Creating a successful AI-based solution is different from regular software development because how it will operate in a real-world situation isn’t easily predicted. Development is an interactive process that requires extensive testing to ensure it will work well with a company’s existing workflow and deliver a worthwhile return on its investment. As such, effective implementation requires an iterative approach that usually takes more time, effort, and money than traditional software efforts.
Artificial intelligence is the key that can unlock a new generation of automation and productivity for businesses. That’s well-understood by now. But the development of AI can also help drive companies’ sustainability efforts — something increasingly important to today’s workers. The organizations that already know all this are the ones who will start to pull ahead from the pack. Companies that want to stay competitive need the benefits that an AI workflow brings to be able to move fast, adapt, and innovate.
However, AI implementation can sometimes seem easier said than done. Too often, money is spent on new AI software that integrates poorly with existing workflows and leads stakeholders to wonder why the business ever bothered with AI in the first place. These events are common enough to make many business leaders have second thoughts about kickstarting their own AI workflow initiative.
These failures aren’t due to any inherent flaws in AI’s potential. Rather, they’re most often due to a misunderstanding of how the development of AI differs from that of standard software. While some extra costs and risks come with AI development, the benefits always outweigh those risks with the right implementation.
Demystifying the Unique Challenges of AI Workflow Automation
Creating a successful AI-based solution is different from regular software development because how it will operate in a real-world situation isn’t easily predicted. Development is an interactive process that requires extensive testing to ensure it will work well with a company’s existing workflow and deliver a worthwhile return on its investment. As such, effective implementation requires an iterative approach that usually takes more time, effort, and money than traditional software efforts.
AI not only needs to be tested to ensure it’s properly interpreting existing data, but it also needs to be tested to ensure it will continue accurately processing data in the future. This can be done through manual testing, which is extremely time-consuming and difficult, or accomplished through a combination of statistical testing and adversarial probes.
There’s no question the development of AI is an intensive process — much more than that of regular software. This is why it’s so easy for people to run into trouble if they’re unaware of this. But done correctly, this process will reap significant rewards for most businesses.
Why AI is Worth the Work
AI’s most immediate benefits arguably stem from its ability to automate manual tasks. By taking over repetitive manual processes, employees can spend less time on busywork and more time on creative problem-solving. This increases a business’s ability to innovate while also keeping employees more engaged.
The automatically collected data to keep AI running effectively also benefits organizations. The wealth of real-time data now at their fingertips improves accountability and makes it easier to catch issues before they can become truly damaging. AI can also be put to use to help employees improve how they do their own jobs.
Chatbots, for example, are good for more than just taking over basic customer service requests and improving the customer experience. With the help of behavioral science, AI can use its experiences with customers to surface suggestions on way service reps can improve support.
How to Build a Solid Foundation for AI Initiatives
An AI workflow provides benefits for businesses on quite a few different fronts. But to make sure that it’s worth the cost, it’s crucial that the development of AI is done right. Here are a few places to start:
- Get your stakeholders on board.
While you might be ready to take the next step with AI for your company, others may not be so enthusiastic. For instance, many employees might worry that the more AI plays a role in the company, the fewer people will be needed. Investors, meanwhile, might be worried about the cost of development of AI and its return on investment.In every case, it’s important to listen to and address stakeholders’ concerns before starting development. Make sure employees know that the purpose of an AI workflow isn’t to replace them but rather to free them up to do more challenging, rewarding work. For investors, it’s important to lay out a clear roadmap for how and when an additional investment will pay off. There should be a clear answer for every concern; if there isn’t, it may indicate a problem with your strategy that needs to be addressed.
- Determine how you’ll measure success.
You may have a vision for what you want AI to accomplish, but if you don’t have concrete goals and key performance metrics to measure its success or failure, then that vision will most likely stay a pipe dream. Set up an evaluation metric for the success of your project right at the start and set goalposts along the way so you can track its progress and make corrections when needed. - Budget plenty of time.
While you might be able to build a promising proof of concept quickly, don’t confuse it with the real thing. To develop an AI solution that can operate in the real world, you need to be willing and able to spend a lot of time on every stage of its creation. Define what these stages will be at the start so you can budget the necessary amount of time.Not only do you need to create enough space for scoping, development, and deployment, but you also need to budget in the time for an iterative development process that starts with acquiring and cleaning data, moves on to model training and evaluation, and then goes back to the data stage again to repeat the loop at least once more.
- Don’t try to do everything at once.
It can be tempting to tackle your biggest problems when developing an AI solution immediately, but doing too much right off the bat will almost certainly give you more headaches than helping hands. Break your issues into minimum viable objectives and start with the one that looks easiest to achieve. This will allow you to deliver visible results faster and give you something to build on.
Once these strategies are implemented, you’ll truly be on a solid foundation for your AI initiatives. It’s important to remember, however, that initial development is far from the end of the story. The majority of work actually happens after a model is deployed. After all, it is a living, evolving thing that will need constant monitoring and care to continue to be successful. It’s a lot of effort, but that effort will translate into a competitive, sustainable business that will lead the pack instead of lag behind.
Written by Tiago Ramalho.
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