Artificial intelligence (AI) is rapidly maturing and becoming more than just a novel new technology. Software developers and vendors are incorporating AI functionality into many different types of applications. AI solutions range from advanced personal assistants to managed detection and response (MDR) tools designed to outsmart malicious threat actors.
AI has proven to be powerful, and in some cases, unpredictable. Recent reports of rogue AI models escaping controlled environments and attacking other models have highlighted some of the technology’s potential dangers. Misusing a formidable AI tool can have unintended repercussions across a business.
Organizational decision-makers may adopt AI solutions for a variety of reasons, such as leveraging new technology or keeping up with rivals. But implementing AI tools is not as simple as moving to a new backup solution or SaaS application. An AI tool can impact many aspects of the IT environment and the business. CIOs must consider several points before committing to an AI solution.
What Problem Are You Addressing With AI?
AI is not a standalone strategy to implement without a clear business reason. Companies can expect poor results if they adopt an AI model and then try to find a valid use for it. They may end up modifying business processes to use the tool but get no benefits. Unquestioningly embracing an AI tool can be an expensive mistake that makes it harder to use the technology more effectively in the future.
Before a CIO evaluates AI models or vendors, they should be able to answer the following questions.
- What specific business process are you looking to improve with greater speed or lower cost?
- How would a successful AI implementation be measured, for example, with reduced errors, employee hours saved, or increased revenue?
- Can this process be fixed with more traditional automation solutions rather than AI?
If these questions have no clear answers, it may be wise to table the AI discussion until it serves a real business purpose.
Data Quality is Key to Effective AI Implementation
All AI capabilities rely on the quality of data available to the model. An AI rollout can fail miserably because of unmanaged data. Companies need to inventory and audit data assets before allowing an AI model to access them. The following aspects of an organization’s data estate are essential to using an AI model effectively.
- Organizations must know the location of sensitive data such as financial records, intellectual property, and regulated health data. They also need to document the people and applications that currently access this information.
- Teams must remove duplicate records and fix stale permissions before AI accesses data resources. AI will exacerbate data inconsistencies. It will not correct these issues on its own.
- Businesses must be able to trace data to specific systems and datasets to protect themselves if a model leaks information.
Design Governance Before Implementing AI Solutions
Governing how AI is used throughout the organization is crucial to its effectiveness. Without robust governance, AI use can spiral out of control and introduce unnecessary business risks. At a minimum, companies should govern the following details of AI implementation.
- Define acceptable use policies that govern which data assets can be used with AI tools and who owns the platform’s output.
- Implement audit trails for all AI-assisted decisions to support regulatory audits. Companies should be very careful with decisions involving hiring, healthcare, and legal issues, as regulators are investigating these areas more closely as AI adoption spreads.
- The IT department should approve all AI tools used in the organization to minimize shadow AI risk.
Choose the Right Implementation Path
Organizations can typically choose from three AI implementation paths. The path they choose should reflect business requirements, budget, and in-house technical skill level. Many companies end up blending the three paths, but decision-makers must ensure proper governance to avoid redundant or unapproved applications.
- Buy: The fastest way for a company to implement AI is to purchase the technology in an existing SaaS tool. This path offers the least control over how the tool handles your data.
- Integrate: You can integrate AI technology into existing tools through the model provider’s API. Teams can build custom features and exert more control over data handling and retention.
- Build: Companies with the right technical skills can host and fine-tune a model. This is the most expensive option, but it provides enhanced data-handling control that may be needed to meet compliance requirements.
Set Realistic Budgets
Companies may underestimate the true cost of successful AI adoption because they are unfamiliar with pricing models. When choosing an AI model and vendor, examine the following costs.
- Usage-based pricing scales as the tool’s use increases across the organization. A popular solution can get expensive quickly.
- Integration and maintenance costs for tasks like prompt engineering and monitoring can exceed the original license cost.
Prepare Your Workforce for a Successful Implementation
A successful implementation is characterized by wide adoption and use of the selected AI tools. This requires a workforce that understands how the tool will be used and how it will affect their jobs.
- Offer employees role-specific training rather than general AI education.
- Provide feedback channels where employees can identify incorrect AI output to improve the system and build trust.
- Communicate clearly about job impact because honesty is essential to avoid adoption resistance, even if it is uncomfortable.
VAST Supports Effective AI Solutions for Your Business
At VAST, we have extensive experience helping companies optimize their cloud or on-premises IT environments. We see the opportunities for effective business use of AI-powered solutions such as the Huntress threat detection and response service. We can help integrate AI tools such as Microsoft Copilot with our deep Microsoft 365 knowledge.
Our managed infrastructure service gives you a team of skilled engineers to ensure you get the maximum benefits of AI tools while you focus on core business activities. VAST’s partnerships with major cloud vendors such as AWS, Azure, and Google give us the necessary perspective to recommend these platforms’ AI solutions.
Get in touch with our experts and learn more about how we can help you efficiently add AI to your IT stack.
