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Unclear AI applications, lack of trust in employees and required technical skills: Those are just a few reasons financial institutions report turbulent or inexistent implementation of AI in their processes. What’s more, a recent report by Gartner revealed that&nbsp;<a href="https://www.gartner.com/en/finance/trends/achieve-finance-ai-success" rel="noopener noreferrer" target="_blank">70% of financial companies</a>&nbsp;fail to acknowledge the benefits AI can bring to their business. Understanding how technological innovation can transform processes by enhancing their quality, efficiency and accuracy is crucial for driving progress in the financial market. Therefore, in this article, we'll explore the main components of <a href="https://accedia.com/services/software-development-services/artificial-intelligence" rel="noopener noreferrer" target="_blank">implementing AI</a> in financial organizations—people, leadership, processes, technology and organization—and how balancing them correctly can create a solid and successful AI strategy. <h2>Build a skilled AI talent team to embrace innovation</h2> According to a 2023 Deloitte survey,&nbsp;<a href="https://www2.deloitte.com/us/en/pages/finance/articles/cfo-signals-3q-2023.html" rel="noopener noreferrer" target="_blank">63% of surveyed CFOs</a> identified “talent resources and capabilities” as a top barrier to AI adoption. To maximize the transformational power behind the technology, CFOs must not overlook the human factor, and the skills required to manage AI systems, safeguard financial integrity and make well-informed decisions. One way to address this challenge is for organizations to bring AI experts into their teams. In the past year, many of the <a href="https://accedia.com/industries/banking-and-fintech" rel="noopener noreferrer" target="_blank">financial companies</a> Accedia is working with have been adding AI and machine learning specialists to their data science departments. This shift can lead to faster results without the need for extensive training and onboarding. By partnering with AI and ML specialists, organizations can accelerate the development and implementation of AI-solutions, ensuring they stay ahead of emerging technologies and trends, and remain competitive in the market. What’s more, these specialists add valuable expertise in advanced data modeling and algorithm optimization, which helps improve the accuracy and effectiveness of AI-models. This provides more reliable insights that drive smarter decisions and better business outcomes. <h2>Have a clear understanding of AI applications in your financial organization</h2> CFOs must clearly understand how AI can be applied to their specific needs. By identifying and prioritizing the most suitable AI applications, you can align efforts and resources more effectively with business objectives. For example, Morgan Stanley announced the implementation of AI to improve its wealth management services, estimating that this innovation would save financial advisors between&nbsp;<a href="https://www.reuters.com/technology/morgan-stanley-ceo-says-ai-could-save-financial-advisers-10-15-hours-week-2024-06-10/" rel="noopener noreferrer" target="_blank">10 and 15 hours a week</a>. To replicate such success, financial institutions need to conduct comprehensive assessments of their operations to identify areas where AI can deliver the most value. Whether it’s enhancing customer service through chatbots, optimizing risk management with predictive analytics or automating routine financial processes, a targeted approach ensures that AI initiatives are both strategic and impactful. One example from our experience involved a large financial institution that identified fraud detection and prevention as a top priority. The project involved integrating AI systems that could analyze large amounts of data and recognize patterns and anomalies. This consequently resulted in a noticeable reduction in risk-related costs and an increase in customer trust. <a href="https://accedia.com/insights/case-study/transforming-fraud-detection-with-AI" rel="noopener noreferrer" target="_blank">Transforming fraud detection with AI – case study</a> <h2>Build a collaborative environment between people and technology</h2> AI indeed offers powerful capabilities, but the financial industry has unique responsibilities including validating the integrity of financial statements, which cannot be entirely delegated to algorithms. Therefore, implementing AI in finance should be viewed as a collaborator rather than a replacement for human employees. CFOs should ensure that AI-driven processes have complete transparency and that their teams remain fully informed and accountable. This approach will inevitably improve collaboration and increase trust between people and machines, allowing employees to leverage their strengths while AI handles complex, data-intensive tasks. Gaining employee acceptance is also crucial for achieving high returns on AI investments. For teams to embrace AI, they need a clear understanding of how these tools work and how they complement human expertise. When employees start to see AI as a complementary resource, rather than a threat, they are more likely to adopt it effectively. This would allow organizations to take advantage of the full potential of AI while maintaining the integrity of financial operations. <h2>Take a well-calculated risk in AI implementation</h2> When it comes to adopting AI, CFOs often face three main risks—vendor selection, the value of the AI use case and the overall costs of AI implementation. An ideal vendor needs to have proven experience in the finance industry, a deep understanding of regulatory compliance and the ability to customize solutions to meet the unique needs of a financial institution. That’s why it’s important to conduct in-depth due diligence by assessing potential vendors’ past projects, client testimonials, case studies and technical expertise to mitigate the risk of an unsuitable partnership. AI tools, with their ability to <a href="https://www.forbes.com/councils/forbestechcouncil/2024/05/02/choosing-a-software-development-partner-in-2024-six-dos-and-donts/" rel="noopener noreferrer" target="_blank">conduct thorough research and comparative analysis</a>, can help with this process. Another step is to make sure to analyze your business processes and clarify <a href="https://accedia.com/insights/blog/how-ai-development-services-and-tools-can-elevate-your-project" rel="noopener noreferrer" target="_blank">where AI can offer</a> the most significant enhanced decision-making capabilities, efficiency gains or cost savings. Start with smaller pilot projects to validate AI's potential impact before scaling up. Additionally, the overall costs of AI implementation can be a significant concern. Therefore, companies should develop a comprehensive budget that accounts for both the initial investment and ongoing operational expenses. A strategic approach typically includes partnering with an AI software services company that offers flexible engagement models, allowing for scalable and cost-effective implementation and reducing the financial burden on your organization. <a href="https://accedia.com/insights/blog/10-faqs-when-meeting-a-custom-software-development-agency" rel="noopener noreferrer" target="_blank">10 FAQs when meeting a new custom software development agency</a> <h2>Conclusion</h2> AI holds the potential to transform the finance function, enabling more accurate predictions, efficient analysis and automated processes. With the right strategies, financial institutions can harness the power of AI to drive growth, innovation and competitive advantage in an increasingly data-driven world. This article was originally published by&nbsp;<a href="https://www.linkedin.com/in/dimitar-dimitrov-0637028/" rel="noopener noreferrer" target="_blank">Dimitar Dimitrov</a>, Managing Partner at Accedia, as a contribution to <a href="https://www.forbes.com/councils/forbestechcouncil/2024/09/18/crafting-an-ai-strategy-a-blueprint-for-financial-institutions/" rel="noopener noreferrer" target="_blank">the Forbes Technology Council</a>.

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