Ethan Pham is Founder and CEO of XNOR Group, a global technology consulting firm specializing in AI-driven digital transformation.
At this point, 88% of organizations have used AI in at least one business function, according to McKinsey. Yet, I still hear CEOs say, “We’ll adopt AI once the market stabilizes.” The truth is, that moment rarely comes. In fast-moving markets, waiting for perfect conditions often means making decisions with outdated information and losing opportunities that may never return.
As founder and CEO of a tech consulting firm that specializes in the finance space, I have observed a widening gap between organizations that operate with real-time AI-powered financial insight and those that still rely on traditional reporting cycles. The difference is not budget or data quality; it is decision velocity. I often tell my clients, “The fastest insight wins. Every day a CEO waits, a competitor may act first.” Companies that move faster can capture opportunities, while those that wait risk falling behind.
Why AI-Driven Decision Speed Matters In Finance
Modern AI in finance does more than automate spreadsheets. It can model market scenarios, detect early risk signals and surface actionable insights quickly. I once advised a Fortune 500 CEO during a sudden market swing. His team promised a detailed report by week’s end. Meanwhile, a competitor using AI-generated analysis produced actionable scenarios in under 30 minutes and acted immediately. That timing difference created a multimillion-dollar advantage.
This is no longer an exception. Boston Consulting Group reported that organizations leading in AI adoption expect roughly double the revenue growth of late adopters. In finance, top-performing teams can achieve productivity gains of around 20%, while banks are aiming to cut costs by up to 50% in areas like know your customer and compliance.
Enhancing Leadership, Not Replacing Judgment
Many executives fear that AI will erode human authority. In reality, I believe it exposes the quality of leadership. In my experience, the most effective organizations adopt hybrid models. This means AI handles data processing, pattern recognition and scenario modeling, while humans provide context, ethics, negotiation and accountability.
I often tell my clients, “AI does not remove judgment. It reveals whether judgment was ever applied consistently.” AI can tell you what is happening and what might happen next. Leaders must decide what should be done, especially when trust, reputation and long-term strategy are on the line. CEOs who master this balance can position themselves to outperform those who treat AI as a shortcut or a replacement for human insight.
Real Risks Of AI In Finance Every CEO Must Address
Despite its potential, AI introduces real challenges:
• Fragmented systems and siloed data slow insight.
• Security and vendor risk rise when relying on third-party models.
• Teams often lack the skills to interpret outputs.
• Bias and transparency issues can lead to regulatory violations.
• Evolving laws can quickly make processes non-compliant.
These are not hypothetical. Research found that AI used in mortgage assessments disproportionately rejected Black applicants compared to white applicants with identical loan applications, violating fair lending laws. Failures like this can create enormous financial and reputational risk.
Adopting AI without governance is not innovation; it is unmanaged risk.
Building Trustworthy AI
Turning AI into a strategic asset requires three commitments:
• Modernizing Decision Infrastructure: AI only works reliably on unified systems. Leaders should ensure platforms provide consistent data access, monitoring and policy enforcement to make decisions with confidence.
• Embedding Security Without Slowing Execution: Encryption, anonymization and human oversight reduce exposure while allowing fast deployment. Safety and speed should reinforce each other.
• Developing Human Capability: Trust grows when leaders and teams understand not just what AI can do, but also what it cannot. For instance, at my company, every employee, even non-technical roles, works with AI weekly using anonymized data. Teams interpret outputs and make final decisions as if the work were real. I’ve found this helps adoption rise, compliance risks drop, and the workforce becomes AI-native while preserving accountability.
Throughout this process, remember that governance and validation are essential. Deploy multilayered reviews where AI systems assess risk, compliance, fairness and fraud in parallel, while humans retain authority over high-impact decisions. AI enhances judgment; it should not replace it.
How To Integrate AI In Finance: A Roadmap
Across industries, I have seen a four-step approach consistently succeed when it comes to actually integrating AI into finance functions. The following roadmap aligns with how leaders make real decisions:
1. Start with high-frequency decisions. AI learns fastest where decisions repeat often and feedback is immediate, such as pricing adjustments, liquidity forecasts and currency hedging. High-frequency decisions can build trust without risking the enterprise.
2. Establish governance early. Governance is not a roadblock; it protects authority. Leaders should define which decisions AI can execute, where human review is required and who is accountable for outcomes.
3. Train leaders to interpret insights. The key skill is knowing when to trust AI and when to challenge it. This judgment separates effective leadership from blind automation.
4. Measure outcomes continuously. The ultimate metric is decision quality: speed, consistency and risk detection, not model accuracy. Continuous measurement allows insight to compound into a competitive advantage.
Looking Ahead
AI-powered financial intelligence is no longer reserved for global banks. Mid-sized manufacturers can simulate supply chain risk, retailers can optimize currency exposure and smaller teams can wield analytical power once reserved for the largest institutions.
Over the next decade, I believe the winners will not be those who automate the most but those who integrate human expertise with machine intelligence most effectively. AI processes data, models scenarios and forecasts outcomes. Humans provide context, ethical judgment, negotiation and strategic direction. Competitive advantage depends on how well the two collaborate.
AI in finance is not a technology upgrade; it is a leadership transformation. Executives who adopt it thoughtfully, and with careful consideration for the risks, can make faster decisions while maintaining accountability and trust. Those who delay risk competing at yesterday’s speed in a world that now moves at the pace of data.
The question for CEOs is no longer whether to use AI, but whether they will lead with it or be led by those who do.
Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?