Steps to Help Get Your Strategy AI Implementation Correct

An AI Implementation Guide

AI is no longer the future—it’s happening now. But turning curiosity into real-world value can feel like a minefield. Use this simple AI framework to help your businesses implement AI with confidence and clarity.

Choose the Right Project

AI doesn’t have to start from scratch. Often, the most significant wins come from enhancing existing processes with automation. The key is to focus on initiatives that align with your business goals, offer a clear ROI, and are technically feasible.

And of course, make sure there’s a clear evaluation criteria set. Assess the financial and operational feasibility and stakeholder impact.

We’d recommend you start small, instead of going for a massive business transformation. Pick one project/use case where AI can deliver quick improvements. And remember to ask the question, “Do I need AI for this?” If the answer is “no” or “probably not” and a simpler tool does the job, use it.

Test out the Project

Before scaling, validate your ideas with small-scale pilot tests. Make sure your data, systems, and people are ready. Think about User Experience (UX) considerations and assess engagement and impact.

This is normal if there is some initial resistance from teams, as AI can seem daunting. The best step is to involve your teams early and frame AI as a support to their work, not as a threat.

Planning and Developing

The key to a successful project is one that has a good structure and roadmap.  So, what do you need to do as part of your project planning? Build a roadmap using agile methods, clear priorities, and the right tools. Address high-risk components first to minimise delays.

Begin with low-risk, familiar tools to ease people into using AI. Building confidence with accessible solutions like Power PDD can help boost AI literacy before scaling.

Execute Efficiently

Once the groundwork is done, build and deploy your solution. Use CI (continuous integration)/CD (continuous delivery) pipelines to keep development smooth and iterative, and be sure to consider security and compliance, such as ethical AI policies, and governance.

A tip for corporate use cases is to use models that do not harvest data for model training (such as Openai and Anthropic) and avoid free tools, as protections will not be in place.

 Launch and Learn

Adoption matters as much as delivery. Promote your AI initiative internally when you first launch, build case studies, and track what’s working (and what’s not). Get early adopters involved and gather feedback so you can refine the solution before a full rollout.

After launch, it’s crucial to understand how tools are being used, the patterns they are being used in, and where they add value and where they don’t.

Let the data guide your next move.

Monitor the Progress

AI isn’t a one-off job. Monitor model performance, user engagement, and compliance regularly. The key is to stay flexible and be ready to evolve as technology and regulations change.

Avoid locking into a single model-use tool. The ideal set up is to use a platform that lets you upgrade easily or automatically select the best model for the job.

Overall, getting AI right isn’t just about the tech used, it’s about smart, practical steps. Start small, keep it human centred, and ensure your AI strategy grows with your business.

Find out how Telic can help you today by getting in touch.

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Neetika

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