Generative AI (GenAI) is here to stay – there’s no question about it. A recent SAS survey of 1,600 organizations found that 54% have begun implementing It, and 86% plan to invest in it within the next financial year.

As organizations integrate AI into their workflows, a critical question arises: "How does this affect me?" For many, this rapid technological shift brings both excitement and uncertainty.

I am a member of the Linux Foundation’s Business Intelligence (BI) and AI Committee – a group of experts from leading AI companies. Part of our work involves exploring how AI and BI are becoming tightly integrated, going beyond traditional reporting to create deeper insights.

But how do these changes impact the day-to-day roles of BI and AI teams? What does it mean when GenAI is thrown into the mix?

Opportunities and challenges of GenAI use

To illustrate, consider a scenario where a business analyst – let’s call her Sally Sue Somebody – finds herself at the forefront of these developments, empowered by AI-driven tools to build dashboards and solutions in real time.

Starting out as a simple report builder, Sally has grown into a key player in her organization. She uses AI-driven tools to develop dashboards and solutions on the fly. Over time, she’s taught herself how to score AI models in BI tools and effectively visualize AI model results without formal data scientist training.

Sally is the kind of analyst who knows her organization inside out – she can build anything in SQL and is the go-to person for all things visualization. If your company has a Sally Sue, you know exactly the kind of problems she’s solving daily, often teaching herself the latest technology along the way.

In the BI and AI Committee's latest whitepaper, The Alchemy of Intelligence: How Generative AI Can Revolutionize Business Intelligence and Analytics in Modern Enterprises, Sally and her colleagues – Peggy Sue Somebody (no relation), Dylan, Bob, and Alex – finally get their hands on GenAI assistants.

Sally’s colleagues represent a common persona in many organizations: business users, business analysts, data scientists, IT administrators and system architects. GenAI is reshaping these roles. Here are opportunities, challenges, and recommendations for anyone who implements it throughout an organization.

Here’s a glimpse of what they, and you, might encounter:

Business users: Boosting productivity with caution

At the heart of any organization, business users like Peggy Sue crunch numbers, build reports and manage spreadsheets. GenAI offers the potential for faster insights, uncovering trends in their dashboards. However, Peggy Sue faces a challenge: some of the answers she’s getting are clearly wrong. The AI’s answers might sound convincing, but how can she ensure their accuracy and prompt it more effectively?

Business analysts: Accelerating workflow with AI assistants

Sally herself represents business analysts. AI assistants make her job easier by automatically generating dashboards based on conversational prompts. While this has the potential to speed up her workflow, the challenge remains: will the AI provide beneficial insights that make her job faster, or will it be just another gimmick?

Data scientists: Automating tasks while ensuring accuracy

Dylan, the citizen data scientist, codes tirelessly, building models and optimizing machine learning pipelines. She can automate parts of her workflow with GenAI, from generating code to tuning models. But like Peggy Sue, she faces the question of accuracy: can she trust the AI’s output without rigorous validation?

IT administrators: Balancing efficiency with security risks

Bob, the IT administrator, is constantly juggling security and infrastructure challenges, especially with high-demand tools like generative AI assistants. While AI can streamline processes, Bob’s biggest concern is managing the security risks and ensuring these tools don’t introduce new vulnerabilities to the organization. How can he keep his organization’s data secure and ensure his infrastructure meets the demand for these new tools?

System architects: Optimizing infrastructure with transparency and trust

Alex, the system architect, focuses on designing and maintaining the organization’s technical ecosystem. GenAI can help him detect anomalies and optimize infrastructure, but transparency is a challenge. Before scaling AI's decision-making processes across the company, Alex needs to ensure they are explainable and trustworthy.

These brief snapshots offer just a taste of the insights the whitepaper covers. To fully understand how generative AI can impact your team - and to dive into the full stories of Sally Sue and her colleagues - download the full whitepaper below.