AI maturity is what happens when curiosity meets control

When AI first entered the enterprise, every week brought a new tool, a new headline, a new promise of transformation. The excitement was real, but the results were inconsistent. Now, the conversation has matured.

We’ve learned that success isn’t about chasing every use case. The teams I work with aren’t asking “What can AI do?” anymore. They’re asking, “Where does AI make the most impact?”

That mindset shift is changing how enterprises think about AI adoption and innovation. We’ve moved beyond a ChatGPT-for-everything approach toward embedded, specialized tools for everything from code editing to data modeling to workflow coordination.

Balancing discovery with control

In the push to innovate ahead of the curve, how do you balance technology discovery with responsibility and control? If you’ve built a culture that rewards innovation, your talent won’t wait for permission to start trying out the latest and greatest technology releases. That’s a good thing. The key is to harness that curiosity safely, turning experimentation into transformation.

Encourage AI curiosity by padding it with structure and disciplined investment in the AI tools that work for your organization. Because without guardrails in place, employees will still explore, just without oversight.

Organizations that fail to orchestrate and communicate clear AI governance may see a flood of shadow AI, so-called workslop, and operational chaos in the place of transformation.

The pillars of safe, scalable AI adoption

AI can finally deliver on much of what vendors have promised and yet, according to BCG, 74% of organizations have yet to show tangible value from their AI investments and only 26% are moving beyond proofs of concept. A separate AI survey from Deloitte found that 31% of board directors and executives felt their organizations weren’t ready to deploy AI — at all.

This isn’t too surprising. Enterprises faced similar challenges during the cloud adoption era. But as with any new technology, the key to capitalizing on it lies in empowered people, clear policies and consistent processes.

Here’s what that looks like in practice.

1. The people pillar: Equip employees to experiment

Treat every employee like a scientist handling experiments that could result in burn or breakthrough. At CSG, we hold regular open forums where employees from various departments come together to authentically share AI use cases, best practices and new tool suggestions.

This upward feedback from the people closest to the technology has been invaluable. It fosters cross-functional learning between teams and leadership, inspires passion and helps shape our AI adoption strategy.

For example, one of our developers proposed switching to a new, AI-driven code generation solution that (after appropriate testing) has become an integral part of our enterprise toolkit.

Once curiosity is sparked, it’s critical to create a protected space for exploration to manage shadow AI effectively.

An EY survey revealed that two-thirds of organizations allow citizen developers to build or deploy AI agents independently. Shockingly, only 60% of those organizations have formal policies to ensure their AI agents follow responsible AI principles. This could be a costly oversight. Breaches involving unauthorized AI use cost an average of $4.63 million, nearly 16% more than the global average.

However, banning these practices outright will just drive usage underground. The better approach is enablement — empowering employees with access to secure, enterprise-grade platforms where they can safely test and build.

The other piece to this puzzle is talent upskilling. Curiosity only delivers value when people have the knowledge and confidence to start testing the waters.

For example, to better train CSG talent, we launched an internal AI academy — a self-guided learning journey that allows employees across the organization to realize benefits of AI that fit their curiosity. The courses cover role-specific AI use, authorized tools and responsible experimentation. We then check utilization reports to help identify adoption gaps, success stories and further training needs.

2. The policy pillar: Governance as the guardrails

Trust, governance and risk mitigation are the foundation of enterprise AI maturity. In that previously mentioned EY survey, almost all respondents (99%) reported their organizations suffered financial losses from AI-related risks, with the average loss conservatively estimated to be over $4.4 million. However, the same survey indicated that organizations with real-time monitoring and oversight committees are 34% more likely to see improvements in revenue growth and 65% more likely to improve cost savings.

That’s why we established a governance committee. It brought together leaders across legal, compliance, strategy and the CIO and CTO offices to eliminate silos and ensure every AI initiative has clear ownership, policy alignment and oversight from day one.

The committee wasn’t formed to slow down progress. On the contrary, governance rails keep innovation on track and sustainable.

With the initial structure in place, the committee’s focus shifts to protection. Enterprises sit on massive volumes of customer data and intellectual property and launching AI without controls exposes that data to real risk.

If one of your developers uploads IP into ChatGPT or a lawyer pastes contract text into a public model, the consequences could be devastating. To navigate these concerns, we authorized secure, internal access to popular AI tools with built-in notifications that remind users of approved usage.

Vendor management is another major focus area for us. With so many vendors embedding AI into their products, it’s easy to lose track of what’s actually in use. That’s where our governance committee will step in. We are working to audit every internal tool to identify risks and avoid vendor sprawl and overlap. Doing so will allow us to maintain visibility into and control over how our data is shared — a crucial piece in safeguarding our customers’ trust.

Finally, governance also needs to extend to how you reinvest the gains. AI creates efficiencies that free up capital, and those newfound resources require a strategy. As we think about strategy across our organization and balancing demands, it’s important that we reinvest those capital savings responsibly and sustainably, whether into new tools, new markets or further innovations that benefit our business and our customers.

3. The process pillar: Avoid the pilot graveyard

In 2025, 42% of businesses reported scrapping most of their AI initiatives (up from just 17% in 2024) according to an S&P Global survey. On average, organizations actually sunsetted nearly half (46%) of all AI proof-of-concepts before they reached production.

The truth is, even the most advanced technology will end up in the ubiquitous AI pilot graveyard without clear decision frameworks and proper procurement processes.

I’ve found success starts with knowing where AI is truly necessary. You don’t have to throw a large language model at a problem when simple automation actually delivers faster, cheaper results.

For example, many back-office workflows, like accounting processes with four or five manual steps, would probably benefit from standard automation. Save your sophisticated, agentic solutions for complex, tightly scoped functions that require contextual understanding and dynamic interaction.

As you do so, keep in mind that only 44% of consumers are comfortable letting AI take action on their behalf. Part of building trust with customers is making sure they don’t feel “stuck” with chatbots and agentic experiences that feel out of their control and not personalized to their needs.

Once you’ve identified the right use cases, a rigorous and disciplined selection process will ensure you can successfully bring them to life. We use bake-off style RFPs to evaluate vendors head-to-head, define success metrics before deployment and ensure every pilot aligns with measurable business outcomes.

During the selection process, it’s also important to plan for the future. Free tools can be a tempting way to test capabilities, but beware: if they become integral to your workflow, you may put your company at the mercy of pricing shifts or feature changes outside your control.

Finally, scaling success requires alignment and awareness. Once a platform or process proves itself, it needs to be deployed consistently across the organization. That’s how you turn one good pilot into a repeatable process.

Lead with curiosity, scale with control

When it comes to AI maturity in the enterprise, the best organizations move fast but with intention.

Curiosity fuels innovation, but structure sustains it. Without one, you stall. Without the other, you spiral. The future belongs to those that can balance both; building systems where ideas can move freely and securely.

This article is published as part of the Foundry Expert Contributor Network.
Want to join?