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AI Governance Strategies: 7 Bite-Sized Case Studies
George Anderson
AI offers incredible benefits when it’s leveraged properly. Yet it also comes with certain risks if you don’t define and implement AI governance policies.
There’s much to learn from companies that are doing this well.
Here are 7 bite-sized case studies of successful AI governance strategies.
1. Professional Services Firm
Use case: Meeting summaries and automated scheduling
A well-known professional services firm has found a sweet spot in applying AI to their operations:
Challenge
Employees were overwhelmed by processing information from numerous meetings. It was essential for the team to capture and understand detail, but the sheer volume of information was too much to handle. As a result, customer experience was beginning to suffer.
Employees also struggled with managing their calendars. It was tough to fit in client meetings alongside internal calls. Personal assistants would have been helpful, but the organization didn’t have the budget to hire that many assistants.
Solution
This professional services company implemented Microsoft Copilot, integrating it with Microsoft Teams. Employees gained access to automatic transcription of meetings through Copilot. The tool also reads entire transcripts and produces clean, bulleted summaries of key points.
In addition, Copilot automatically manages employees’ schedules, prioritizing client meetings and essential internal calls. Users can modify their schedules on their own, but Copilot takes up the bulk of the administrative load.
👉 Governance strategy
Users needed to have the right permissions configured in Microsoft 365. If their permissions were too broad, users may have gained access to sensitive information through Microsoft Copilot chats. A decision was made to address permissions and data governance before implementing Copilot. This ensured that users could only see appropriate information in Copilot responses.
2. Healthcare Provider
Use case: AI-powered drug cost management
A regional healthcare provider is creating better patient outcomes through AI. Specifically, they’re using this advanced technology to manage the cost of drugs.
Challenge
Healthcare providers face significant challenges with medication cost volatility. This organization was no exception. They frequently received anomalous drug pricing in error, but it was difficult to detect this phenomenon or address it at scale.
The provider also struggled with legitimate but unpredictable increases in drug costs. These increases were not given in error, but they did make it challenging to manage specific budgets.
Solution
The healthcare provider implemented an industry-specific AI tool to address drug cost problems. The tool can:
Detect erroneous anomalies in drug costs
Predict legitimate increases in drug costs
Forecast the outcomes of drug price negotiations as authorized by the Inflation Reduction Act
👉 Governance strategy
It was important to implement this AI tool in accordance with HIPAA regulations, particularly as the tool might take PII (personally identifiable information) into account in determining correct drug prices for patients. This required a nuanced approach to AI data governance, informed by HIPAA best practices and deep experience in the healthcare industry.
3. Manufacturing
Use case: CAD design and predictive maintenance
A major manufacturer is making great strides in product engineering and predictive maintenance. And they’re doing it with AI solutions adapted for their industry.
Challenge
Manufacturing companies have limited room for waste. This organization was struggling to come up with innovative solutions using CAD design. It was hard to find human experts to carry design exercises to completion, and technical challenges made it difficult to create designs that actually worked in their intended applications.
The company also struggled to deliver timely maintenance on equipment under service contracts. They needed a way to get ahead of issues before they arose.
Solution
This manufacturer integrated AI into their operations in two significant ways:
AI-enhanced CAD design: Designers input various parameters while AI generates multiple design options that meet the requirements.
AI-powered predictive maintenance: Technicians detect emerging mechanical issues before they cause manufacturing shutdowns.
These applications help tighten margins, improve quality, and increase uptime.
👉 Governance strategy
Well-structured data was essential to this AI implementation. Without that clean data, the AI tools wouldn’t be able to generate CAD designs or detect maintenance issues. Consequently, this manufacturer implemented new data governance policies that dictated how data was created, transferred, and stored within the company’s internal systems.
4. Distribution
Use case: Inventory and supply chain optimization
A national distributor of electrical equipment is using AI to tighten margins through better handling of inventory. It’s a great use case that highlights what AI can do for supply chains.
