Top five AI trends for 2025

Until now, data has largely been understood as “information humans need to make a decision.” For AI to take over some—or all—of that decision-making, this information must be contextually distilled into training data. This is why, in 2025, poor data quality will lead to 30% of generative AI projects failing post-proof of concept.

Even though large language models (LLMs) are pre-trained on vast amounts of data, they often fall short in specialized, domain-specific use cases unless fine-tuned with targeted business data. To ensure data readiness, businesses must address these critical questions:

• Is your data gatherable? Can it be systematically collected and organized?
• Is it reliable? Is it accurate, consistent, and trustworthy?
• Is it indexable? Can it be efficiently searched and accessed?
• Is it durable? Is it properly maintained for long-term usability?

Legacy systems weren’t designed for interoperability, resulting in siloed structured and unstructured data that complicates AI integration. Each system stores data in unique formats, requiring extensive cleaning and transformation for consistency and compatibility. Beyond data quality, AI regulations like the EU AI Act (2025) are driving stricter AI and data governance.

Recognizing these challenges, organizations are shifting focus from model optimization to data readiness.

AI itself plays a pivotal role in preparing quality data by standardizing, enriching, and correcting information to ensure consistent quality. AI-powered data analysis unlocks siloed or unstructured data by reorganizing it into accessible formats, enabling seamless integration and usability across platforms. Once data readiness is achieved, retrieval-augmented generation enhances AI’s ability to access domain-specific data in real-time, reducing hallucinations and improving decision-making. Yieldmo, an advertising technology company, has successfully deployed an innovative machine learning platform built on a highly efficient data infrastructure, enabling it to match users, publishers, and advertisers while optimizing campaign delivery and performance.

As generative AI struggles to meet immediate business expectations, organizations will shift focus back to predictive AI—a tried and true approach delivering tangible ROI. This shift is not necessarily a “rolling back” of generative AI but rather a natural progression as the hype cycle subsides, and organizations recognize that generative AI is not a silver bullet for complex computational problems where accuracy and speed are critical.

Industries like retail, manufacturing, and CPG rely on predictive AI to process vast datasets—SKU x channel x location combinations—while adjusting dynamically to external market fluctuations.

Predictive AI helps with demand sensing and forecasting by processing vast amounts of supply chain and market data, enabling:

• Accurate procurement decisions to optimize sourcing and reduce waste
• Optimized transportation planning for efficient logistics and cost savings
• Improved inventory management to prevent overstocking or shortages
• Data-driven pricing and promotion strategies to adapt to market trends, maximize customer engagement, and boost sales

The integration of edge computing further enhances predictive AI’s impact by reducing latency and enabling real-time decision-making, such as detecting defects in manufacturing lines or adjusting inventory strategies on the fly.

By combining insights into “what will happen” (predictive AI) with “what can be done differently” (generative AI), organizations can adapt and innovate with fewer surprises and greater efficiency. Predictive AI models flag potential risks, while generative AI creates solutions—whether optimizing delivery routes, adjusting inventory strategies, or personalizing promotions based on demand forecasts.

Grid Dynamics’ predictive AI-driven anomaly detection for a global industrial tech client delivered 650% ROI with a new $250M revenue opportunity and an 8x increase in analyst productivity through automated variance analysis.

Tech-driven AI projects often generate buzz but stall without strong business sponsorship or a clear ROI path, while business-led initiatives may struggle with technical challenges. A product-led strategy bridges this gap, aligning business goals with technical execution while ensuring continuous evolution. Organizations that embrace this approach, build cross-functional teams, and adopt product-led thinking from the start will be better positioned for success in 2025.

The goal is not to simply push an AI application out the door and consider it done—it’s to lay a solid foundation, put something out there for a small user community, and gradually expand through constant monitoring and tweaking. Start small, scale steadily, and consistently monitor and fine-tune your AI systems. Unlike traditional software, AI requires ongoing nurturing to prevent performance drift and ensure it thrives against all odds.

To take AI from POC to production, businesses must establish:

• Zero-trust security layers to prevent vulnerabilities in multi-vendor environments
• Modern data stores to eliminate silos and ensure structured training data
• Observability platforms to monitor AI performance, usage, and costs
• Model banks for centralized AI models with built-in testing for accuracy
• API gateways with guardrails to enforce security and compliance
• Orchestration layers to test and switch between third-party LLMs based on performance
• CI/CD pipelines for automated AI deployment and ongoing updates

A dedicated LLMOps infrastructure ensures that AI applications scale successfully while minimizing risks. Grid Dynamics’ AI product & platform assessment helps organizations evaluate their AI readiness, address execution gaps, and create a clear roadmap for enterprise-wide AI adoption.