It’s undeniable that artificial intelligence (AI) and generative artificial intelligence (GenAI) have become central to technology conversations around how to do more with less. This rings particularly true as organizations still struggle with limited budgets, a shortage of skilled talent and the need to meet ever-changing customer expectations. Sound familiar?

According to a survey conducted by KPMG, 77% of business leaders believe that GenAI will have the most significant impact on their businesses out of all emerging technologies. Additionally, 71% of these leaders plan to implement their first GenAI solution within the next two years.[1]

With stats like these, CIOs are among those who feel the excitement (and pressure) around unleashing GenAI. But they are also neck deep in questions like, “What does this mean for my business?” and, “What risks do I need to consider?” Paramount among these vulnerabilities may be, “How do I navigate the noise around AI to empower my data engineering teams for success?”

3 Ways AI Can Enhance Data Engineers’ Work

Today, 80% to 90% of the data we generate is unstructured,[2] and the race to the top is faster than ever. That means data engineers are under more pressure to build and maintain reliable data pipelines to provide valuable insights for their business stakeholders, which can be frustrating. But traditional AI and GenAI have the potential to shift the pendulum back in favor of inspiration. Let’s look at three big wins AI and GenAI can deliver:

Innovation

Imagine if the deluge of unstructured data was effectively used to feed AI models. The winners would be those who can quickly find patterns, glean insights and get valuable recommendations where others can’t. To feed innovation, CIOs should brainstorm with their data engineering teams to get buy in on the best ways to start incorporating AI/GenAI into their work and even see some quick wins.

Consider asking your teams:

Which tactical use cases are you working on that might benefit from AI/GenAI?
How can GenAI lead to potential productivity gains or reduce the cost of building data pipelines?
What collaboration gaps could it close?

This kind of open discussion may also uncover hidden passions of the team and spark new ideas.

Productivity

With the deployment of GenAI, the role of a data engineer will essentially stay the same, but what they work on (and how they work) will shift. It will remove a lot of annoying ad hoc work and reduce the amount of manual coding and debugging needed to build pipelines.

Thanks to the profound productivity gains achieved by building pipelines with GenAI, data engineers will also be able to explore more of the tools already at their disposal. For example, these tools can help them find datasets that could be used to create reports that departmental leaders in sales or marketing can leverage to drive faster results.

Collaboration

Data engineers value collaboration, so they will be an asset when it comes to piloting and implementing GenAI. This is wildly important in the areas of data quality, data governance, DataOps, data observability and change management.

Great data engineers also know focusing on data quality is an ongoing discipline. It’s even more critical in AI’s ability to predict the best, next customized step, or to craft new code based on trusted, curated inputs. Plus, clear data lineage matters, particularly in terms of understanding where data comes from, which is usually challenging and might require a data scientist in some instances.

And it’s not just tech. Managing change with people is vital, too. How effectively can you rewire your organization around new processes? How will roles change to absorb productivity improvements and maximize the benefits of GenAI (including new positions like prompt engineering). And how can you effectively train your team to use these new tools and workflows?

Get Started with Gen AI Today

It’s no surprise that the real make or break of this new AI/GenAI trend comes down to the humans that guide it. CIOs and their data engineering teams are uniquely positioned to realize the game-changing, long-term potential for their organizations.

Are you ready to embrace GenAI? Find out how Informatica can provide you with faster, simpler, more accessible data management with our advanced AI capabilities.

[1] https://kpmg.com/us/en/capabilities-services/advisory-services.html

[2] https://techjury.net/blog/big-data-statistics/

The post How to Empower Data Engineering Teams in the Generative AI Era appeared first on Datanami.