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Operationalizing AI: How to Beat 2 Major Challenges

Elizabeth Bell, Appian
July 25, 2023

If you’re not using artificial intelligence (AI) in your organization right now, you’re behind. But the reality is that beyond inputting some ideas into a large language model like ChatGPT, AI just isn’t that simple to operationalize across your business (although the benefits are real). You have to do your due diligence and make decisions, like whether you can invest the resources to develop your AI capabilities in house or work with a large vendor—and if you can take the risk of sharing your data with them

Read on to understand the methods organizations have traditionally used to operationalize AI, and the costs and benefits of each, along with a hybrid option that can help you realize your goals of efficiency and transformation. 

[ See Gartner's predictions on adoption times for AI and other hyperautomation technologies. ]

2 common approaches to operationalizing AI—and their challenges.

1. Large public cloud providers.

Large public cloud providers for AI offer a wide range of services that appeal to many organizations because of benefits like computing power, storage, and AI services. But there’s also a major issue: data privacy. It’s not uncommon for these providers to use your data to train their own AI models. Lack of clear data privacy practices around these public AI services should make you uneasy, especially when you need to comply with data privacy regulations or want to retain a competitive edge in your market. 

Hear more about the issue of AI and data privacy from Appian CEO Matt Calkins.

2. In-house AI.

Developing your AI models in house solves the privacy problem, but it’s extremely resource intensive. You’ll need a team of experts—including data scientists, data engineers, software engineers, and an IT team—along with security experts to ensure your data is protected. After all the steps that go into building an AI model (preparing data, extracting features, selecting the model, training the model, etc), you’ll still need to incorporate it into your IT ecosystem, a project that gets more complex as the ecosystem does.

While it might appear that these are the only two options for operationalizing AI, they’re not. 

A better way: Platforms with native AI capabilities.

A platform with AI capabilities built in, such as a process automation platform, can help you operationalize AI without having to share your data with a public cloud provider or build your own data team. Because it’s private AI, your models are tailored to you, trained only on your data—and that data isn’t shared to train a larger algorithm. A platform like this can help you use AI across all your processes. 

Take Appian’s AI Skill Designer, for example. This capability enables you to process content at scale. With a minimal amount of prep work, you can train and deploy AI models in minutes. For example, if you want to use AI to classify incoming customer support emails, you can upload a set of sample emails, let the system train the model, and then test it. If it meets your desired confidence threshold, you can use our workflow orchestration tool to add the skill to an existing process. That’s another benefit of using an AI-powered process automation platform: You can incorporate AI into a larger process where it can work alongside tools like robotic process automation (RPA), business rules, and process modeling. 

To learn more about the Appian AI Skill Designer, watch the demo from Appian World 2023, and see what we have planned on our AI roadmap for generative AI and low-code at our AI Vision web page

Operationalize AI within a broader automation strategy.

As the AI arms race continues, you have to take care how you integrate and operationalize AI. Sure, AI will be transformative, and it’s splashy to talk about, but remember that the real business impact of operationalizing AI is more efficient processes. Reaching that goal requires technology like AI, but not just AI—you also need RPA, business rules, process modeling, and other automation technologies. Rather than risking your or your customers' data with a large public cloud provider, or undertaking a resource-draining project that will take time to deliver practical results, take a holistic look at your processes to see how you can bring multiple technologies, like AI and automation, to work together in harmony with humans. 

[ Learn how to use AI securely: Private AI: A Practical Implementation Guide for Secure AI. ]