AI is evolving at such dramatic pace that any step forward is a step into the unknown. The opportunity is great, but the risks are arguably greater. While AI promises to revolutionize industries – from automating routine tasks to providing deep insights through data analysis – it also gives way to ethical dilemmas, bias, data privacy concerns, and even a negative return on investment (ROI) if not correctly implemented.
Analysts are already making predictions about how the future of AI will – at least in part – be shaped by risk.
According to a 2025 report by Gartner titled Riding The AI Whirlwind, our relationship with AI is going to change as the technology evolves and this risk takes shape. For instance, the report predicts that businesses will start including emotional-AI-related legal protections in their terms and conditions – with the healthcare sector expected to start making these updates within the next two years. The report also suggests that, by 2028, more than a quarter of all enterprise data breaches will be traced back to some kind of AI agent abuse, either from inside threats or external malicious actors.
Beyond regulation and data security, there is another – relatively unseen – risk, with equally high stakes. Not all businesses are “ready” for AI, and while it can be tempting to rush through with AI deployment, doing so can lead to major financial losses and operational setbacks. Take a data-intensive industry like financial services, for instance. While AI has the potential to supercharge decision-making for operations teams in this sector, it only works if those teams can trust the insights they’re acting on. In a 2024 report, ActiveOps revealed that 98% of financial services leaders cite “significant challenges” when adopting AI for data gathering, analysis, and reporting. Even post-deployment, 9 in 10 still find it difficult to get the insights they need. Without structured governance, clear accountability, and a skilled workforce to interpret AI-driven recommendations, the real “risk” for these businesses is that their AI projects could become more of a liability than an asset. Walking the AI tightrope isn’t about moving fast; it’s about moving smart.
High Stakes, High Risk
AI’s potential to transform business is undeniable, but so too is the cost of getting it wrong. While businesses are eager to harness AI for efficiency, automation, and real-time decision-making, the risks are compounding just as quickly as the opportunities. A misstep in AI governance, a lack of oversight, or an overreliance on AI-generated insights based on inadequate or poorly kept data can result in anything from regulatory fines to AI-driven security breaches, flawed decision-making, and reputational damage. With AI models increasingly making—or at least influencing—critical business decisions, there’s an urgent need for businesses to prioritize data governance before they scale AI initiatives. As McKinsey puts it, businesses will need to adopt an “everything, everywhere, all at once” mindset to ensure that data across the whole enterprise can be used safely and securely before they develop their AI initiatives.
This is arguably one of the biggest risks associated with AI. The promise of automation and efficiency can be seductive, leading companies to pour resources into AI-driven projects before ensuring their data is ready to support them. Many organizations rush to implement AI without first establishing robust data governance, cross-functional collaboration, or internal expertise, ultimately leading to AI models that reinforce existing biases, produce unreliable outputs, and ultimately fail to generate a satisfactory ROI. The reality is that AI is not a “plug and play” solution – it’s a long-term strategic investment that requires planning, structured oversight, and a workforce that understands how to use it effectively.
Establishing a Strong Foundation
According to tightrope walker and business leader, Marty Wolner, the best piece of advice when learning to walk a slackline is to start small: “Don’t try to walk a tightrope across a canyon right away. Start with a low wire and gradually increase the distance and difficulty as you build up your skills and confidence.” He suggests the same is true for business: “Small wins can prepare you for bigger challenges.”
For AI to deliver long-term, sustainable value, these “small wins” are crucial. While many organizations focus on AI’s technological capabilities and getting one step ahead of the competition, the real challenge lies in building the right operational framework to support AI adoption at scale. This requires a three-pronged approach: robust governance, continuous learning, and a commitment to ethical AI development.
Governance: AI cannot function effectively without a structured governance framework to dictate how it is designed, deployed, and monitored. Without governance, AI initiatives risk becoming fragmented, unaccountable, or outright dangerous. Businesses must establish clear policies on data management, decision-making transparency, and system oversight to ensure AI-driven insights can be trusted, explainable, and auditable. Regulators are already tightening expectations around AI governance, with frameworks such as the EU AI Act and evolving US regulations set to hold companies accountable for how AI is used in decision-making. According to Gartner, AI governance platforms will play a pivotal role in enabling businesses to manage their AI systems' legal, ethical, and operational performance, ensuring compliance while maintaining agility. Organizations that fail to put AI governance in place now will likely face significant regulatory, reputational, and financial consequences further down the tightrope.
People: AI is only as effective as the people who use it. While businesses often focus on the technology itself, the workforce’s ability to understand and integrate AI into daily operations is just as critical. Many organizations fall into the trap of assuming AI will automatically improve decision-making, when in reality, employees need to be trained to interpret AI-generated insights and use them effectively. Employees must not only adapt to AI-driven processes but also develop the critical thinking skills required to challenge AI outputs when necessary. Without this, businesses risk over-reliance on AI – allowing flawed models to influence strategic decisions unchecked. Training programs, upskilling initiatives, and cross-functional AI education must become priorities to ensure employees at all levels can collaborate with AI rather than be replaced or sidelined by it.
Ethics: If AI is to be a long-term enabler of business success, it must be rooted in ethical principles. Algorithmic bias, data privacy breaches, and opaque decision-making processes have already eroded trust in AI across some industries. Organizations need to ensure that AI-driven decisions align with legal and regulatory standards, and that customers, employees, and stakeholders can have confidence in AI-powered processes. This means taking proactive steps to eliminate bias, safeguard privacy, and build AI systems that operate transparently. According to The World Bank, “AI governance is about creating equitable opportunities, protecting rights, and – crucially – building trust in the technology.”
Data: Having a single, consolidated data set across an entire operation is vital to ascertaining both a start and end position for AI’s involvement. Knowing where AI is already used, understanding where to deploy AI, and being able to spot opportunities for further AI involvement, are crucial to ongoing success. Data is also the best metric through which to measure the benefits of AI – if businesses don’t understand their “start” position and don’t measure AI’s journey, they cannot demonstrate its benefits. As Galileo once said, “Measure what is measurable, and what is not measurable, make measurable.”
Walking a tightrope is about preparation, calm, and finding balance with every step forward. Businesses that approach AI with measured caution, structured data governance, and a skilled workforce will be the ones who make it across safely, while those who charge ahead without securing their footing risk a costly fall.
The post Walking the AI Tightrope: Why Operations Teams Need to Balance Impact with Risk appeared first on Unite.AI.
