AI Ready: Turning Strategy Into Action
Scott Harvey
Distilling Complexity into Game-Changing Solutions | Fintech & Banking | Strategic Leadership | Product & Market Strategy | Enterprise Transformation | M&A | P&L Management | Growth & Innovation | Advisory & Consulting
AI adoption in banking is booming—but are your AI initiatives actually delivering results?
Despite billions invested in AI, as few as 15% of AI projects generate meaningful ROI. The difference between success and failure? Alignment with business goals.
Before investing in AI, ask yourself: Are you solving a real business problem, or just chasing the latest technology trend?
In this article, we explore how to ensure AI projects are strategically integrated into your organization’s mission and objectives.
Why Alignment Matters
When AI is implemented without clear alignment to business goals, the risks include wasted resources, poor user adoption, and underwhelming outcomes. However, when AI is purposefully tied to your organization’s objectives, it becomes a powerful driver of:
Step 1: Define Your Business Goals
Ask yourself: What’s costing your bank the most money right now? High fraud rates? Slow loan approvals? Customer churn?
Example: AI-powered fraud detection has helped banks cut fraud losses by 30%, while AI-driven underwriting has reduced approval times by 40%, directly boosting customer retention.
To ensure success, keep your goals SMART (Specific, Measurable, Achievable, Relevant, and Time-bound). For instance, “Improve loan approval time by 30% within six months” is a SMART goal AI can support.
Step 2: Identify the Right Use Cases
Once you’ve defined your goals, identify the AI use cases that align with them. Here are some examples:
Step 3: Involve Key Stakeholders Early
AI adoption is not just a technology initiative; it’s a business transformation. To ensure success:
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Step 4: Establish Metrics for Success
How will you know if your AI initiatives are working? Define key performance indicators (KPIs) that reflect your business goals. Examples include:
Use these metrics to track progress and adjust strategies as needed.
Real-World Example: How AI Transformed a Regional Bank’s Loan Process
Case Study: A Mid-Sized Regional Bank’s AI Success
A mid-sized regional bank was losing customers because loan approvals took 5-7 business days, far too slow in today’s digital banking world. Borrowers were taking their business elsewhere.
By implementing an AI-powered underwriting model, they: ? Reduced loan approval times by 40% (from 5-7 days to 48 hours) ? Improved customer satisfaction scores by 25% ? Increased internal efficiency, allowing staff to focus on higher-value tasks instead of manual approvals
The takeaway? AI must solve a business problem, not just be a cool tech experiment.
Why Now is the Time to Act
AI adoption in banking is accelerating, and aligning AI with your business goals is the foundation for success. By taking a strategic approach, you can ensure that AI delivers measurable value while positioning your bank as a leader in innovation.
Call to Action
Is your bank truly AI-ready? Let’s make sure your AI strategy delivers measurable business value.
Let’s talk! Reach out to explore how AI can reduce risk, drive efficiency, and boost customer retention.
Partner @ Sole Consulting: We drive strategic change for leading companies
2 周Most big companies have existing governance and stage gate mechanisms to determine if a project should be funded and proceed. It seems like some of that is being suspended in the rush to try out AI. Probably ok for small scale experimentation. Not so great for a larger rollout. You've called out some good practices on what companies can do to ensure a positive outcome.
CEO @ Domus Lingua | Translation, Management
2 周Love this