Beyond the Hype: The Finance Leader’s Blueprint for Profitable AI

By Jeremy Ung, Chief Technology Officer at BlackLine

AI’s promise of helping to get more work done is very intriguing for finance companies, as many teams are budget constrained and spend countless hours on manual, time-consuming tasks. This promise has impacted every industry with companies like Microsoft, Google, and Meta pouring billions more into their AI infrastructure. 

Despite these massive investments, there are remaining concerns from enterprises about the return on investment (ROI) with AI deployments. MIT’s recent GenAI Divide: State of AI in Business report found that despite $30-40 billion in enterprise investment, 95% of organizations are seeing zero return on their AI initiatives. As a result of this challenge, Forrester predicts the AI bubble will burst by 2026, with enterprises delaying 25% of planned AI spend into 2027 because they’re not receiving the expected returns.

Yet, enterprises are still eager to augment their workforce, but it’s crucial for them to address this ROI obstacle and accelerate value to AI. Luckily, for finance leaders, profitable AI strategies are in reach, if investments are carefully carried out.

Risks of an AI Sprawl

With the competitive and market pressures to adopt AI, many enterprises have been treating it like the SaaS explosion, investing in many different AI tools all at once. With new AI tools emerging every week, it becomes easy for an AI sprawl to happen. This situation limits enterprises from receiving the full value of their AI tools. Some investments may be actually duplicating efforts and present an unnecessary cost. 

With overlapping investments, it’s common for AI initiatives to become fragmented. If an enterprise’s AI strategy is not standardized or consistent across the entire company, this can have a direct impact on the AI outputs. A lack of oversight can increase the risk of untrustworthy data being introduced into company AI deployments without strict governance, which can create significant legal and financial problems, especially for finance companies.

Remaining Blind to AI Outputs

If companies don’t have a unified strategy and a single source of truth for data, they remain blind and can’t verify AI outputs. This can put organizations at risk for regulatory and compliance fines. When it comes to finance and accounting, the tolerance for inaccuracy is zero. It’s critical to apply AI to these domains in a trustworthy manner or organizations face reputational damage and financial losses.

Beyond the governance challenge, when companies do a complete AI overhaul, it can become difficult to upskill their workforce on each new tool. The full value of AI processes cannot be realized if financial leaders simply “throw” the technology at their teams without properly teaching them how to leverage its opportunities and manage its inherent risks.

Building Trust with AI Workflows

Finance and accounting professionals can start to receive the value from AI when they are able to trust AI’s outputs. 

Transparent AI models that provide a clear, auditable, explainable chain of thought allow finance and accounting teams to feel comfortable that AI is not feeding them inaccurate information and saves teams from having to spend time correcting errors.

It’s also critical for financial leaders to have financial discipline with their AI investments. Instead of consistently adopting AI capabilities, finance leaders need to evaluate which tools make sense and will bring the most value. These AI investments must also be kept flexible so that organizations can pivot as the AI landscape changes.

Turning AI into an Asset

To get the maximum value of AI investments and be on the path of profitability with these tools, organizations should follow these key strategies: 

• Phase AI Integrations: By slowly introducing AI tools, organizations can build confidence among teams and allow them to master the technology. Phasing integrations in lower-risk areas gives accounting and finance teams an opportunity to review AI outputs and assess its accuracy without introducing great risk. This approach allows for smaller, targeted investments, and preserves capital for successful integrations. 
• Trusted and Transparent Data: AI deployments need to be built on an uncompromising foundation of data integrity. In finance, accuracy is paramount, so the data and the way the automation is carried out must be reliable and based on trusted information. 
• Establish a Control Layer: By creating a unified, standardized governance for AI deployments across the company and implementing explainable AI (XAI) that shows its reasoning, finance leaders can fully trace and audit every AI-driven action, decision, or generated output. This not only helps avoid the risk of regulatory fines and reputational damage; but it also helps organizations reduce the risk of inconsistent results, which ultimately accelerates the time to value. 
• AI-Fluent Teams: Organizations need to place a bigger focus on upskilling their finance teams to understand the new technology and how it will impact the business and workflows. With this knowledge, every team member can accept or reject AI-powered outputs, validating AI’s logic. However, this process must be continuous as AI evolves, to ensure that teams’ AI fluency doesn’t become outdated.

From AI Sprawl to Strategic ROI

As companies plan their AI strategies for next year, it is possible to make wise technology investments and ensure every dollar spent on AI delivers measurable value and ROI.  

By following these strategies, organizations can help to avoid wasted spend on unreliable, duplicative, and fragmented AI efforts. By coordinating AI tools across the organization, finance leaders can avoid an AI sprawl and ensure that capital is only being spent on tools that support the overall business mission and can adapt as AI continues to evolve.

Companies can’t afford to not adopt AI with massive brands pouring so much investment into advanced technology. However, finance leaders can address their concerns by focusing on trust, auditability, and value delivery of AI, rather than just adoption volume. This can help organizations link AI projects to tangible business outcomes, helping to make an organizations’ AI strategy profitable. The companies that are able to do this successfully will be the ones that move beyond the AI divide, building long-term sustainability that will help them to maintain a competitive edge and flexibility as the financial industry continues to change.