Artificial intelligence is developing at such a dramatic pace that any step forward is a step into unknowns. The chances are great, but the risks can be said to be greater. While AI promises will revolutionize the industry – from the routine tasks of automation to providing in-depth insights through data analytics – it also gives way to ethical dilemmas, biases, data privacy issues, and even negative return on investment (ROI) (ROI) if not implemented correctly.
Analysts are already predicting the future of AI (at least in part) that will be affected by risks.
According to Gartner’s 2025 report Riding an AI whirlwindAs technology develops, our relationship with AI will change and form this risk. For example, the report predicts that businesses will begin including emotional and emotional protections under their terms and conditions, and the health care sector is expected to begin these updates within the next two years. The report also shows that by 2028, more than a quarter of all enterprise data breaches will be traced back to some kind of AI agent abuse, whether it is an internal threat or an external malicious actor.
In addition to regulations and data security, there is another relatively unknown risk, which poses the same high risk. Not all businesses are “prepared” for AI, and while they may be eager to pass through AI deployment, doing so can lead to significant financial losses and operational setbacks. Take data-intensive industries such as financial services as examples. While AI has the potential to boost decisions for operational teams in the industry, it only works if these teams can trust the insights they take. In a 2024 report, ActiveOps revealed that 98% of financial services leaders cited “significant challenges” when adopting AI for data collection, analysis and reporting. Even after development, nine out of 10 people still find it difficult to get the insights they need. Without structured governance, clear accountability and skilled workforce to explain AI-driven recommendations, the real “risk” for these businesses is that their AI projects may be more important than assets. Walking an AI rope is not about moving quickly; it’s about moving smartly.
High risk, high risk
The undeniable potential of AI to transform its business is undeniable, but so is the cost of making mistakes. Although businesses are eager to leverage AI to improve efficiency, automation, and real-time decision-making, risks are as complex as opportunities. Blunders based on AI governance, lack of supervision or excessive reliance on insights based on AI-generated, data based on insufficient or saved data can lead to anything from regulatory fines to AI-driven security violations, flawed decision making and reputation damage. As AI models increasingly formulate or at least affect critical business decisions, it is urgent to companies prioritize data governance before expanding their AI initiatives. As McKinsey said, businesses will need to adopt a “one, anywhere, all, all” mindset to ensure data across the enterprise can be used safely and securely before they can develop an AI plan.
It can be said that this is one of the biggest risks associated with AI. The promise of automation and efficiency can be tempting, leading companies to pour resources into AI-powered projects before making sure their data is ready to support their data. Many organizations are eager to implement AI without first building strong data governance, cross-functional collaboration, or internal expertise, which ultimately leads to AI models, which enhance existing biases, produce unreliable outputs and ultimately fail to produce satisfactory ROIs. The reality is that AI is not a “plug” solution – it’s a long-term strategic investment that requires planning, structured oversight and a workforce that understands how to use it effectively.
Build a strong foundation
According to Tightrope Walker and business leader Marty Wolner, the best advice when learning to walk is to start small: “Don’t try to cross the canyon immediately. Start at the low line and gradually increase distance and difficulties as you build skills and confidence.” He thinks the same is true for the business: “Small wins can prepare you for your bigger challenges.”
These “small wins” are crucial for AI to provide long-term sustainable value. While many organizations focus on AI’s technological capabilities and are ahead of the competition, the real challenge is to establish the right operating framework to support AI adoption at scale. This requires a three-pronged approach: strong governance, continuous learning and commitment to the development of ethical AI.
Governance: Without a structured governance framework, AI will not be able to function effectively to determine how it is designed, deployed, and monitored. Without governance, AI initiatives could be dispersed, irresponsible or totally dangerous. Enterprises must develop clear policies in data management, decision transparency and system oversight to ensure AI-driven insights can be trusted, explained and reviewed. Regulators are already intensifying expectations for AI governance, with frameworks such as the EU AI Act and the evolving U.S. regulations that will put companies accountable for the use of AI in decision-making. According to Gartner, the AI governance platform will play a key role in enabling businesses to manage their legal, ethical and operational performance, thereby ensuring compliance while maintaining agility. Organizations that are now failing to engage in AI governance may now face significant regulatory, reputational and financial consequences.
people: Artificial intelligence is as effective as those who use it. Although businesses are often focused on the technology itself, the ability of the workforce to understand and integrate AI into daily operations is equally critical. Many organizations fall into the trap of assuming that AI will automatically improve decisions, when in reality, employees need to be trained to interpret the insights generated by AI and use them effectively. Not only must employees adapt to AI-driven processes, but they must also develop critical thinking skills that are needed to challenge AI output when necessary. Without this, businesses may be overly dependent on AI – allowing flawed models to affect unchecked strategic decisions. Training programs, high-skill programs and cross-functional AI education must be priorities to ensure that employees at all levels can work with AI, rather than replace or replace it.
ethics: If AI is a long-term enabler of business success, it must be rooted in ethical principles. Algorithm bias, data privacy vulnerabilities and opaque decision-making processes have eroded trust in AI in some industries. Organizations need to ensure that AI-driven decisions are aligned with legal and regulatory standards, and that customers, employees, and stakeholders can be confident in AI-driven processes. This means taking positive steps to eliminate bias, protect privacy and build transparent operational AI systems. According to the World Bank, “AI governance is about creating fair opportunities, protecting rights, and crucially – building trust in technology.”
data: Having a combined dataset throughout the operation is critical to determining the start and end points of AI participation. Understanding where AI is already using, knowing where to deploy AI, and being able to identify opportunities for further AI engagement is critical to sustained success. Data is also the best measure of AI’s benefits – if a company does not understand its “start” position and does not measure the AI’s journey, they will not be able to prove its benefits. As Galileo once said: “Measure measurable and unmeasurable methods to make them measurable.”
Walking ropes are about preparing, calming and balancing with every step forward. With measurement prudence, structured data governance and skilled workforce to AI businesses will be safe businesses, and those who pay the price forward without ensuring their underlying risks will be expensive to fall.
