Is it dirty? If it takes years to adequately compile enough data, the project isn’t viable. If our data is a mess, we have to determine what effort is involved Regardless, perfect data doesn’t exist, and we can’t let that hold us back. Don’t settle on a low-impact project because another dataset is more complete. The discovery stage is the perfect time to jump in and explore what you have. Take some time to model the data to determine if you can tell the story with less.
3. Are you creating value?
When deciding on a project, adding value should always be your focus. This could be cutting costs, augmenting revenue streams or simply streamlining business processes. Where do you have processes that are inefficient? Where can you make better decisions? The value proposition should always be supported When we look at potential AI projects, we want to pinpoint tasks and not massive overhauls. It’s ideal to select processes that are repetitive, have clearly defined rules, are prone to human error and come with the data to support them. We need to construct logic around these processes so there’s little room for gray areas.
4. Do you know what your definition of success is?
Difficulties delivering a successful project isn’t unique to AI. This problem vexes countless project teams for a lot of the same reasons. It usually boils down some combination of unrealistic timelines, going over budget, scope creep and not having the right expertise to properly execute. Planning is key.
Disassemble the silos. AI engineers and data scientists need to work hand in hand with business analysts and end users to understand the problem and discover what a successful outcome looks like. Find a team lead who can not only bring cross-disciplinary teams together but can also talk about the AI solution in plain language so pivotal stakeholders will have a clear understanding of what impact AI will have, and where it won’t.
Also, don’t assume you can hire your way to success. Lean on trusted partners to provide the necessary AI expertise your team will need as they ramp up and face those technical hurdles that are sure to come during those first projects.
Artificial intelligence is a game changer. According to McKinsey, AI will create $13 trillion in GDP growth
Mark Runyon works as a principal consultant for Improving in Atlanta, Georgia. He specializes in the architecture and development of enterprise applications, leveraging cloud technologies. Mark is a frequent speaker and contributing writer for the Enterprisers Project.