Bryan Kirschner is the Vice President of Strategy at DataStax. He has the honor of working towards a future where anybody can construct a GenAI application, all GenAI applications can function at the speed, size, and resilience necessary to fully express their value, and no one is excluded or left behind when it comes to using and profiting from GenAI.

Generative AI (GenAI) is transforming industries at an unprecedented pace, offering new avenues for innovation while raising critical questions about governance. To harness its potential responsibly, a balance must be struck between rapid technological advancements and regulatory oversight that fosters trust.

Ensuring Oversight Keeps Pace with Rapid Innovation
Regulators can keep up with rapid advancements by making transparency a core principle in oversight. Transparency enables understanding of new technologies without requiring full disclosure of proprietary methods. Companies must clearly explain their use of technologies like Gen AI, ensuring visibility into their implications for both regulators and consumers.

This transparency should extend across the entire development and data lifecycle of Gen AI applications. Companies must document how systems are built, the data sources involved, and operational behaviors. Such practices foster a culture where tools are either explainable or behave predictably and responsibly.

Transparency also prevents the accumulation of technical debt in Gen AI systems, ensuring their structure and data sources are properly documented. This approach supports effective oversight, aligns with consumer demands for trustworthy technology, and keeps oversight evolving alongside innovation.

Balancing AI Innovation with Privacy and Security
Balancing innovation and privacy involves providing consumers with genuine choices about how their data is used. Transparency helps consumers make informed decisions, such as contributing data for AI training when they see societal benefits, like free access to tools such as ChatGPT. However, there must be a distinction between data used for societal benefit and data for commercial purposes. For profit-driven applications like targeted marketing, companies should offer a fair value exchange focused on earning consumer trust.

Ultimately, consumers should have a say in whether their data contributes to AI development for public good or private gain. This ensures innovation progresses without compromising individual privacy, fostering competition on trust.

Adding Competency to Transparency and Choice
If we were to identify a ‘top three’ alongside transparency and authentic choice, the third key element would be competency—so, promoting AI literacy and understanding among users. This would involve regulatory and policy frameworks that encourage companies to educate consumers on using Gen AI effectively, helping them become "smart humans in the loop."

Gen AI can empower people in tasks like problem-solving, idea generation, and content creation, but many users may misinterpret or misuse AI tools due to a lack of understanding, as they might with basic tools. Therefore, it’s essential to promote AI competency, enabling people to know when to use Gen AI effectively, how to spot issues like 'AI hallucinations,' and to discern when alternative tools may be more appropriate.

We’ve seen what happens when we don’t have this literacy around technology in place, with social media leading to the wrong incentives or encouraging the wrong kinds of behavior. We’ve seen social media associated with negative things like the impact on body image within user populations, and we should look at how we can prevent those kinds of situations coming up in AI, and teach users about what to look out for. If we don’t have this, it will lead to market failure. We want capital flowing into productive innovation, not exploitative opportunities.

Prioritizing Budgets for Gen AI: Key Use Cases and Investments

Budget allocation for Gen AI typically falls into two categories: hero apps and workflow apps.

Hero apps are strategic, high-impact Gen AI projects with significant business goals, requiring substantial investment and delivering high ROI. Workflow apps, on the other hand, are smaller-scale tools designed to enhance productivity across roles. These apps, like ChatGPT or Copilot subscriptions, have low setup costs and are adaptable, making them ubiquitous in enhancing workflows.

As organizations build expertise and recognize the advantages demonstrated by early adopters, Gen AI integration will become routine, driving widespread innovation and productivity

The most significant budgets will likely go to areas where Gen AI familiarity is higher. As users become more proficient, investments will grow organically. For example, ChatGPT’s summarization feature saves hours weekly, demonstrating immediate efficiency gains. Over time, such workflow enhancements will evolve into products and features integrated into daily operations.

Understanding Gen AI as a collaborative partner rather than a straightforward tool requires thoughtful usage. Just as calculators revolutionized calculations, Gen AI transforms writing and problem-solving—but only for those who learn to use it effectively and responsibly.

Gen AI Investments: Impact on Talent and Innovation
Even conservative Gen AI applications—such as generating reports, taking meeting notes, and writing code—offer immense productivity gains. Agile teams can leverage Gen AI to accelerate idea generation, prioritization, and code production, gaining a competitive edge.

This productivity shift is influencing talent strategies. Companies prioritize hiring individuals skilled in using Gen AI to enhance workflows, as proficiency in these tools becomes essential for competitiveness. As a result, firms adopting Gen AI see advantages in time-to-market, employee experience, and innovation. Early adopters gain a significant lead over slower-moving competitors, highlighting the transformative potential of Gen AI.

Overcoming Barriers to Make Production AI Mainstream
Gen AI adoption is uneven, with only a minority of companies advancing high-impact applications. This is due to challenges in integrating Gen AI consistently across organizations. To become mainstream, firms need robust systems for building, deploying, and managing Gen AI—a departure from static AI applications.

Unlike simple one-time tools, Gen AI requires sustained interaction between human workflows and AI. This involves addressing issues like model drift and adapting to evolving user needs. By applying Gen AI’s full range of capabilities, companies can unlock transformative benefits across departments.

Next year is poised to be the breakthrough year for the production of AI. As organizations build expertise and recognize the advantages demonstrated by early adopters, Gen AI integration will become routine, driving widespread innovation and productivity.