Is your company ready for AI? 5 questions for the C-suite before implementation

Introduction

With the rapid growth of AI-powered business technologies, it seems as if there’s now a smart solution for tasks at every level of the enterprise. From generative AI models designed to handle basic customer service requests, to agentic AI capabilities that can forecast supply chain demand, it’s no wonder there’s a rush to adopt these tools and unlock efficiency gains.

Look closer at the implementation process, however, and it’s clear why C-suites – CIOs in particular – are losing sleep. Navigating the AI revolution comes with hundreds of additional questions: What does this mean for my workforce? How will the use case I choose impact operational security, and how do I mitigate that risk? Is now the right time for my company to dive in, or should I wait for additional capabilities?

Before your company begins an AI journey, aligning on five key questions can help you devise a strategy that maximizes your investment.

Let’s dive into the right ways to go about answering each of these five questions.

Q1. What’s our business case?

Although it may appear AI use cases are endless, these possibilities don’t always translate into impactful outcomes. Adopting AI without a clear understanding of how it can help your business can be an expensive mistake. Companies are already seeing the consequences of launching without a strategy – so far, businesses have invested $30-40 billion in generative AI, but 95% of investments have produced no results, according to anMIT study. Don’t let a sense of “we need to be first” lead your organization to experiment for experimentation’s sake.

MIT found that misalignment with day-to-day operations is one of the key reasons why these projects fail. Instead of letting technology drive your thinking, let your complexities lead. Consider your 3-4 main business objectives for the next year and look for small ways AI can help you achieve those objectives. Once you’ve proven a use case, leverage that foothold to scale up AI investments.

It’s also important to recognize that many AI technologies are still young, and promising use cases may not pan out. To avoid letting unhelpful AI initiatives become cost centers, set checkpoints and hold regular project team updates to review progress. Don’t be afraid to redirect resources if it appears a project is going off target.

Q2. How do we build trust around AI?

The generative AI shift is happening so fast that your customers – let alone your employees – can’t catch up. Even CEOs and CTO/CIOs aren’t on the same page regarding staff sentiment toward AI implementation. Kyndryl found45% of CEOs believe their staff is resisting the technology, while only 8% of CTOs/CIOs agreed. It is imperative that leadership agrees on a rollout plan. If a generative AI tool is introduced as a “black box,” with no transparency around how the model was trained, questions around data security could further slow adoption.

As your team begins adopting AI tools, don’t rush the development phase. Take time to clean the data behind the model, ensuring there are no out-of-date or incorrect records that might appear in AI output. Put rigorous security protocols in place to ensure the data cannot be corrupted and test the model to ensure it’s not offering hallucinations. Trust, but verify.

Finally, be open with teams about how, where and why AI solutions will be implemented. Kyndryl’s study found that, of companies considered AI pacesetters, 47% said they’re prioritizing the process of ensuring transparency around AI goals and implementation, compared to 39% of other companies. Giving employees parameters to work with the tools, and demonstrating how those guidelines will protect operations, can help demystify the technology and encourage quicker acceptance

Q3. Should data be siloed – or should we break it free?

In general, breaking down data silos can be a catalyst for greater innovation. When sales and marketing can see what the other is doing, or HR & Legal can share data across a single solution, there’s less chance for error and greater chance the task will be completed faster, with fewer headaches.

For organizations experimenting with AI for departmental specific projects, e.g. using generative AI to simplify copy development for marketing, it may not be necessary to break down silos. However, for any project involving multiple business units, AI has made the end of data silos a “need-to-achieve,” not just a “nice-to-achieve.” An agentic AI model that needs sales forecasts and inventory data to predict potential shortages will function far better if data is available through a single source of truth.

The numbers are clear: Gartner predicts that by2026, organizations will abandon 60% of AI projectsunsupported by AI-ready data. To avoid hitting a wall during implementation, take the time to organize your data and improve accessibility across business units before building your AI model.

Q4. Who owns the data?

It’s a complicated question, but answering it can better position your generative AI initiatives for success. Whoever owns it needs to assume the responsibilities of overseeing data collection, maintaining data integrity and ensuring data privacy. Of course, this requires aligning across business units to develop processes for inputting and cleaning all data, and that can be a full-time job.

In some organizations, these tasks would fall to the Chief Data Officer (CDO). More than 80% of organizations have a designated CDO, according to MIT – but only 47% report to technology leadership. In organizations without a distinct data management role, or regular collaboration between the departments, there may be tension between the business and IT. That gray area is likely to delay AI transformation. Rather than letting politics get in the way of innovation, bring senior leadership together once your AI-focused goals have been defined and appoint a cross-functional team to create and execute a data management strategy.

When you’re determining who should lead that team, consider whether your AI initiative is “project-focused” or “product-focused.” If you’re “project-focused,” you may be able to appoint a team member to serve as a temporary bridge between the business and IT. If you’re “productfocused,” it may be worth hiring a manager who can oversee data quality full-time, long-term.

Q5. How fast is too fast?

We talked about the need to figure out business use cases that ensure a successful AI implementation. Still, AI’s evolution isn’t slowing down, and many business leaders are concerned at the thought of pausing a project. A Coleman Parkes study found 91% of decision-makers were afraid competitors would have a leg up if their AI was more advanced.

If you’re under pressure to get your AI initiative operational, make sure you balance your timeline to ensure you’re not cutting corners. Data readiness is absolutely critical to ensuring your AI tools function properly. Improperly cleaned or protected data could lead your company to put a faulty or hallucination-prone tool on the market.

Rushed AI implementations can also lead to security issues that put your company at risk of legal action. Coleman Parkes’ survey also found 43% of decision-makers have halted AI investments because of cybersecurity risks, and 36% have done the same because of regulatory and compliance concerns. Additionally, 76% are concerned their AI tool will leak proprietary data to the public. Take the time to vet the security measures of the AI platform you’re adopting and set monthly meetings to ensure your team is up to date on any AI-related litigation or lawmaking that could impact your project.

Conclusion

Generative and agentic AI tools are new frontiers, so CIOs are on an even playing field – most are just trying to figure out what these changes mean for their operations. Getting the team to agree on a goal, dividing the work of preparing data and designing your model, and ensuring security along the way can feel like a Herculean lift.

If you were unsure of how to answer one or more of these questions, don’t feel as if you have to navigate AI alone. Iron Mountain’s experts can help maximize your investment in new AI solutions. Our InSight Digital Experience Platform (DXP) can help throughout your implementation, from getting your data AI-ready to improving your ongoing data governance and creating automated workflows throughout your business.

Contact us today to get the conversation started.