Feature
Artificial intelligence (AI) is delivering on its promise to help IT teams accomplish more with less. Whether those tech teams are trying to get traction for a new software tool, defend vast networks and systems against malicious attacks or aid departments in leveraging the technology to help customers more efficiently, IT teams are finding a willing ally in AI.
Here are five case studies demonstrating how AI has gone above and beyond to help tech teams succeed:
1. California State Polytechnic University, Pomona, California
Protecting the massive network at California State Polytechnic University, Pomona, which is large both in terms of the physical space it occupies and the number of users who connect to it, was proving to be a daunting task for the university’s IT team, according to a case study.
“We were getting so many device alerts that it could soon become overwhelming,” says John McGuthry, VP and CIO for the school. “The amount of information we were looking at kept increasing. We needed a better way.”
The campus is 1,400 acres, occupies over 100 buildings and can have as many as 100,000 people logging into its networks at once. With health care, law enforcement and other types of organizations on the campus, compliance was complex too. The school worked with IBM and its AI-enhanced QRadar SIEM system for a solution.
The IBM AI, once trained, analyzed and categorized the alerts — identifying those that might be a threat — so it was clear what needed human attention. It also sorted the network data into logs to simplify the university’s compliance reporting and auditing.
IBM’s intelligent systems identified when something looks like a targeted attack or other nefarious activity, surfacing anything that is a threat or weakness, along with the relevant details, to make investigations faster and easier. The IT team was able to protect the networks without becoming overwhelmed.
Results
- Monitors 84,000 devices on campus
- Tags 20-40 events a day that require investigation
- Generates 44 GB of logs and reports a day to simplify compliance and auditing
2. OCBC Singapore
Keeping up with communications – especially questions from customers – is a time-consuming job at the OCBC contact center in Singapore. Singapore is a financial hub for all of Asia, and OCBC is one of the island’s biggest banks. The bank’s IT team thought AI may be able to help — so it developed a chatbot that tapped into ChatGPT to speed up the process, according to a case study.
“We took an exploratory approach to understand the capabilities and risks of ChatGPT — wanting to see if we could harness its benefits in a safe and secure environment,” says Bryan Lee, managing director of group technology architecture, OCBC.
They built a chatbot in Microsoft Azure using that tool’s native access to ChatGPT and installed it into Microsoft Teams, because employees were already using that for communication.
The team launched the chatbot to an internal engineering team. When that went well, they took it to a pilot program of about 200 employees. They trained employees on how to use it and organized workshops, so people could tell others how it was helping.
Employees liked the productivity benefits and time savings. The system became a popular way to process customer communications easily, efficiently and accurately. Quickly, word about it spread through the company, and people began using it to generate code and create newsletters and job descriptions.
Results
- Used by nearly 1,000 employees to analyze and answer customer questions
- 72% of employees reported productivity improvements using the AI chatbot
- Boosts the productivity of software engineers by 20%-30%
See more: The Dangers of AI Technology: Shadow AI and Data
3. Stripe
The developers at Stripe used ChatGPT to answer support questions from users, customize customer support and automate fraud detection. They found that, in most cases, the AI is more helpful than they imagined it would be, according to a case study.
“Our mission was to identify products and workflows across Stripe that could be accelerated by large language models and to really understand where LLMs work well today and where they still struggle,” says Eugene Mann, product lead for Stripe’s applied machine learning team.
“But just having access to GPT-4 enabled us to realize, ‘Oh, there are all these problems that could be solved with GPT surprisingly well.’”
By feeding the AI the company’s extensive technical documentation, for example, the support team discovered it could ask ChatGPT to troubleshoot issues. The AI understood the questions and hunted down answers in the documentation, which saved tech support teams an enormous amount of time. It delivered clear, concise summaries of the issues and answers.
The AI was also able to analyze customer websites for the support team. This allowed the team to customize the type of support they offer each business. In fact, it often did a better job at this than humans.
Stripe also uses social networks to crowdsource technical questions. But sometimes bad actors try to trick community members into giving up important information in those networks. ChatGPT was able to analyze the syntax of all the posts and flag those it thinks were suspicious for Stripe’s fraud team to investigate.
Results
- Identified 15 prototype use cases for ChatGPT to integrate into the Stripe platform
- ChatGPT was better at analyzing customer websites than humans
- ChatGPT was able to digest and understand extensive technical documentation – and become a virtual assistant – almost instantly
4. Wehkamp
Wehkamp is a large, big-box retailer in the Netherlands with over 2.7 million customers. Its shopping site gets more than 500,000 visits a day. All of those customers are looking for product descriptions for the goods the company sells. It used Wordsmith AI to automate the product listings for that site, according to a case study.
With over 400,000 items across 2,000 brands, in fashion, living, beauty and more, the company needed a vast number of clear, concise product descriptions – many more than its team of writers could generate. It also needed to tune those listings to the company’s strategic SEO focus. It was impossible to keep up with the sheer volume of copy that was needed.
“Automating the product descriptions … meant that we could scale our efforts, while saving time,” says Caroline Jelinda, product manager, data science, Wehkamp. Without the AI tool, she said, “there would be no way for us to have done this as quickly and efficiently as we did.”
Wordsmith was able to turn the data sets provided by product manufacturers into product descriptions in Dutch. Not only was it able to generate accurate copy, but the product descriptions were also optimized for search engines. The company’s copywriters and editors, therefore, were able to shift their focus from creating individual product descriptions to creating the materials to put those products in front of the right buyers.
Results
- Generated thousands of SEO-optimized product descriptions more quickly than humanly possible
- Generated over 300,000 narratives
- Increased website traffic and SEO rankings
5. Hill City Technologies
Going out on your own as an IT services entrepreneur involves building muscle in all sorts of skills outside of IT. One of those skills is tracking down CTOs who may be willing to outsource IT services, introducing yourself to them and letting them know what services you’re selling to build a client base. Thad Butterworth, the owner of Hill City Technologies, discovered that CoPilot AI could help him do better targeted networking in less time, according to a case study.
He didn’t start out using an AI to manage his social networking efforts. He started by trying to reach CTOs himself by contacting them on LinkedIn. Being a one-person operation, short on time, he went straight to messaging them about his service. That approach was shut down by CTOs.
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Butterworth needed a casual approach to networking. He had to build a relationship before pitching his wares but that kind of networking is time-consuming.
He developed a more laid-back approach and tried it out himself, which worked better than his previous approach. But he needed a way to do more of it faster and reach more CTOs with it. He turned to AI for help scaling this method.
“CoPilot AI created a way for me to meet people and get access to their network and get to more potential customers in a way that doesn’t irritate them or push them away,” Butterworth says.
It worked. He built a bigger network and closed a large client that helped put his new firm in business.
“My success really comes from the expansion that CoPilot AI has given me through my LinkedIn network,” he says.
Results
- Quickly built a personalized messaging strategy that could scale
- Quadrupled a targeted LinkedIn network within weeks
- Closed a large client through an AI-scaled network messaging
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