It’s no secret that AI is transforming how we work, how we serve customers, and how we innovate.
In recent blogs, we’ve covered how Naviant customers are stepping into their AI journeys with initiatives like:
- One customer is using it to analyze unstructured data.
- Another uses AI to enhance their workflows, sharing that it’s using AI-powered RPA bots to simplify its AR process for certain cases.
- Yet another has started using AI-powered chatbots and document recognition tools to support customer interactions.
But before you dive headfirst into AI adoption, there’s one critical question to ask:
Is your organization actually ready for AI?
Truthfully, most aren’t. And if that’s you, it’s okay, because this blog is your guide to changing that.
We’ve put together a practical checklist to help you assess and strengthen your AI readiness. Whether you’re just starting your journey or looking to scale, these 10 steps will help you build a solid foundation for success.
The Ultimate AI-Readiness Checklist: 10 Steps to Get AI-Ready
Step 1: Audit Your Infrastructure
To tap into AI’s potential while maintaining security, you need a solid, scalable infrastructure that’s equipped to manage your data. Specifically, your databases need to be secure, compliant, and flexible enough to gracefully handle both business booms and the slower times.
The key to achieving this solid foundation is adopting a federated, cloud-native platform like Hyland Content Intelligence. This platform will allow you to deploy modular services that transform your data into AI-ready formats. This way, AI systems can establish real-time connections, pull up contextually relevant information, and operate on secure, scalable, and audit-ready content.
Step 2: Curate Your Data
High-quality AI output relies on curated, enriched content. Here’s how to get there:
- Curate Your Content
Here, you’ll use tools to extract, normalize, and structure your content to ensure it’s formatted in an AI-friendly way.
- Normalize and Structure Your Data
In this step, you’ll convert your unstructured data into standardized formats. This is a crucial step because machine learning (ML), analytics, and automation workflows can’t do much with unstructured, messy data.
- Generate New Metadata
Here, you’ll be boosting searchability and improving AI model accuracy. Getting the right metadata in place will help your systems find what they need faster and make smarter decisions.
Step 3: Establish Governance Protocols
AI is powerful, but strict guidelines are needed to ensure its safety, efficacy, and security. It’s very much a “With great power comes responsibility” situation (thanks, Spider-Man).
AI Governance can get you there, helping you adhere to compliance and security standards. In this step, you’ll develop clear guidelines for employees to follow when using AI to mitigate risks associated with the technology. When you establish these strict standards, you can implement AI safely, covering areas like data access monitoring, malicious incursion detection, and setting responsible AI policies.
To solidify your guidelines even further, consider creating an AI council. This group of experts can oversee your AI implementation efforts and ensure that your guidelines, current laws, and best practices are being followed every step of the way.
Step 4: Build Ethical Guardrails
AI-ready businesses don’t just prioritize quality AI outputs, they actively monitor for bias and defend against tainted data that could lead to unauthorized results.
To be AI-ready from an ethics standpoint, you need guardrails in place to keep things fair, transparent, and accountable. This will allow you to explain how your AI engine came up with its answer.
Step 5: Upskill Your Workforce
AI tools are only as powerful as the people using them, but the competition for AI skills talent is high across industries. Fortunately, you can bridge the AI talent gap with a strong upskilling program and an emphasis on user-friendly AI technologies.
Upskilling happens when your staff improve their current skills and expand their abilities with structured training programs, and in the context of AI, your entire workforce will likely require some level of upskilling. You can learn more about how to build a strong upskilling program here.
And on the technology side, AI experts recommend options that come with intuitive interfaces like point-and-click low-code tools, as they can empower non-technical users to get involved, and quickly.
Step 6: Test and Tune Your Models
Lastly, it’s important to note that AI isn’t a “set it and forget it” technology. It needs ongoing testing, tuning, and validation to stay accurate and relevant. Schedule regular check-ins to assess your progress, update your roadmap, and explore new opportunities.
To really put your models to the test, use real-world scenarios in your testing, watch for overfitting, underfitting, and performance dips, and adjust accordingly. Over time, this attentive approach will help you maximize your investment and achieve continuous improvement.
Are You AI-Ready Yet?
AI has the potential to revolutionize your organization, but only if you’re ready for it. By following this checklist, you’ll build a strong foundation that sets you up for long-term success.
If you have questions or want help assessing your AI readiness, comment below. We’d love to hear from you!
