Fewer than one in four companies—only 22%—consider their AI deployment as strategic. That’s according to the respondents of our State of Data Science and AI survey. An unclear or absent strategy can limit how productive artificial intelligence (AI) initiatives become.
However, we’re seeing progress toward those strategic approaches. In particular, there’s a varied mix of AI implementation and how organizations can measure its value. And that’s encouraging as companies look to build on their AI foundations.
For the eighth consecutive year, we conducted our State of Data Science and AI survey to dive further into how data scientists, developers, engineers, and other roles are innovating with AI and open source, and where roadblocks might remain.
ROI Is Taking On Many Forms
Productivity improvements (58%) and cost savings (47%) are the most common ways companies are measuring ROI. Fewer (25%) are looking at go/no-go decision error reduction or risk mitigation.
Other areas that may be worth exploring include reducing time-to-value, selling more of a product, or broadening the user base, and creating a happier, more engaged workforce. There’s no one-size-fits-all solution to measuring the value of AI. It’s a matter of looking at what makes it valuable to your company, your customers, and your people.
About 26% of respondents said difficulty demonstrating ROI is a top concern about AI risk. And 13.5% reported they don’t measure ROI at all, or are unsure how their company tracks it.
Is that necessarily a bad thing? It’s all too easy to get caught up in metrics. Yes, it’s important to measure your AI usage and the outcomes tools are producing. But when hitting a certain number or benchmark becomes the main goal, that’s where we’ve lost sight of what really matters. The key is understanding where AI improves your company. Where does it make you better? How does it solve real business challenges, and not just fabricated ones that look good in a report?
Getting a better grasp of those results lays the foundation for AI initiatives. And it sets up companies to scale at a more rapid pace.
How Can AI Scale Faster?
Ignoring structural issues can stall AI projects before they get off the ground. We asked respondents about challenges when they’re moving data science or AI models to a production environment. Here are the most common answers:
45%: Data quality and pipeline consistency issues
40%: Security, privacy, and regulatory compliance challenges
39%: Scaling inference and managing compute costs
33%: Cross-team collaboration and communication gaps
Notice a theme across these issues? They’re all results of a misaligned organizational approach. Fragmentation is bad when there’s a lot of time spent integrating the tools and a lack of clarity on how it fits into a workflow. The entire team doesn’t need to be experts on the inner workings of an AI model, but they should know how to use it efficiently, properly, and safely to solve the problems the tool promises.
When I can add a new tool to my toolchain and it just works, that’s a feature, not a bug. Interoperability is key. Clear and concise processes and guidelines can nudge an organization down the right path, especially as we move towards companies developing their own smaller models to address specific business use cases.
Aligning on goals is a vital step. What will really help AI projects scale is confidence and trust, with both employees and customers. More than half of our respondents (53.3%) have no AI governance policies or frameworks in place, or those policies are still being developed. A start is better than nothing at all, and putting some guardrails in place keeps people on the right path, even if they’re experimenting along the way—which is absolutely encouraged.
Open Source Will Guide the Path Ahead
The good news is that we have an ecosystem that’s full of those supportive guidelines and tools. The open-source community was built on collaboration and community, which is a necessary component of creating high-quality and innovative things.
I’m encouraged by the enthusiasm around open source. About three in four respondents (76%) say their organization has more priority on open source this year compared to their previous 12 months. And 92% of respondents use open-source AI tools and models already.
When we’re intentional about learning how to use AI and how to secure, deploy, and monitor it, that’s when we’re at our best. Anaconda is here to empower faster time-to-value and innovation, and we’re looking forward to seeing more creative implementations of open-source AI tools.
Download the full 8th Annual Data Science and AI Report: How Companies Are Moving Ahead—Or Not—in the AI Race.