Financial Institutions Preparing for AI Advancements Through 5 Strategies
In the realm of financial services, it's no secret that age-old operating models often fail to foster collaboration between business and tech teams. While tradition has its charms, it often comes at a cost - the inability to swiftly adapt and harness transformative technologies like Generative AI (GenAI).
These once-accepted silos are now proving to be expensive and a significant hurdle. Misaligned priorities, slow decision-making, and a stifled innovation environment are the price we pay for not aligning our business and tech teams.
A recent McKinsey report suggests that GenAI could potentially add between $200 billion and $340 billion in value annually for the global financial services sector. It highlights how GenAI can revolutionize customer-facing chatbots, prevent fraud, and expedite time-consuming tasks such as code development and report summarization. Why let something as simplistic as a lack of collaboration obstruct such potential?
Here are five observations that justify a rethink to embrace AI:
- Traditional financial services organizations typically have business units focusing on strategic goals and tech teams managing infrastructure. Misaligned priorities and slow decision-making stem from this division, often hindering GenAI implementation and its full potential benefit.
- Cultivating a collaborative culture where business and tech teams work together from the outset is essential for transformational success. Establishing cross-functional teams to co-create AI strategies ensures both business needs and tech capabilities are aligned.
- Cloud-based systems, designed with embedded AI, are paving the way forward. Leveraging existing core systems' AI capabilities is the first step in preparing the ground. Modern ERP & HR platforms, for instance, embed AI across their systems and applications, making it easier to analyze data from various departments for enhanced insights, better-informed decisions, and trend forecasting.
- Personalized dashboards showcasing relevant metrics tailored to roles are the antithesis of silos. AI can analyze user behavior and adapt the system to suit individual workflows, enhancing overall effectiveness.
- AI thrives on data, but its effectiveness can be significantly impacted if this data is siloed or managed inconsistently. Eliminating silos, building on agile infrastructure, and embedding AI into core systems enables data connectivity across departments, providing the necessary information required to evolve at an appropriate pace, providing comprehensive oversight and insight into the business.
Addressing these challenges can have a far-reaching impact on people and processes within financial services organizations. The first step is to prepare the ground, break down silos, and empower cross-functional collaboration with modern operating models. Emphasizing AI literacy and fostering a culture of experimentation and innovation is crucial for evolution and growth.
- Misaligned priorities between business and tech teams in traditional financial services organizations often lead to slow decision-making, which can hinder the full potential benefit of Generative AI, such as GenAI from McKinsey's suggested additions of $200 billion to $340 billion annually.
- To fully leverage the capabilities of Generative AI and other transformative technologies, a collaborative culture needs to be cultivated, where business and tech teams work together from the outset, establishing cross-functional teams to co-create AI strategies.
- Cloud-based systems with embedded AI, like modern ERP & HR platforms, can help prepare the ground for GenAI implementation, enabling data analysis across departments, making it easier to make better-informed decisions and predict future trends through trend forecasting.
- Personalized dashboards tailored to roles, using AI to analyze user behavior, can enhance overall efficiency within the financial services sector by adapting systems to individual workflows and eliminating silos.
- Siloed or inconsistently managed data can significantly impact the effectiveness of Generative AI. To get the most out of AI, it's essential to eliminate silos, build an agile infrastructure, and embed AI into core systems, enabling data connectivity across departments, providing comprehensive oversight and insight into the business.
