If your organization is anything like ours, you manage data on a daily basis—and a lot of it. Have you ever wondered if there’s an easier way to extract knowledge and actionable insight from that data?
What if you could …
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Detect anomalies before they occur, like equipment maintenance needs or incidents and deviations
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Forecast future demand for your products or services
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Improve process efficiency while reducing waste
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Synthesize large amounts of data quickly and effectively
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Rely on a personal assistant to help answer your questions
This is where artificial intelligence, or AI, comes in. Here, we look at four crucial ways your organization can prepare for the future and make the most of this super-charged technology.
1. Generate high-quality data
AI models are data hungry. Whether used for fine-tuning or extracting insight, the more disparate sources of high-quality data that are available to feed the models, the better the outcome. The adage “garbage in, garbage out” certainly rings true for AI.
In the modern age, it’s not hard to generate data—the challenge is generating and digitizing the right data. If you feed AI models data that is not important or valuable to your organization, then you can expect the model outputs will be equally unimportant. And even with the right data, if it’s not readily accessible for AI to digest, it will minimize the value AI can provide. The intersection of both these aspects is critical. The former requires upstream consideration of what data is important to the goals and requirements of the organization. The latter requires the digitation of high-quality data collection (e.g., sensors and instrumentation rather than manual data collection).
2. Build and manage systems to support and integrate your data
While data governance isn’t as flashy of a topic as AI, it is critically important for organizations to have strong data governance systems, strategies, and policies in place. Within an organization’s data governance strategy are the systems that collect and store data throughout its lifecycle. To reap the full value of the data we collect, our various data systems should fit together like puzzle pieces rather than standing alone in silos.
Modern solutions like data warehouses, data lakes, and data hubs are designed to cohesively integrate different data sources together. These solutions have their own strengths and limitations—do you understand the differences so you can deploy the right solutions within your organization?
3. Foster a culture receptive to digital transformation
“As many as 70% of all digital transformation efforts fail, often due to lack of clarity, discipline, and leadership.” –Tony Saldanha
As many as 70% of all digital transformation efforts fail, often due to lack of clarity, discipline, and leadership, according to Tony Saldanha in his book Why Digital Transformations Fail. Saldanha further iterates that digital transformation is not about implementing the next new shiny technology but instead about how a business can operate and deliver value to its customers in the digital age.
Adopting AI into your organization will require ongoing cycles of digital transformation and a culture that is receptive to change. In the chaos and potential organizational fatigue of digital transformation, clarity can help to reduce external noise. Determining your organization’s business justification for AI usage and developing a roadmap to evaluate emerging technologies for their applicability can be especially useful.
4. Understand and mitigate the risks
Due to the complexity of their neural networks, large AI models are often described as “black boxes.” Widely available foundation models may be fine-tuned with additional training data for improved performance of specific applications, or an intermediate index or vector database might be used to ground the model with enterprise data. In either case, the black box nature of these models can introduce unknowns on how the model could bias output. These issues can carry potential ethical and compliance risks for your organization.
AI models also raise substantial security risks, especially when hosted outside of an organization’s secure environment. Prompts and other data fed into externally hosted AI platforms will often be incorporated into future training data for these models. An important part of effectively using AI is addressing and mitigating the associated ethical, compliance, and security risks. To guide your organization’s AI strategy, consider engaging a diverse group of representatives from management, legal, information technology, and end users.
Ready to up your technology game?
It’s hard to imagine all the exciting possibilities AI may introduce to your organization and industry. By following these four crucial steps, your organization can realize the benefits of these advancements, while remaining agile and competitive in this digital age. AI has the potential to be the most disruptive and impactful technology of our generation—is your organization ready?
Where we come in
At Barr, we’re committed to the ethical use of AI. Our diverse AI task force reviews industry trends and use cases; understands the legal, ethical, and regulatory landscape; and evaluates the technology, infrastructure, and data governance considerations related to AI.
Need help generating the right data for your organization? Barr will work with you to build and optimize end-to-end data collection and controls processes, from sensor and instrumentation selection to programmable logic controller (PLC) system design, installation, programming, and commissioning.
Barr also offers a variety of digital solution services, including current state digital solutions evaluations; digital architecture improvement and implementation; digital twin implementation; advanced analytics and business intelligence services (including machine learning algorithms and artificial intelligence); digital transformation strategy consulting; and environmental management information systems (EMIS) selection, implementation, and integration.
Contact us for help streamlining your organization’s data management processes.
About the author
As a senior data scientist, Kyong Song’s expertise lies at the intersection of engineering and data. He has over 10 years of experience in engineering, analytics, and digital transformation, collaborating with organizations across various sectors to achieve their environmental, analytical, and technology goals. He focuses on projects related to digital transformation strategy, data science, digital twins, and systems implementation.
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