From AI Chaos to Control: A Flexible Data Integrity Ecosystem

If you’re leading any kind of AI initiative right now, you already know the opportunities are vast – but so is the complexity. Between widespread generative AI adoption, a wide variety of LLM options, and compelling visions of agentic AI-fueled automation, the pace of innovation is extraordinary.

But the fact is this: we won’t get the most from our AI initiatives unless we have full control: control over the technologies we use, how we use them, where, and – most importantly – the data that fuels them.

When we lack that control and choice, AI can simply feel chaotic and risky, rather than empowering and valuable.

So, how do we avoid the chaos? It comes down to embracing an AI ecosystem for data integrity – that’s our vision at Precisely, and we’ve been making some tremendous strides toward advancing that mission.

What exactly do I mean by an AI ecosystem for data integrity? We’re talking about a connected, flexible ecosystem that directly supports your AI-driven priorities. We all know that trusted AI starts with trusted data – and our vision at Precisely is for you to be able to access and leverage that data, at scale, without being limited by your chosen AI tech stack.

This all comes back to choice and control, as well as interoperability. Whether we’re operating in the cloud, on-prem, or complex hybrid environments, we need the flexibility to choose the AI models, tools, and policies that align with our unique goals and risk appetite. We need to know that when we integrate those environments – especially with our most sensitive and critical data – we’re doing so with maximum data integrity.

Why Control Is So Hard: AI Adoption Growing Pains

Now, I know that all of this control and flexibility sounds great in theory, but it’s been difficult for organizations to tame the AI chaos up to this point.

One reason is that nearly every vendor in our tech stacks is injecting AI into their offerings – sometimes without transparency or adequate governance controls – faster than we’re able to adequately vet them.

From infrastructure to applications, we’re being asked to adopt capabilities before we’ve even had time to assess the risks or discover the benefits.

We need to separate what’s valuable from what’s risky. We must leverage AI’s potential without losing control over how our data is used.

After all, not all environments are created equal. Some might be secure and fully governed. Others? Not so much. AI models have varying levels of transparency. And not all AI tools can support the full scope of our data, especially across hybrid environments, mainframes, and legacy systems.

Ultimately, AI doesn’t really care about where our data lives now – it cares about where our data is going.

That’s what makes this idea of an AI ecosystem for data integrity so important to us at Precisely – we care about your data’s entire journey and want to make sure you have what you need to succeed, regardless of where you stand now. Across our data integrity solutions, AI plays a huge role in driving efficiency and providing valuable recommendations that help to accelerate the delivery of trusted data to the various technology environments most important to realizing your AI goals.

With that in mind, I want to share three key best practices and outcomes to help you get the most from a robust, impactful AI ecosystem for data integrity – one that helps you meet your objectives across top AI-driven initiatives like analytics, automation, and cloud modernization.

1. Ensure collaboration, accountability, and scalability

The first thing I’ll cover goes right back to two of the core principles of our AI ecosystem vision: choice and control.

Both are within reach when you embrace concepts like bring-your-own-model (a true game-changer) and enable robust governance. This gives you the freedom to innovate on your own terms. Here’s what that means, at a glance:

• Align AI initiatives with your existing investments
• Uphold internal security and compliance standards
• Easily adapt to changing business needs

And, critically, this approach fosters cross-functional collaboration, accountability, and scalability of AI initiatives across your data pipelines. AI governance capabilities help you engage the right people and processes that support your AI decisions, while helping support model value and performance evaluation, data drift monitoring, and usage policy enforcement.

2. Get the insights you need – from wherever you need them

To stay competitive, your organization depends upon fast, confident decision-making grounded in trusted data. However, real-time access to the data you need hasn’t historically been easy.

This is where interoperability comes into play. We take a lot of pride in our network of trusted partners at Precisely because we know that ecosystem integrations with many of our leading tech partners – which include AWS, Confluent, Databricks, Google Cloud, IBM, Microsoft Azure, ServiceNow, and Snowflake – mean insights that are faster, more consistent, and reliable for our customers.

It’s key to have access to integrations like these to ensure a consistent flow of trusted data across your entire ecosystem – from legacy systems and cloud platforms to AI and analytics applications. It’s how you derive trust and value from your investments.

3. Choose all the partners that support your vision

That’s right – “partners,” plural.

We’re all entering this new era of AI-powered intelligence together, and my final bit of advice is to choose partners that offer the unique capabilities required for your most pressing needs. They need to be committed to continuous investments in innovations that will make your life easier and your AI initiatives as successful and scalable as possible.

As you evaluate partners and AI technologies, I also recommend taking a closer look at how your team works through this process.

At Precisely, for example, we’ve had a cross-functional AI Council in place for two years now. This is a group dedicated to vetting AI use on a few different levels:

• For our products: This includes the AI capabilities we’re incorporating into our products, and how to do so responsibly – so we ensure the best outcomes for our customers, without forcing potentially risky capabilities that they’re not ready for.
• For our internal teams: This includes determining how to deploy AI tools internally so our teams can reap the benefits of efficiency, productivity, and more – which also ultimately contributes to better experiences and results for our customers and partners.

This has been an effective way for us to streamline decision-making and stay up to date on the latest developments as these technologies continue to evolve at such a breakneck pace.

Embrace an AI Ecosystem for Data Integrity

I really can’t overstate how thrilled our team is about this next chapter in our AI ecosystem. Especially today, as the data and AI space continues to consolidate and your choices become more constrained, the stakes for interoperability and control have never been higher – and that’s exactly what our ecosystem delivers.

The innovations that make up this ecosystem came to life with your needs in mind – they’re the result of our daily conversations with customers and trends in the market at large. And now, they’re ready for you to put into action.

Faster insights, better decision-making, and greater AI scalability are all in our future – and there’s so much more to come.

Be sure to catch up on all of our AI developments and what they mean for you.  And to learn more, don’t hesitate to reach out to our team directly.