Digital transformation with AI can yield a variety of benefits for businesses, from cost savings to service delivery. Improving productivity and efficiency top the list of benefits achieved from enterprise AI adoption so far, with two-thirds (66%) of organizations reporting gains. Other benefits organizations reported achieving include:

• Enhancing insights and decision-making (60%)

• Reducing costs (45%)

• Enhancing client/customer relationships (59%)

• Improving products/services and fostering innovation (43%)

• Increasing revenue (16%)

Revenue growth largely remains an aspiration, with 84% of organizations hoping to grow revenue through their AI initiatives in the future compared to just 16% that are already doing so. These numbers suggest AI is on the verge of breaking out and delivering a wide range of benefits that go far beyond efficiency and productivity improvements.

Ultimately, however, success with AI isn't just about boosting efficiency or even growing revenue. It's about achieving strategic differentiation and a lasting competitive edge in the marketplace.

One-third (34%) of surveyed organizations are starting to use AI to deeply transform creating new products and services or reinventing core processes or business models. Another third (31%) are redesigning key processes around AI. The remaining third (35%) are using AI at a more surface level, with little or no change to existing processes. While each are capturing productivity and efficiency gains, only the first group are truly reimagining their businesses rather than optimizing what already exists

As AI moves from experimentation to deployment, governance is the difference between scaling successfully and stalling out. Enterprises where senior leadership actively shapes AI governance achieve significantly greater business value than those delegating the work to technical teams alone. True governance makes oversight everyone's role, embedding it into performance rubrics so that as AIhandles more tasks, humans take on active oversight.

Autonomous systems also heighten needs for data and cybersecurity governance. Organizations need to define where humans should remain in control, how automated decisions are audited, and which records of system behavior should be retained. In terms of regulation, effective governance integrates with existing risk and oversight structures, not parallel "shadow" functions. It focuses on identifying high-risk applications, enforcing responsible design practices, and ensuring independent validation where appropriate. Leading organizations proactively monitor evolving legal requirements and build systems that can demonstrate safety, fairness, and compliance.

On the data front, legacy data and infrastructure architectures cannot power real-time, autonomous AI. As AI capabilities extend beyond software into devices, machinery, and edge locations, organizations need to evaluate if their technology foundations are ready to support potential physical AI deployments. Modernization should create a "living" AI backbone: an organization-wide, real-time system that adapts dynamically to business and regulatory change. Key ideas covered in the report:

• Leaders are enabling modular, cloud-native platforms that securely connect, govern, and integrate all data types. They break down silos with domain-owned data products and embed privacy, sovereignty, and security-by-design, while enforcing enterprise standards for quality, interoperability, and lineage. 
• A unified, trusted data strategy is indispensable. Forward-thinking organizations converge operational, experiential, and external data flows and invest in evolving platforms that anticipate needs of emerging AI.

According to the leaders surveyed, insufficient worker skills are the biggest barrier to integrating AI into existing workflows. These are the top ways organisations are adjusting AI talent strategy:

• Designing and implementing upskilling and reskilling strategies (61%)

• Educating the broader workforce to raise overall AI fluency (51%)

• Providing performance-based incentives for leveraging AI (48%)

• Redesigning career paths and career mobility strategies (47%)

• Combining or reimagining organizations based on new patterns resulting from AI usage (40%)

• Measuring worker trust and engagement (37%)

• Assessing changes to the anticipated supply and demand of skills (30%)

• Assessing target talent acquisition levels and hiring specialized talent to drive AI initiatives (30%)

• Changing the balance between full-time, contract, and gig workers (20%)