Businesses are under enormous pressure to adopt AI.
Employees are already experimenting with it. Software vendors are adding AI features to nearly every platform. Competitors are talking about productivity gains, automation and new ways to operate more efficiently.
That creates a tempting question:
Which AI tools should we be using?
For most businesses, that’s the wrong place to start.
Before selecting an AI platform, you need to identify the business problem you’re trying to solve, determine whether AI is actually appropriate for it and understand what data and security risks the implementation could introduce.
Otherwise, AI can quickly become another software investment that costs money without delivering measurable value—or creates risks nobody considered before employees started using it.
Start With the Problem, Not the AI Tool
The best AI opportunities usually aren’t the most dramatic ones.
They’re everyday processes that consume too much employee time, create bottlenecks or involve repetitive work that doesn’t require much human judgment.
Look for tasks such as:
- Summarizing meetings and identifying action items
- Drafting routine communications
- Searching large amounts of internal information
- Summarizing documents or reports
- Categorizing or extracting information from documents
- Handling repetitive customer inquiries
- Automating portions of routine administrative workflows
These are specific problems with outcomes you can measure.
If employees collectively spend 15 hours every week creating a particular report, for example, you have a baseline. If an AI-assisted workflow reduces that to five hours while maintaining accuracy, you can quantify the value.
That’s much more useful than purchasing an AI platform and then trying to figure out what to do with it.
Identify the Friction in Your Business
Before evaluating AI tools, talk to the people doing the work.
Ask:
What do you do repeatedly?
Look for tasks employees perform daily or weekly using essentially the same process.
Where do you spend time searching for information?
Information scattered across email, documents, shared drives and business applications can create significant inefficiency.
Which processes involve unnecessary manual steps?
Copying information between systems, manually creating reports or repeatedly entering the same information may present automation opportunities.
Where are the bottlenecks?
A process that consistently waits for one person or one manual step may be a better AI opportunity than a task that’s merely inconvenient.
Then prioritize the opportunities. A good first AI project should solve a clearly defined problem, have a measurable outcome and carry a manageable level of risk.
Before You Deploy AI, Ask What Data It Will Touch
This is where AI adoption becomes an IT and cybersecurity issue.
Employees may use AI tools with customer information, financial data, intellectual property, contracts, internal communications or other sensitive business information.
That means businesses need to understand where information entered into an AI system goes, how it’s processed, whether it’s retained and what administrative controls are available.
NIST’s Generative AI Profile specifically addresses risks organizations should consider when developing, deploying and using generative AI systems as part of its broader AI Risk Management Framework.
Before approving an AI tool, businesses should consider questions such as:
- What information will employees be permitted to enter?
- What information should never be entered?
- Does the provider use submitted data to train its models?
- How long is data retained?
- What security and administrative controls are available?
- Can access be managed through existing identity systems?
- Can administrators monitor how the platform is being used?
- Are there compliance requirements affecting the data involved?
- What happens to company data when an employee leaves?
These questions are particularly important when employees begin adopting AI independently—a practice sometimes referred to as shadow AI.
A free AI account used by an employee may not provide the same security, privacy, administrative or data-governance controls as an enterprise deployment.
Don’t Ignore Your Existing Technology
Another common mistake is buying a standalone AI tool before understanding what AI capabilities already exist within your technology stack.
Many organizations already use platforms that are adding AI functionality across email, productivity applications, CRM systems, cybersecurity tools and other business software.
Before introducing another vendor, determine whether your existing platforms can solve the problem.
There are several advantages to doing this.
Your organization may already have established identity management, permissions, security controls and data governance within those platforms. Employees may also require less training because the AI capabilities are integrated into software they already use.
The best solution isn’t necessarily the newest AI product. It’s the one that solves the business problem without unnecessarily increasing cost, complexity or risk.
Measure the Result
AI adoption should have the same accountability as any other technology investment. Define what success looks like before implementation. Depending on the use case, that could mean:
- Fewer employee hours spent on a repetitive task
- Faster customer response times
- Reduced manual data entry
- Shorter reporting cycles
- Fewer process bottlenecks
- Faster access to internal information
Then measure the result. If the tool doesn’t materially improve the process, don’t keep it simply because it uses AI.
AI Also Needs Human Oversight
Efficiency isn’t useful if the output is wrong.
Generative AI systems can produce inaccurate information, omit important context or generate answers that sound convincing despite being incorrect. Human review should therefore be built into workflows where accuracy matters.
The amount of oversight should depend on the use case.
Using AI to create a first draft of an internal email is very different from using it to summarize a contract, provide financial information or make decisions affecting customers.
NIST’s AI Risk Management Framework is built around managing AI risks throughout the design, deployment, use and evaluation of AI systems—not simply selecting a tool and considering the job finished.
Build an AI Strategy Around Business Value
AI can absolutely create meaningful efficiencies for small and midsize businesses.
But adopting AI isn’t the objective.
Improving the business is.
Start by identifying where employees are losing time, where processes are unnecessarily manual and where better access to information could improve productivity.
Then determine whether AI is the right solution.
If it is, evaluate the technology alongside its security, data privacy, integration and management requirements. Start with a controlled use case, establish a measurable goal and evaluate the results before expanding.
That approach isn’t as exciting as adopting every new AI tool that hits the market.
It’s far more likely to produce value.
Not Sure Where AI Fits in Your IT Strategy?
Invenio IT helps businesses evaluate technology based on their actual operational and security requirements—not what’s generating the most hype.
If you’re considering AI or other new technology, we can help you evaluate how it fits into your existing IT environment, identify security and data-protection considerations and determine where technology can deliver meaningful business value. Schedule time to talk to a data protection specialist today.
