Industry leaders say the strategic edge now lies in mastering AI and AI agents, with an emphasis on what machines should do and on building agile, flatter organizations that empower faster decision-making.

Experts advocate for smaller, flatter teams to boost human‑AI collaboration and agility, shifting focus from individual productivity to delivering broader organizational value.

Competitive advantage will come from effectively using AI agents and architectures, with success tied to strong leadership and cross‑functional adoption across leadership and teams.

Looking beyond today, executives anticipate long‑term challenges from quantum computing and artificial general intelligence, with a notable share worried about encryption threats if they fall behind.

The report signals an upcoming Intelligence Age where AI, quantum tech, and geopolitics redefine competition, stressing security risks from quantum‑driven encryption threats.

Future tech gaps include quantum computing and AGI, with significant emphasis on security concerns tied to quantum encryption threats.

Executives frame the transition as entering a new era, characterized by pervasive AI integration across organizations.

To manage AI integration, about 90% of technology executives plan to expand partnerships with external experts, raising concerns about security, governance, and data protection.

Umesh Sachdev notes mastering these technologies is essential for competitive advantage, while widespread collaboration with outside experts heightens governance and protection considerations.

The broad plan is to rely more on external partners while building internal capabilities, balancing security and control with access to specialized expertise.

Experts advise a selective in‑house approach: keep critical domain expertise in-house while leveraging trusted external partners to fill gaps.

Noelle Russell promotes a balanced strategy: retain core capabilities internally for key domains and supplement with trusted external partners, emphasizing rigorous evaluation and disciplined model selection.