1. Shadow AI

According to a 2025 MIT report, 90% of the workforce reportedly uses AI chatbots, even though only 40% of firms have an official LLM subscription.

​Shadow AI threatens data security, even in tasks like using ChatGPT to summarize information. Any AI usage without company sign-off counts: 81% of firms lack vital visibility into AI usage, and 65% are already battling increased security risks because of that.

​Before launching your next AI initiative, take stock of what’s being used, how and where. Gartner analysts have predicted that 40% of organizations will be hit with security threats because of shadow AI. AI and data audits are the first critical step to grappling with governance gaps; it's impossible to stop data leaks without knowing where their sources are.​

2. Data Foundations Aren’t AI-Ready

For most firms, a core weakness isn't the data itself, but rather the systems and governance used to manage it. At this point, we've all seen accounts of the rising consequences between bad data, organizational readiness and AI adoption: financial losses, cyberattacks, leaked confidential information, reputational damage, workflow bottlenecks, bias and hallucinations, to name a few.

​Yet most firms fail to realize that their data is unreliable until these disasters strike. Before diving into how data should be properly managed, the definition of "bad data" needs to be clear. Datasets with issues like poor formatting, inaccuracies, inconsistencies, biases and duplicates are inherently bad. Outdated data is another culprit behind AI systems that only exacerbate poor decision making and outcomes.

​It’s critical to treat data management as a parallel project to AI deployment. It's also a necessary step to building an AI strategy that successfully scales. Set the data guardrails in place, recognizing that data readiness isn't defined by the quantity of information available but rather its quality and usability across systems and workflows.​

3. AI Governance Isn’t Operationalized

Poorly performing AI doesn’t always indicate a problem with the model. Chances are, the governance framework behind the technology failed to make the leap between paper and practice.

Many firms have some sort of governance policy around AI. Still, gaps remain in how these policies are being applied to daily operations. Decision rights and explainability, escalation pathways, enforceable controls, accountability and ownership—these are just some of the key areas where operationalized governance becomes essential for scalable adoption.

Keep in mind that AI governance is as much of a corporate concern as it is an IT one. Strategic governance is at the heart of AI deployment that's compliant, secure and conducive to business ROI. This governance needs to be accessible on an organization-wide level—something that only 41% of companies with an AI strategy are working toward.​

4. Humans Aren’t In The Loop

Part of solving the operationalized governance puzzle is effectively designing for human involvement alongside AI. There's no question that humans need to be integrated across any AI strategy. Organizations need to focus on where people are needed, which decisions and workflow stages require oversight and how AI should be operationalized to facilitate these interventions.​

5. Focused On The Frontlines

So much of AI deployment focuses on the frontline and customer-facing touchpoints. That’s just the tip of the iceberg in what AI could be doing for organizations’ ROI. The operational and back-office functions—finance, compliance and HR, for example—are where processes are most repetitive, document- and data-heavy, and where compound ROI gains are made over time.

Part of this issue boils down to measurement, and the other part is tied to change management. Leaders who don’t track the extent or depth of AI adoption fail to pick up on the missed potential beyond the front-facing tasks. Besides that, the visibility of AI on the frontlines is a driving force for leadership endorsement to focus on these areas. But visibility doesn’t necessarily mean value.

Pinpoint where AI can be doing more legwork behind the scenes. How much time and money can be saved by automating invoice processing in finance or audit generation in compliance? How does the backend workflow support the front-desk chatbot to process a customer request and see it through?​

6. What Matters Isn’t Necessarily Measured

Don’t fall prey to sinking money, time and energy into AI strategies that are measured by benchmarks that don’t make sense. Map out your AI strategy according to internal evidence and historical data, aligning it to improve existing business and operational KPIs. This is what helps organizations determine whether they're AI mature: They’re not throwing in new adoption metrics, but closely coordinating AI efforts to drive ongoing business goals.

Think about it: Any respectable medical doctor wouldn't make a diagnosis without examining the patient’s symptoms and medical history first. The same applies to your AI game plan. Relying on vendor benchmarks as well as internal and industry anecdotes alone will likely steer your strategy in the wrong direction—before you’ve even started.

Examine and measure the gains across key areas such as volume, quality and cost wins. Are workflows processing more outputs at a faster rate? Are outputs accurate and reliable? What's the fully loaded cost of this workflow now versus before? Organizations that keep a pulse on relevant measurements of AI success are best prepared to avoid burning through AI budgets at breakneck speed.

These six steps position organizations to build a sustainable, long-term AI advantage because they cover the core bases that ensure firms possess the AI maturity and readiness needed to succeed.​

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