Small and mid-sized organizations are discovering that cloud-based data strategies unlock AI capabilities without massive upfront infrastructure costs. By consolidating fragmented data into centralized repositories on platforms like Microsoft Azure and Amazon Web Services (AWS), companies can build custom generative AI applications that automate routine tasks and accelerate business decisions. The approach is proving transformative for real estate firms, nonprofits, and other organizations that previously lacked the resources to pursue AI initiatives .

How Are Companies Building Custom AI Tools on the Cloud?

The path forward involves three core steps that organizations are following to implement cloud-based AI successfully.

• Data Consolidation: Companies hire third-party specialists to classify and organize data scattered across dozens of applications, then build a centralized data lake on cloud platforms to serve as a single source of truth for analytics and AI.
• Iterative Implementation: Rather than attempting to prepare all data at once, successful organizations focus on high-priority business needs first, building early wins to secure buy-in from skeptical staff members.
• Multi-Cloud Architecture: Organizations often use multiple cloud providers in tandem, pulling raw data into one platform for storage and transformation, then moving cleaned data to another platform for analytics and AI applications.

New York-based real estate firm Fisher Brothers exemplifies this approach. In 2024, the company's technology leadership decided to prioritize AI and data analytics after recognizing the potential to transform operations. E.M. Hinchey Jr., vice president and head of technology, initially attempted to classify and optimize all 176 applications' data at once, but the project stalled after eight months. "We tried to boil the ocean," he recalled. "It was too much." The team also faced resistance from staff who didn't see the project's value .

Hinchey pivoted to a department-by-department approach, focusing first on high-priority business needs like loan management and facility operations. Fisher Brothers now uses Microsoft Power BI reports to analyze loans, deal flow, and treasury operations. The company also built Fisher GPT, a private generative AI chatbot running on Azure that answers questions about loans and cleaning operations. When a partner asked the finance team whether to convert a multifamily residential building into condos, Hinchey used commercial AI tools like ChatGPT and Perplexity to produce a comprehensive report in five minutes. "When I put it in front of the financial team, they were like, 'Wow! This would have taken me three months to figure out. And the data in here is accurate,'" he said .

What Makes Cloud-Based AI Accessible to Smaller Organizations?

Cloud platforms offer integrated services that eliminate the need for expensive on-premises infrastructure investments. According to IDC analyst Ashish Nadkarni, public cloud services such as Azure, AWS, and Google Cloud Platform provide mature, scalable, and secure tools that make AI adoption seamless for smaller organizations .

"With a suitable data strategy, they can leverage it for both the analytics side, which is more traditional, and the generative AI side, which is more future-oriented," said Ashish Nadkarni, IDC analyst.

Ashish Nadkarni, IDC analyst

These integrated services include data cleaning and warehousing, analytics tools, and AI development platforms. Organizations pay for cloud services as operational expenses rather than making major capital investments in servers and infrastructure. When implemented effectively, this approach improves productivity and delivers strong return on investment while reducing costs .

Fair Trade USA, an Oakland, California-based nonprofit that certifies ethically and sustainably sourced products, demonstrates how smaller organizations leverage cloud infrastructure. The organization turned to AWS to consolidate, clean, and manage data from coffee producers in Ethiopia, fish suppliers in Indonesia, and other global partners. The goal was to operate more efficiently so staff could spend less time on manual administrative work and more time on the core mission of connecting corporate buyers with producers while ensuring fair pay and safe working conditions .

Fair Trade USA built its Insights Hub in 2024, a self-service analytics platform using AWS Redshift for data warehousing, Amazon S3 for storage, and AWS Glue for data pipelines that clean and transform data. AWS Translate converts multilingual information into English. The organization started simple, bringing in customer relationship management data, then self-reported data from partners and producers, then audit data. "We started simple, bringing in customer relationship management data, then self-reported data from partners and producers, then audit data," explained Olena Gomozova, director of engineering and data analytics. "We're adding data sources like Lego blocks" .

"We're working to unify and standardize data collection and create a single source of truth for business intelligence. This enables our business leaders to make decisions and understand the impact of the program," said Olena Gomozova, director of engineering and data analytics at Fair Trade USA.

Olena Gomozova, Director of Engineering and Data Analytics, Fair Trade USA

Fair Trade USA implemented coffee as its first product category in 2025. Staff and partners can now access insights including volumes of Fair Trade products purchased, compliance data, and community development investments in educational, environmental, health, and safety projects. Partners can also view supply chain maps to trace where their ingredients originated .

Why Is the Cloud Strategy Gaining Momentum Now?

The convergence of three factors is driving adoption. First, generative AI tools like ChatGPT and custom large language models (LLMs), which are AI systems trained on vast amounts of text data, have become accessible to organizations of all sizes. Second, cloud platforms have matured to offer end-to-end solutions for data management and AI development. Third, organizations recognize that AI can dramatically accelerate decision-making and automate routine tasks, creating competitive advantages .

At Fisher Brothers, initial skepticism about AI has given way to enthusiasm across the company. The organization is preparing additional data sources in its data warehouse beyond the initial set of loan and cleaning operations data. In the future, Fisher GPT, built using OpenAI's GPT-5 model, will analyze previously siloed information and quickly provide answers that used to take several months to obtain manually, such as each partner's expected distributions for the year. "It will allow us to ask better questions about our data and just get much faster answers," Hinchey noted .

For organizations considering similar initiatives, the lesson is clear: cloud-based data strategies are no longer the domain of tech giants. By focusing on high-priority business needs, building early wins, and leveraging integrated cloud services, smaller organizations can implement AI capabilities that rival those of much larger competitors.