The shift to decision intelligence: Move beyond analytics to real time activation

UK businesses are experiencing tangible benefits from artificial intelligence, with 66% reporting significant productivity gains. However, traditional analytics tools remained reactive and slow, relying heavily on human input. The advent of decision intelligence, which combines AI autonomy with human oversight, is poised to revolutionize this landscape.

Decision intelligence systems, powered by domain-specific foundational models, can now autonomously handle complex decisions across various domains such as supply chains and customer operations. Unlike traditional automation, which followed predefined workflows, these systems analyze data from multiple sources to make intricate decisions, much like an underwriting agent that estimates creditworthiness, adjusts loan pricing, and manages paperwork.

The European decision intelligence market is expected to grow from $4.35 billion in 2025 to a projected $20.61 billion by 2035. Yet, the key challenge for organizations adopting this technology lies in determining the appropriate level of autonomy for their AI systems. While AI can handle many routine tasks, it must escalate to human intervention in certain situations, such as when a compliance issue arises.

This balance between AI and human judgment is crucial for effective decision-making. The future enterprise will leverage AI to augment human expertise, particularly in areas requiring domain-specific knowledge, ethical oversight, and exception handling. Organizations that successfully integrate this human-AI division of labor will reap substantial benefits, such as the double-digit ROI observed in beauty and fragrance retailers.

However, the path to realizing the full potential of decision intelligence is not without risks. Cybersecurity threats, compliance failures, and lack of transparency are among the challenges that need to be addressed. Moreover, the initial cost of implementing these systems can be significant, though foundational models are helping to optimize this expense.

Ultimately, the successful adoption of decision intelligence requires a strategic approach that involves not just technological upgrades but also changes in operating models, talent acquisition, and the establishment of robust governance frameworks. By mastering this division of labor between humans and AI, organizations can move beyond reactive analytics and embrace proactive decision-making, gaining a competitive edge in the rapidly evolving technological landscape.