Artificial intelligence is no longer the stuff of sci-fi movies: it’s a present-day business reality. Far from glossy lens flares and space age computer noises, we’re seeing AI in use in normal cubicles every day. There is clear growing pressure to “use AI,” from boards, customers, and competition, but the more important question isn’t whether to adopt it: it’s how to implement it strategically, securely, and in a way that advances your business strategy.
How prevalent is business AI use? According to McKinsey’s latest State of AI report, 88% of organizations now use AI in at least one business function, and nearly half have embedded it in three or more. In financial services specifically, adoption is accelerating even faster: an industry survey from RGP found that over 85% of financial firms are actively applying AI in areas such as fraud detection, IT operations, and risk modeling, while the Financial Stability Board notes AI’s growing role in operational efficiency, compliance, and analytics across regulated institutions.
AI is a tool, not a strategy.
Many businesses are finding ways to streamline and improve operations, connect better with customers, and provide a better experience through AI, so it makes sense that stakeholders and customers might want to see more AI from your business. As powerful as it can be, AI is a tool that needs proper implementation to be impactful. It should serve the existing business goals and strategy. The best implementations improve or replace systems or processes that are already in place and already have a strong strategic backbone. Using AI for the sake of using AI is a recipe for perfectly automated chaos: fast, scalable, and completely misaligned with your actual business goals.
Establishing the Business Case for AI
Businesses use AI for a range of processes, such as deploying AI to detect fraud in transactions, streamlining compliance by tracking regulatory changes. Teams are using AI to streamline communication and project management, store institutional knowledge, and generate powerful reports quickly. AI is being used not to replace human judgment, but to improve security and fraud detection, bolster operations, and provide insights that guide strategic planning.
Before jumping down the AI rabbit hole, ask yourself a few important questions:
- What business outcomes are we trying to achieve or improve?
- Which workflows would benefit most from automation or additional insight?
- What is the goal? Communication enhancement? Stronger reporting? Process automation? Fraud reduction?
Identifying the area with the most potential positive impact is step one to building a responsible AI plan with your IT provider. When you know what you want to accomplish, building the model that will serve you best becomes a matter of steps to follow and not just a pile of spaghetti to throw around.
What are the risks?
As powerful a tool as it is, AI implementation is not without risks—quite the opposite. Risks of AI use range from technological to ideological. It’s important to consider the risks when you implement AI in your business and build systems to mitigate them. AI is a tool, and like any tool, it requires a thought-out strategy and proper management to be safe and effective.
AI Just Because
You guessed it, if we haven’t sent it home enough yet: implementing AI “just because” is a quick way to end up with an expensive tool that doesn’t help much but does have unregulated access to all your data and files and is potentially exploitable.
Data Privacy and Security Exposure
If your AI vendor wasn’t vetted properly or your set-up was rushed, sensitive information could be out in the open, waiting for bad actors to take advantage or for regulators to find noncompliance. AI systems rely on data: if you aren’t in control of how that data is stored and accessed, you could be opening yourself to unnecessary risk.
Validation Issues and Misuse
Generative AI can provide responses that sound really good and confident but are actually incorrect. Without strong validation measures in place, employees or customers may receive false information or data analysis may be based on incorrect numbers. In addition, employees who are not properly trained or following proper protocols may rely too heavily on the AI, which may yield incorrect answers or lead the employee to provide reports they do not understand and can’t explain or replicate. AI is meant to support human judgment, not replace it.
What does ideal implementation look like?
The best AI implementations are more about discipline than technology. As McKinsey notes in its State of AI research, organizations seeing measurable impact from AI tied its use to business outcomes rather than implementing it just because. Once you’ve defined that desired outcome, here’s what you should consider as you build your AI plan.
Vendor Management and Governance
AI should move through the same risk, compliance, and implementation review processes as any other technology in your stack. AI introduces operational and systemic risks that require governance, auditability, and data oversight.
Sometimes, a tool you’re already using will have built-in AI features; this can be the smoothest way to ensure your vendor is trusted and passes muster. For example, for users in the Microsoft ecosystem, Copilot is already part of Microsoft and will have the same security as any other Microsoft product, making it a safer option that already has access to your data and files.
Information Access Levels and Permissions
If you wouldn’t share an executive folder with an entry-level employee, that entry-level employee’s AI tool should not be able to access it either. When you set up your AI model, ensure that data permissions are in place so you can block unauthorized access to sensitive data.
Employee Training
An untrained employee using AI is almost as bad as having no AI strategy to begin with. There should be an onboarding process and clear guidelines for employees using AI for their work so you can ensure they understand the purpose of the tool, use it properly, understand how to verify/correct the information as needed, and don’t over-rely on it.
Measurement and Accountability
AI implementation isn’t a one-time deployment. It’s important to define success measures and check in on those measures as your tool is adopted. For example, if your business case was increased productivity, are you able to see indicators of increased efficiency? More tickets closed, more customers supported, fewer meetings, increased revenue? Conversely, you can track areas of risk, such as employee misuse or data exposure, and continue refining processes and security.
AI is not innovation: it’s a tool that can help you innovate.
AI isn’t a magic wand that can fix everything and just having it in-house doesn’t make your business stronger. It’s a tool that, properly utilized, can power decision-making, improve operational efficiency, and strengthen risk management. Without clear objectives, strong governance, and continuous measurement, it can just as easily add more bloat, error, and inefficiency to your processes.
AI implementation is not about who gets there fastest or can say they have it, it’s about building the perfect tool with intention that will align with and support your business.
Build your AI tool the right way with FIT
Every AI build looks a little different. At FIT, we work with you to figure out where AI can actually make an impact, and where it might introduce unnecessary risk. We can help you build a plan that makes sense for your business and roll it out. Whether you’re just starting to explore AI, your board is knocking down your door, or you’ve got some strong ideas and you’re ready to dive in, we’re here to help you move forward with clarity, security, and confidence.