How to discover Trusted and Meaningful Insights using AI

Successful AI starts with the alignment of human minds. In practical terms, everyone should collaborate around five things: how the initiative is managed, where the data comes from, how it moves throughout the organization, who can access that data, and how prepared those people are to act on AI-generated insights. In other words, a smart strategy. 

Penske, the subject of our first Data Leader Scenario in Part 2, showed just how powerful upfront collaboration can be.

Penske demonstrated a critical behavior that contributed greatly to the success of their AI project: rules before tools. Yes, there are many AI tools at your disposal, and many of them can help you drive trusted and meaningful insights. However, those tools are worth nada without robust, well-thought-out strategies and policies that inform their use. So, where to start? 

As mentioned in Part 1, giving data governance and quality their due diligence is a crucial first step. Set the guidelines that influence the entire lifecycle of data, from creation to collection to storage to disposal, including who can access or use that data every step of the way. Chances are, there’s also legislation or regulations that determine how your industry addresses and executes data governance and privacy issues, so make sure your framework accounts for these and establishes clear oversight on ethics, methods of mitigating bias, and training. 

From there, it’s important to make sure your plans for data and AI make sense for your company’s plans, then set goals that are relevant, measurable, and achievable. If you’re using GenAI, then maybe your metric is speed. If you’re deploying an agentic solution, perhaps it’s employee or customer satisfaction. Do you work in a highly regulated industry? Then compliance and auditability might reign supreme. Pick and choose which metrics matter the most and keep that list focused. Create too many targets and you risk missing all of them.

If you’re tasked with an AI initiative but don’t have a specific use case in mind, simply think of one process that could benefit from automation. Be honest. You thought of hundreds, didn’t you? Our recommendation: Focus on specific processes like insight generation or customer service. These incremental changes are an easier “sell” to stakeholders, ones that maximize return and positively change the way your business operates (versus changing your business entirely). 

Plus, you’ll really be seen as a Data Leader when your people have the support to do more strategic, developmental work. Leave all the heavy, tactical stuff for AI to handle: real-time fraud detection, development of predictive models for IoT devices, automated credit approvals, and report generation. 

You’ve talked with the stakeholders. You nailed down the strategy. And your entire organization is adopting the new policies. Kudos! 

But now your data needs to be ready for prime time, fast. Because your AI becomes whatever it eats: the good, bad, and the ugly. So it’s imperative that you have a built-in data QA system in place to make your models lean, mean, insight-generating machines — or else you risk wasting time and money. That’s precisely what RS Components was seeking to avoid in our next Data Leader Scenario. 

1. Diverse
   
   Having more structured and unstructured sources leaves less room for biases. This means your data can’t be siloed. Instead, it should span a wide range of patterns, perspectives, variations, and scenarios that are relevant to the application. If you don’t diversify, the training data becomes a liability for prejudicial assumptions and unfair decisions. 

2. Timely 
   
   We covered this one a bit in the “real time” section of Part 1, but it’s important to reiterate because if data isn’t timely, it won’t help you make predictions and informed decisions. Training AI models with outdated information is like putting water in your gas tank; it won’t take you anywhere. To ensure AI is using the latest and greatest, your data pipeline must be as low-latency and real-time as possible.  

3. Accurate 
   
   If the data is flawed, incomplete, or not aligned with project goals, your AI model will be just as flawed. Follow these three operations to get data correct: Profile source data, operationalize remediation strategies, and enable data lineage, an important tool for tracing a source’s history to measure impact and prevent accidental modification. 

4. Secure 
   
   Simply put, leaving data vulnerable is like leaving your front door open. And it’s not just the integrity of your AI model that’s at stake; it’s your reputation. Thankfully, automation can boost security at scale, helping you detect and classify data at scale, manage access via masking and tokenization, and control how data moves throughout the company. 

5. Discoverable 
   
   It’s all about getting the right data in the right format and making sure it gets to the right people and systems. Strong metadata and data catalog management can help here. Processes like semantic typing make it easier for automated systems to understand data and provide more context, while a business glossary and metadata catalog make it findable and searchable, perfect for those with limited data or AI acumen. 

6. Consumable 
   
   If AI can’t use your data, then you can’t rely on it to produce high-quality results, especially when deploying advanced automation like AI agents. Making data easy to absorb enables AI systems to process information fluidly and transform it into smart, imaginative outcomes. 

With their powers combined, your AI readiness quotient goes sky-high. Leaving gaps in your armor — even just one — can sabotage your decision-making and efficiency with inaccurate data or questionable insights. Make sure to cover all the bases. For a more in-depth read on the six steps, download our ebook “The 6 Principles of AI Ready Data” 

To be clear, in this lightning-quick, get-to-AI-no-matter-the-cost world, this train needs to be fast. Like, bullet train fast. And if the results don’t meet expectations or generate value, there goes all the work you put in. So don’t be a casualty, be a champion for your org and your team. That’s exactly what Nirmal Kumar did when he helped IndianOil adopt an AI platform with over 150 “power users”. 

Sure, we all want our AI initiatives to lead to big financial gains like Indian Oil’s. It’s also important to see the transformative value, not just in the bottom line, but in what your projects can deliver. Unsurprisingly, you’ll find that operational improvements can lead to substantive monetary returns as well. 

Everything comes together once you’ve established the right data foundation for AI as described earlier in Part 2. Even then, should you blindly trust what the computer says? Definitely not. You can use trust scores to quickly vet data quality. It’s like a green light that confirms your data meets the specific criteria that aligns with your goals. That’s confidence at a glance. Confidence that your data will drive meaningful insights so you can make quicker choices and more accurate predictions — and get to market faster. 

Unfortunately, it turns out cloud data efforts cost way more than most people thought, with most GenAI prompts costing more than the average search query. And those costs will only increase as new reasoning models demand more power by 2027.¹¹ All that might sound like ROI buzzkill, but consider this your wake-up call to factor in both expense and energy requirements. With more inclusive cost governance, you can better forecast and track expenses with fewer surprises that won’t derail your AI models.  

And don’t worry, many more cost-saving strategies lie ahead in Part 3. 

The sooner you trust your data and AI, the sooner your organization can transcend the doubt, indecision, and inefficiency that hold back even the most hardened Data Leaders. When your data works harder at the foundation, your output is stronger. You’re acting more than reacting. And your next breakthrough is closer than you think.