Recognizing there is no single "best" LLM, AlphaSense built a system to test and deploy various models for different tasks. This allows them to optimize for performance and even stylistic preferences, using different models for their buy-side finance clients versus their corporate users.

Beyond model capabilities and process integration, a key challenge in deploying AI is the "verification bottleneck." This new layer of work requires humans to review edge cases and ensure final accuracy, creating a need for entirely new quality assurance processes that didn't exist before.

In an enterprise setting, "autonomous" AI does not imply unsupervised execution. Its true value lies in compressing weeks of human work into hours. However, a human expert must remain in the loop to provide final approval, review, or rejection, ensuring control and accountability.

Despite AI models showing dramatic improvements, enterprise adoption is slow. The key barriers are not capability gaps but concerns around reliability, safety, compliance, and the inability to predictably measure and upgrade performance in a corporate environment. This is an operational challenge, not a technical one.

To mitigate risks like AI hallucinations and high operational costs, enterprises should first deploy new AI tools internally to support human agents. This "agent-assist" model allows for monitoring, testing, and refinement in a controlled environment before exposing the technology directly to customers.

Long-horizon agents are not yet reliable enough for full autonomy. Their most effective current use cases involve generating a "first draft" of a complex work product, like a code pull request or a financial report. This leverages their ability to perform extensive work while keeping a human in the loop for final validation and quality control.

Pega's CTO advises using the powerful reasoning of LLMs to design processes and marketing offers. However, at runtime, switch to faster, cheaper, and more consistent predictive models. This avoids the unpredictability, cost, and risk of calling expensive LLMs for every live customer interaction.

While AI models improved 40-60% and consumer use is high, only 5% of enterprise GenAI deployments are working. The bottleneck isn't the model's capability but the surrounding challenges of data infrastructure, workflow integration, and establishing trust and validation, a process that could take a decade.

IBM's CEO explains that previous deep learning models were "bespoke and fragile," requiring massive, costly human labeling for single tasks. LLMs are an industrial-scale unlock because they eliminate this labeling step, making them vastly faster and cheaper to tune and deploy across many tasks.