6 hours Ago
Mike Britton is the Chief Information Officer at Abnormal Security , a leading behavioral AI-based email security platform. We’ve finally reached a point in the AI hype cycle where initially inflated expectations are becoming a reality, and if you’re a CIO, your IT team is likely in the mode of shifting from experimentation to implementation . But the rapid advancement of AI technology can feel overwhelming.
It’s moving fast, holds immense potential and is critical to the future success of organizations. That’s why getting AI implementation right is essential—yet the pressure to do so can be paralyzing. For many leaders, it feels like an impossible equation: How can AI be implemented effectively without excessive spending or unnecessary risks? Whether you’re looking at generative AI, agentic AI, multimodal AI, copilots or any of the other AI trends crossing the chasm from hype to practicality, the key lies in taking a strategic, problem-solving approach.
Rather than investing heavily upfront, the best strategy is to start small, focusing on AI solutions that directly address immediate business challenges. Here are some guidelines for getting started: Before evaluating AI tools, start by identifying the most practical use cases across different functions of the organization. Where are teams spending the majority of their time? What steps in this process take up the most time? How complex are these tasks? What could teams achieve if this process was faster? Run through this exercise, and you’re likely to find some familiar scenarios: • Sales operations specialists with deep, niche knowledge about pricing strategies and contract structuring are constantly responding to requests for information.
• Security analysts spend the majority of their day triaging user-reported phishing emails. • The marketing team is responsible for churning out dozens of pieces of collateral each week. • Software engineers are bogged down by writing and reviewing boilerplate code or maintaining documentation.
• The analytics team's been tasked with delivering an endless stream of reporting on business performance. By exposing your bottlenecks and focusing on the processes that are repetitive, time-consuming or prone to errors, you can ensure that AI projects address real business problems and start creating an immediate impact. Embarking on your AI journey doesn't have to mean large-scale transformations from the outset—in fact, it shouldn’t.
Instead, rolling out small-scale pilot programs can allow you the flexibility to experiment, learn and adapt without substantial financial commitments. It’s a good way to assess AI’s potential impacts, build up internal AI experts along the way and create a sound foundation for broader implementation. Additionally, implementing pilots within a controlled testing environment can help minimize risks.
By restricting access to select groups, including stakeholders who already have permission to view the relevant data, you can ensure sensitive data remains protected. Once AI proves its value and you’ve instilled confidence in the technology, you can gradually expand access and train other teams. Clearly defining success is critical to ensure AI investments deliver results.
Businesses need to understand how their AI solutions align with overarching operational goals, such as improving productivity or reducing costs. This requires setting clear metrics and KPIs that can be tracked over time. For example, if the goal is to improve customer service, you might track metrics like response times or customer satisfaction scores.
If financial growth is a priority, you could measure new revenue streams generated by AI solutions or monitor reductions in operational costs through automation. By establishing these benchmarks at the outset, CIOs can create a framework for evaluating the ROI of AI initiatives and ensure that investments are delivering their intended goals. But measuring value doesn’t stop at implementation.
AI isn’t a “set it and forget it” solution. CIOs must look to build a culture of ongoing evaluation. This means regularly reviewing the performance of AI tools, collecting real-time feedback from key users and adjusting as needed.
This not only ensures continued alignment with business goals but also helps organizations adapt as new opportunities emerge. The pressure is on to deploy AI, but CIOs can succeed by taking a strategic, incremental approach. Start small with high-impact use cases, run controlled pilots, and set clear metrics for success.
By focusing on AI solutions that improve efficiency and solve real business problems, you’ll be best positioned to harness AI’s potential without excessive cost or risk, so you can scale initiatives with confidence. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?.
