The Surprising Roadblocks to Scaling GenAI (& How to Smash Through Them)

Scaling AI Maria Pere-Perez

This article was written by our friends at Databricks. Databricks is the data and AI company. More than 10,000 organizations worldwide — including Block, Comcast, Condé Nast, Rivian, Shell and over 60% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to take control of their data and put it to work with AI.

Generative AI (GenAI) is no longer a novelty. It’s the new engine driving business growth, just like the internet did in the late 1990s. With 81% of companies planning to spend more than $200,000 on GenAI in the next year, it's clear this tech is now essential. But here’s the catch: Scaling GenAI isn’t as straightforward as it looks. Dataiku and Databricks recently conducted a survey. They got insights from 400 senior AI leaders. It revealed the major challenges businesses face. More importantly, it showed how to overcome them.

→ Read Now: AI, Today: A Survey Report of 400 Senior AI Professionals

roadblock

1. The Data Quality Crisis That Can Sink Your AI Plans

Data is the lifeblood of GenAI. But, messy, unstructured data can cripple even the best projects. The survey found that many businesses struggle with data in PDFs, images, and poorly formatted text. Low-quality data in GenAI models leads to flawed insights and poor decisions.

💡Your Move: Invest in robust data management practices. Your team must clean, organize, and structure data before using it in GenAI models. The survey shows that companies prioritizing data quality are seeing better AI results.

2. The Silent GenAI Killer: Governance and Compliance

Governance isn’t just a checkbox; it’s a necessity. The survey found 22% of respondents pointed to IT and governance issues as major obstacles. Without proper oversight, GenAI models can "hallucinate." They can produce inaccurate results. This can lead to bad decisions and compliance issues, especially with sensitive data.

💡Your Move: Build a comprehensive governance framework. Implement monitoring systems that check AI output accuracy and establish data privacy protocols. This approach keeps you compliant and builds trust in your AI systems. The survey found that companies with strong governance can scale GenAI better. They also reduce risks.

3. ROI Measurement: GenAI's Unseen Hurdle

Here’s the shocker. 65% of companies using GenAI reported positive returns. Some even achieved a 10x ROI. But many still struggle to track these gains properly. Without clear ROI tracking, it's tough to get your board's support for more investment, even if the benefits exist.

💡Your Move: Develop a clear, actionable plan for tracking ROI. Set measurable goals for your GenAI projects. They must align with your company's goals. Regularly assess these metrics to show how GenAI drives growth and efficiency. The survey found that firms with good ROI tracking can better secure investment and support from leaders.

Why These Challenges Should Be on Your Radar

The Dataiku and Databricks survey shows that scaling GenAI is complex but possible. Data quality, governance, and ROI measurement are more than tech issues. They are key to your GenAI strategy's success or failure. Addressing these challenges will help your business grow and help you outpace competitors.

Final Thoughts: Are You Ready to Scale GenAI?

Scaling GenAI is not just about integrating new technology. It’s about a strategic approach. It should emphasize data quality, build strong governance, and define ROI metrics. The survey’s takeaway is simple. The businesses that address these challenges head-on will be the ones that win in the long term.

Business leaders, take note. Be proactive. Use the right tools to tackle these challenges. This will harness GenAI's full potential. It will also drive growth and keep you competitive in today's fast-changing market.

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