Why AI matters at this scale

Under the Pavement is a large digital agency and custom software development firm based in Portland, Oregon. With over 10,000 employees, the company operates at an enterprise scale, delivering complex web, mobile, and software solutions for a diverse client base. The company's primary business involves transforming client ideas into functional digital products, requiring significant investments in creative talent, project management, and software engineering.

For an organization of this size in the technology services sector, AI is not a distant trend but an immediate lever for competitive advantage and operational efficiency. The sheer scale of its workforce means that marginal gains in individual productivity compound into massive financial returns. Furthermore, client expectations are rapidly evolving; businesses now seek partners who can integrate AI capabilities into their digital products. Failure to adopt and master AI tools risks ceding ground to more technologically agile competitors and could lead to stagnation in service offerings. For Under the Pavement, AI represents a dual opportunity: to streamline its own high-cost operations and to develop a lucrative new suite of AI-enabled services for its clients.

Concrete AI Opportunities with ROI

1. Augmenting Developer Productivity: Integrating AI coding assistants across all development teams can automate up to 30% of routine coding tasks, such as writing boilerplate code, generating tests, and documenting functions. For a 10,000-person company with a large engineering cohort, this translates to millions of dollars in saved labor costs annually and accelerates project delivery times, directly improving profit margins and client satisfaction.

2. Launching an AI Services Practice: The agency can build a dedicated practice focused on developing custom AI solutions like chatbots, predictive analytics dashboards, and content personalization engines for clients. This creates a new, high-margin revenue stream. By leveraging pre-built models and platforms, the practice can demonstrate quick wins, building case studies that attract further business in a booming market.

3. Intelligent Project Management & Scoping: Machine learning algorithms can analyze thousands of past project artifacts—proposals, timelines, change orders, and budgets—to predict the realistic scope, resources, and potential risks for new proposals. This reduces costly under-bidding and overruns, protecting profitability. It also provides data-driven insights to clients, enhancing trust and positioning the agency as a sophisticated partner.

Deployment Risks for Large Enterprises

Implementing AI at this scale introduces unique challenges. Integration Complexity: Embedding AI tools into established, enterprise-wide workflows for design, development, and client management requires significant change management and technical integration to avoid creating siloed efficiencies. Data Security & Governance: Using generative AI on client projects raises serious concerns about intellectual property and data privacy. The company must establish robust policies, secure AI sandboxes, and train all staff to prevent sensitive client data from being exposed to public AI models. Skill Gap & Cultural Resistance: A workforce of 10,000+ will have varying levels of technical aptitude. Rolling out AI effectively requires a substantial investment in training and addressing cultural resistance from employees who may fear job displacement or added complexity. A top-down mandate without proper support will lead to low adoption and wasted investment.