From life-changing implementations like medical diagnostics imaging and self-driving vehicles to humble use cases such as virtual assistants or robot vacuums — artificial intelligence is being put to use to solve an incredible range of problems.
Despite widespread AI implementation efforts, however, the development of effective AI tools is still far from easy. Teams can expect to encounter quite a few obstacles along the way.
Data is one of the most important elements in developing an AI algorithm. Remember that just because data is being generated faster than ever before doesn’t mean the right data is easy to come by.
Low-quality, biased, or incorrectly annotated data can (at best) add another step. These extra steps will slow you down because the data science and development teams must work through these on the way to a functional application.
At worst, faulty data can sabotage a solution to the point where it’s no longer salvageable. Don’t believe it? That’s exactly how Amazon spent years building a sexist hiring tool that the company would eventually scrap.
Just Getting Started
Once you have high-quality data, your work is far from over. Instead, you’ll need to convert it into a machine-readable format — a process that comes with numerous challenges.
In highly regulated industries like finance and healthcare, for instance, data will need to be carefully de-identified to ensure it meets privacy standards.
If you’re sourcing international data, you’ll also need to adhere to data-sharing laws that govern the countries where the data originates. The process sounds like dotting the i’s and crossing the t’s — but adherence to data will require in-depth knowledge of a complex regulatory landscape.
Crunching the Numbers
Of course, data is nothing without a team to turn it into insights that can inform an AI model.
If your organization lacks a trained data science team in-house, you might have to hire or outsource these capabilities.
Even if you do have a team of experienced engineers on your roster, the sheer time required to annotate raw data can get in the way of actual algorithm development.
Employees aren’t likely to take a pay cut just because you have them performing lower-value work.
These obstacles certainly add complexity to the development process, but they shouldn’t be deal-breakers. Instead, a well-constructed plan can help you avoid some of these hurdles while you clear others one at a time as they appear.
3 Steps to Overcome Common AI Application Development Obstacles
REMEMBER: Maximize Efficiency and Outcomes
The AI development process is iterative, with each iteration is aimed at improving the accuracy and scope of the model. As you begin to plan how your own development journey will unfold, focus on the following three steps.
1. Find the right partner for primary tasks
Data sourcing, annotation, and de-identification can consume more than 80% of a data scientist’s time.
Leveraging the expertise of the right partner can save a huge amount of your AI team’s time and energy. You want to allow your team to utilize the skills you pay them for instead of performing mundane data-cleaning functions.
Besides ensuring your team is free to put their best skills to good use, an experienced partner can help you track down the highest-quality content for training your AI data model.
Gartner Research predicts that 85% of AI implementations through 2022 will produce errors in output due to bias in input. With the right partner helping you source and annotate data, you can avoid a costly scenario where “garbage in yields garbage out.”
2. Align stakeholders with clear use cases and customer needs
Building an AI solution is a considerable investment that will require lots of participants with varying roles.
Having a diverse range of experiences and perspectives is critical to a successful AI implementation, but only if these stakeholders are aligned on the project’s goal.
Existing gaps between different perceptions of the ideal outcome only widen as the development process progresses, so it’s important to take the time to nip these misunderstandings in the bud early.
Spend time with all stakeholders and teams to establish clearly defined goals and criteria for success. This small upfront investment will cost you time and money, but it will save you both in the long run by keeping participants aligned for the project’s duration.
3. Get it right, one implementation at a time
AI is extremely powerful, but it’s not a silver bullet; there are still many business problems for which AI isn’t a suitable solution. Instead of throwing artificial intelligence at the wall and seeing what sticks, organizations should start by prioritizing the use cases that make the most sense.
Are you looking to filter through a vast amount of data? AI is an excellent option. If you’re trying to spot patterns, it’s equally capable, and software can scale to outperform millions of human analysts with ease.
Start with simple or proven AI implementations that offer the easiest and quickest path to a payoff, and take the experience gained through these ventures to more complicated future projects.
Conclusion
Creating an AI application isn’t easy, but the potential rewards are massive. Keep a clear understanding of the potential pitfalls your team could encounter throughout the process.
Your potential pitfalls include data sourcing and annotation issues, personnel shortages, skills gaps, and a lack of alignment toward a common goal.
Construct a plan that takes these obstacles into account. Start with the above three steps, and you’ll be well on your way to an effective AI implementation.
Image credit: scott graham; unsplash, thank you!
Vatsal Ghiya
CEO and co-founder of Shaip
Vatsal Ghiya is CEO and co-founder of Shaip, which enables the on-demand scaling of platforms, processes, and people for companies with demanding ML and AI initiatives.