Challenge
Distributors often struggle with shifting demand, unpredictable supply chains, and complex logistics. This company needed better predictive insight into future demand shifts. They also needed to cut costs by adhering to a JIT (just-in-time) inventory process—and they needed to allocate warehouse space proactively as orders came in.
Predicting demand shifts based on historical data and current trends
Managing inventory using a just-in-time (JIT) approach
Allocating warehouse space based on order volume
👉 Governance strategy
This distributor needed clean data for their AI solution. That meant organizing and structuring historical data related to order volume so the tool could predict seasonal demand shifts. It also required a new policy that would only accept properly-formed orders—so warehouse allocations could be made based on solid data.
5. Software Development
Use case: Enhanced coding efficiency
AI can’t replace humans in the coding process, but it can help developers speed up their processes. It can also handle routine coding tasks—so developers can focus on more complex challenges. This software development company needed to produce faster outcomes without sacrificing quality.
Challenge
Developers needed plausible coding suggestions to help them complete tasks faster. They also needed to automate the first pass in generating code so they could free up human experts to review and refine more code per workday.
Solution
While developers must be cautious when using AI for coding, this software company has implemented an AI strategy that improves productivity while avoiding common pitfalls:
AI provides realistic inline suggestions in Visual Studio based on actual data such as TSQL queries against real databases
AI helps developers start projects by writing initial code based on specific requirements, which developers then refine
👉 Governance strategy
This software development company knew that it was important to provide data security for both the inputs and the outputs of their AI tool. Proprietary code needed to be protected, and they had to ensure that the AI tool had clean data to work with. Secure APIs, proper user permissions, and well-structured data were essential to establishing data governance for AI.
6. Higher Education
Use case: Improving learning outcomes
This university is finding groundbreaking ways to improve student learning through the careful application of AI. This is especially important as this generation of students relies on AI chatbots in a way that’s arguably detrimental to their education.
Challenge
This university encountered many students cheating on exams and engaging in plagiarism. The administration needed better ways to proctor exams and detect plagiarism at scale.
The university also needed to optimize their budgets based on shifting needs. They lacked the analytical tools to do this effectively across numerous departments.
Solution
Universities are finding numerous applications for AI. This institution’s AI strategy includes:
AI-powered exam proctoring to prevent cheating and encourage proper preparation
AI-powered plagiarism detection that compares student work against vast academic text repositories
AI-powered financial analysis to optimize resource allocation
👉 Governance strategy
Clearly, it was essential to protect student privacy with the implementation of these AI tools. The university’s AI governance policies included strong cybersecurity protection and role-based access to safeguard student information. The financial analysis AI solution also required properly-structured data, so the institution had to implement more stringent data governance policies for internal financial data.
7. City Government
Use case: Traffic flow optimization
This local government knew they needed to adjust their traffic light timing. They are now using AI to create better traffic flow for drivers in their municipality.
Challenge
Static timing for traffic signals has become obsolete. Today, drivers on major roads expect lights to stay green if there are no cars waiting on the other street. This local government needed to optimize traffic patterns for efficiency with dynamic, responsive controls.
Solution
This city government now uses AI to analyze traffic patterns and dynamically optimize signals for more efficient traffic flow. They’ve reduced traffic congestion in their streets by 21%–a difference that every driver can feel.
👉 Governance strategy
City administrators knew they needed clean data to work with. If the AI solution was working with bad data, that could create worse traffic congestion than before. It was essential to upgrade traffic cameras, radar sensors, and sensors in pavement to provide accurate data—and then ensure that this data was transferred and structured properly. City administrators also knew they needed processes and technologies to retain human oversight and practice regular review of AI decisions.
The Takeaway
Shape your AI governance strategy to fit your use case
No two organizations are the same when it comes to AI governance. You need to understand your internal requirements fully—and you need to know what’s possible with specialized AI tools. Here at Corsica Technologies, we’ve helped 1,000+ clients take big strides with technology. Contact us today, and let’s examine how to implement AI governance for your organization.
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