6 Steps for Bringing Your AI Project from Concept to Reality

There is often a disconnect between proof of concept testing of AI, which typically occurs in a controlled environment, and applications that occur in the real world. External forces, like variable conditions, integrations with existing workflows and actual time requirements commonly lead to a breakdown of these proof of concept solutions. In fact, one recent study by the International Institute for Analytics revealed that fewer than 10% of artificial intelligence pilot projects actually reach full-scale production. To avoid this same disappointment with your own AI projects, here are a few expert tips.

Have a plan for data collection.

In order for artificial intelligence and machine learning to deliver measurable value, it must have access to quality, relevant data. Without this, the project will inevitably fail. An important point to keep in mind, also, is that the more diverse and closer to actual real-world conditions your dataset is, the greater your chances of success. Dedicate an adequate amount of time and resources to this step, as it will give your project a solid foundation.

Anticipate risks and dependencies in advance.

In order to bring your AI project to life, you must thoroughly and as accurately as possible identify the various conditions the system will encounter. It’s unlikely you’ll be able to solve for every one of them, however, there will be at least some that you can prepare to overcome in advance. Make a list of all the risks and potential issues you can foresee and then rank those risks in order of priority, focusing on the most impactful first. The earlier you can remove a potential roadblock, the smoother your project will go.

Determine your milestones and metrics for success.

One of the biggest reasons AI projects fail is because they don’t effectively solve the target problems. This can stem from misunderstandings and miscommunications, and can result in a tremendous waste of time and money. To avoid this, make sure that there is a clear and accurate definition of exactly what the goals are. Set specific milestones and metrics that you will use to measure progress. Include relevant stakeholders in this step to ensure that everyone is on the same page and nothing is ambiguous before moving forward.

Don’t try to reinvent the wheel.

Avoid getting caught up in the trap of trying to solve problems that have already been solved. While the goal is certainly to adopt AI at an organization-wide level, that doesn’t mean every task, process or workflow is a good candidate for automation. Start instead with low-hanging fruit that can produce quick and measurable wins and focus on a solution that will allow you to use what’s already available to create a harmonious, interconnected infrastructure. Remove silos wherever possible.

Emphasize value over accuracy.

While maximum accuracy is always the goal, 100% is rarely achievable. It can be helpful to go into the project with the right focus: on delivering as much business value as possible, as opposed to attempting to achieve perfection. Understand that you will be able to tweak and make improvements along the way, so it’s ok to go from test environment to live environment, even if the solution isn’t one hundred percent perfect. If you’re not realistic in your goals and expectations, you’ll never get off the ground.

Don’t leave humans out of the loop.

Despite the incredible advancements in AI technology capabilities, some things are still better left to humans. Avoid being lulled into the idea that your workforce is suddenly expendable just because you’ve got some robots waiting in the wings. To the contrary, successful AI projects integrate a balance of human and digital workers. Besides, who better to identify areas of opportunity where intelligent automation could add value than the people who are working in the trenches day in and day out.

The last point we will leave you with is that developing and implementing an AI solution is a process. If you want to achieve long-term, sustainable success, you need to think of it more as a marathon than a sprint.

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