AI vendors tend to push expensive “AI transformations” built for Fortune 500s: they’re expensive and require dedicated implementation teams. Meanwhile, tools packaged for mid-sized businesses are usually far too simplistic for cross-functional workflows and often can’t scale with the company.
How do you thread the needle? To keep the sophistication without the bloat, your artificial intelligence implementation needs to combine (1) tight upfront planning to protect your current workflows and (2) nimble execution to deliver quick, measurable ROI.
Phase 1: Assess your company’s AI readiness
AI will only work if you feed it high-quality data. Ask these questions before you spend anything:
- Can you determine and document where your key data lives?
- Can you assess its cleanliness? Do you have a way to flag if data is stale? Can you systematically weed out duplicates and inconsistencies?
- Do you have a single source of truth (one trusted place for the latest, correct version of your data)?
These checks will tell you whether your data is ready for AI. If you can’t answer both confidently, stop and clean up your data before going further.
Phase 2: Choose a fast, low-complexity use case to build organizational momentum
Your first foray into artificial intelligence implementation should be built around a high-impact but narrow enough workflow. Many mid-sized businesses start with:
- Customer service ticket routing/drafting
- Automated accounts payable and invoice processing
- Sales call summarization and automated CRM entry
Using pre-built tools is often the best route for these contained use cases. Such deployments only take a few weeks to roll out if you work with an artificial intelligence implementation specialist like our team here at CA. You don’t necessarily need to build a custom application right away especially if you don’t possess unique proprietary data that warrants that level of engineering.
Phase 3: Run a time-boxed Proof of Concept (PoC)
Before you commit to a larger deployment, you need to validate the tool in a low-risk scenario within a fixed window (four to six weeks). Select a small group of willing users and isolate real-world data (avoid overly sanitized sample data). Make sure that your hard metrics are concrete (for example, “reducing average resolution time by 20%”).
Run weekly check-ins to catch friction early. Throughout this validation cycle, be sure to (1) keep a running record of both qualitative and quantitative feedback, and (2) actively manage user resistance.
By the end of the trial period, you should have a PoC report showing how your tool performed against your target metrics. List any integration issues. The PoC should be evidence-heavy enough to inform a final Go/No-Go recommendation.
Phase 4: Deploy the AI tool
Appoint peer champions within every affected department: someone who can help their colleagues get comfortable with the artificial intelligence implementation process.
It’s also important to provide role-specific training and ongoing coaching to boost user adoption. If your employees hate the tool or fear that it will replace them, you’ve wasted your money.
CA’s team of AI implementation specialists can help you map out an artificial intelligence implementation roadmap that allows you to prove whether the use case justifies further investment before scaling. Contact us to get started.
