Shutterstock CTO’s AI Scaling Playbook Without Sprawl

In this installment of the IT Leaders Fast-5 — InformationWeek’s column for IT professionals to gain peer insights — Courtney Totten, CTO and CISO at Shutterstock, explains why her team took several months to evaluate AI tools, establish governance models, and create guardrails before deploying those technologies.

Her team has also been deliberate about “training the trainer” to extend AI knowledge throughout the organization. This ensures that AI adoption is not limited to a few experts but spreads across business units effectively.

Totten oversees Shutterstock’s network, cloud operations, security, engineering, and AI infrastructure, and has been in the IT and cybersecurity industries for more than 20 years. She has held leadership roles in both the public and private sectors.

Proactive AI Strategy and Governance Framework

Over the past year, the team made a conscious decision to be proactive with AI rather than reactive. They spent six months evaluating AI tools, building governance models, and establishing clear guardrails.

Once that groundwork was completed, they were able to onboard eight tools within just ten months. This structured approach helped ensure scalability without compromising control or compliance.

Now, the focus has shifted to getting these tools into employees’ hands and gathering real-world use cases. Interestingly, many of the most impactful insights came from business users rather than technologists.

Some tools were already in place but lacked AI functionality. For example, Slack was being used daily, but its AI features were not activated until after thorough evaluation and security checks.

Driving Efficiency with AI-Powered Tools

Simple features like automated notes and summaries turned out to be highly valuable. These small enhancements significantly improved productivity and streamlined daily workflows for employees.

The organization also leveraged ChatGPT to assist teams with repetitive tasks. One notable use case involved creating a Q&A system to handle process-related queries efficiently.

Previously, teams spent a large portion of their day answering repetitive questions. By automating responses using AI, they were able to reduce manual administrative work and focus on higher-value tasks.

This shift not only improved operational efficiency but also boosted employee satisfaction. Teams could now dedicate more time to serving customers and driving innovation.

Overall, these AI-driven improvements demonstrated how even small implementations can lead to meaningful business outcomes.

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Managing Costs and Building AI Talent

One major challenge was managing costs, as cloud and AI expenses began to grow rapidly. Recognizing this early, the organization established a dedicated FinOps and governance team to monitor and optimize spending.

This team works closely with cloud and AI providers to ensure cost efficiency while maintaining performance. They also introduced a quarterly cost-optimization challenge to encourage company-wide participation.

The initiative created a culture of financial discipline, where teams actively look for ways to reduce costs and reallocate resources toward innovation.

In parallel, the company is investing heavily in employee training and skill development. By leveraging partnerships with providers like AWS, Google, and OpenAI, they offer continuous learning opportunities.

The goal is to ensure that AI becomes a core skill across all roles. As AI continues to evolve, building a workforce that is both resourceful and adaptable remains a top priority.