Home Technology Artificial intelligence The AI Revolution of Fall 2024...
Artificial Intelligence
CIO Bulletin
28 December, 2024
- Peyton Taylor
For Oleksandr Boiko, CTO at Field Complete and Principal Backend Engineer at Plat.ai, the latter half of 2024 represented a decisive shift for corporate IT: AI transformed from an experiment into a working tool backed by budgets and deadlines.
"It wasn't about the algorithms—it was about people," Oleksandr reflects. "People finally started building and expanding businesses using generative AI. The GPT-4o model, in particular, came out remarkably intelligent and powerful, giving businesses the confidence to integrate AI into their core operations."
By the fourth quarter of 2024, AI had become non-optional for U.S. businesses.
Boiko, who leads technology strategy at Field Complete—an all-in-one field service SaaS platform serving everyone from individual handymen to large construction enterprises—points to specific catalysts that drove this transformation.
"It's not about the dates," he explains. "The releases of GPT-4o, o3, and Claude Sonnet 3.5 removed the main barrier. With them, large language models stopped being demos and became part of the production pipeline. These releases allowed companies to seriously consider applying AI in corporate environments. By year's end, we saw substantial positive feedback from both large corporations and smaller players, proving the technology was ready for enterprise use."
AI's quiet integration into everyday tools set the stage for leaders like Oleksandr to apply it inside enterprise systems. Compact models capable of running on smartphones without internet connectivity opened possibilities for offline operation and enhanced privacy. These developments made AI accessible not just to Fortune 500 enterprises but to local businesses as well.
For Oleksandr's distributed teams at Field Complete, spanning three continents, the transformation brought measurable impact across multiple dimensions.
"Code writing speed increased by at least 20%," he reports. "But more importantly, code quality improved significantly because AI took on many tasks that are tedious for developers but critical for product quality—things like testing and documentation."
The changes extended beyond engineering. "Working through hypotheses reached an entirely different level," Oleksandr continues. "Tasks that previously required weeks now take hours. Legal research questions also save significant money in the United States, leaving attorneys only the work that genuinely requires human judgment."
Boiko identifies three critical challenges his teams faced during the adoption process:
"First, we had to start trusting the models' responses enough that it actually accelerated work rather than slowing it down," he explains. "Second was the retraining or additional training of people. It turned out that knowing how to ask the model correctly is also a skill that needs to be practiced."
The third challenge revealed a crucial philosophical shift: "At this stage, still awaiting AGI, we didn't see AI as a threat to human professions in our company. Instead, we viewed it as a copilot that closes many gaps in knowledge, processes, and speed of human work on tasks."
This perspective—AI as augmentation rather than replacement—defined successful implementations across the organization.
For a technology leader with Oleksandr's background—having risen from early engineering roles to Head of Engineering and CTO positions while consistently focusing on aligning technology with business growth and long-term scalability—the transformation represented both challenge and opportunity.
"We recognize the importance of attracting and growing top talent while developing resilient teams," Oleksandr notes, reflecting on his experience managing distributed engineering organizations. "The AI transformation required us to guide our teams through rapid change while maintaining focus on building platforms that solve complex problems at scale."
At Field Complete, Boiko's team didn't just adopt AI—they pioneered innovative applications within their field service management platform. The company, which has secured two rounds of investment from venture capital funds and private investors, developed solutions that fundamentally changed how contractors operate.
"Our product's functionality covers all necessary processes for contractors from the handyman level to property owners," he explains. "This allows clients to have solutions for all their tasks in one application. There are no similar mass-market solutions. Many software options cover some percentages, but our solution significantly speeds up order processing and reduces the financial burden on clients."
Within Field Complete, the team developed Job Sharing—an innovative process where contractors can close related orders they don't specialize in through other companies.
"Job Sharing removed the main friction for the customer—multiple invoices and searching for contractors for every small task," Oleksandr explains. "One contractor, one checkout; work distribution happens under the hood. They can work with just one contractor instead of searching for separate people for each problem. That contractor can subcontract work to other companies, receiving a share of revenue."
Currently, Job Sharing operates through trusted, first-degree networks—contractors refer work to companies they personally know. The next release will open this as a marketplace, giving customers access to a broader service bench without multiplying vendors or invoices.
Under the hood, this represents more than a feature—it's a business model shift. Field Complete treats service capacity as a shared network asset rather than isolated silos, which is why the economics work at both handyman and multi-property scales.
When asked about AI development in the United States over the coming years, Oleksandr's perspective reflects the industry's measured optimism.
"Everyone was waiting for AGI, understanding that even without it, many jobs would be replaced by AI," he observes. "There had never been such a boom in history, and it's unlikely to be short-lived because many companies need to rethink their processes and find places where AI can specifically improve them. Competitors will be doing the same."
As both CTO at Field Complete and Principal Backend Engineer at Plat.ai, Boiko combines hands-on technical expertise with strategic leadership. For him, the events of late 2024 marked a fundamental shift in how technology leaders approach AI.
Oleksandr's dual role offers a revealing contrast in AI implementation. At Plat.ai—a U.S.-based loan origination platform handling every lending process except funding—the team has used specialized AI models for borrower evaluation and risk assessment for years, but approached LLMs differently.
"When we integrated large language models, we did so with constraints that wouldn't make sense in other industries," he explains. "In lending, you can't ship fast and fix later. But both industries benefit from AI—just with different guardrails."
"For us, this is no longer a trend," he observes. "It's infrastructure. We measure the benefits of AI the same way we measure uptime or release costs. And while competitors are looking for the 'right moment,' we just ship."
As a juror for UAtech Venture Night at Web Summit Vancouver, Oleksandr evaluates not the appeal of the pitch but the discipline of execution: whether a team can take an idea to customer metrics within a quarter. This role recognizes his expertise in evaluating innovation and long-term impact in Ukrainian-founded startups, bridging the worlds of established enterprise technology and emerging innovation.
The competitive advantage window is narrowing. While many organizations continue evaluating AI adoption strategies, companies like Field Complete are already measuring AI performance alongside core infrastructure metrics. For technology leaders who navigated this transition with strategic foresight, the transformation has opened capabilities that seemed theoretical just months earlier.
Oleksandr Boiko serves as CTO at Field Complete (USA) and Principal Backend Engineer at Plat.ai, bringing over 18 years of experience building products for multi-million user bases across SaaS and Fintech. His career journey spans from early engineering roles to senior leadership positions, including Head of Engineering and CTO, with a consistent focus on aligning technology with business growth and long-term scalability. He brings proven expertise in managing globally distributed teams across three continents, designing scalable architectures, and fostering high-performing engineering cultures.
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