Enterprise leaders are stepping into 2026 facing a paradox of heightened volatility, cautious optimism, and mounting pressure to accelerate investments in artificial intelligence (AI) and quantum computing, according to a new study by the IBM Institute for Business Value. The research surveyed more than 1,000 C‑suite executives and 8,500 employees and consumers, providing a cross‑industry snapshot of strategic priorities.
Only about one‑third of executives remain optimistic about the broader global economy, yet more than four‑in‑five are confident that their own organizations will deliver solid performance over the next twelve months. This confidence is driving a push for faster decision‑making, operating‑model redesign, and deeper integration of AI into daily workflows. At the same time, customers are signaling that they will reward—or penalize—brands based on how responsibly those brands handle data.
Trend 1: Agentic AI as a Strategic Asset
Agentic AI—autonomous AI agents that can act, learn, and make decisions with minimal human oversight—is emerging as a core tool for senior leadership. A majority of executives report that AI agents are already contributing to operational efficiency, from supply‑chain optimization to customer‑service automation.
To unlock the full potential of agentic AI, organizations must address three foundational requirements:
- Data architecture must evolve from batch‑oriented reporting to near‑real‑time insight pipelines.
- AI agents need seamless, secure access to core enterprise systems such as ERP, CRM, and logistics platforms.
- The technology must transition from experimental pilots to production‑grade, scalable deployments.
Leaders are now tasked with defining clear governance frameworks that delineate which decisions can be fully automated, which require human review, and which must remain entirely human‑led to mitigate risk and preserve accountability.
Trend 2: Upskilling the Workforce for an AI‑First Era
Most employees consider the pace of technological change in their roles to be manageable and feel confident about mastering new tools. Interestingly, twice as many workers say they would welcome expanded AI usage rather than resist it, viewing the technology as a means to eliminate repetitive tasks and acquire new capabilities.
Executive surveys indicate that by the end of 2026, at least 50 % of the workforce will need some form of reskilling to adapt to AI‑driven automation. The most in‑demand competencies are problem‑solving, creativity, and innovation—skills that machines struggle to replicate.
Talent retention is increasingly linked to development opportunities. Workers are willing to change employers if a competitor offers superior training programs, making upskilling a critical lever for reducing turnover and maintaining a competitive edge.
Trend 3: Customers Demand Transparent Data Practices
Consumer trust in how brands leverage AI will be a decisive factor for the success of new products and services. While customers may tolerate occasional AI errors, they have zero tolerance for opacity.
Key expectations include:
- Clear explanations of how personal data is collected, stored, and used.
- Explicit disclosure when AI is involved in an interaction.
- Simple mechanisms to opt‑in or opt‑out of AI‑driven processes.
For executives, this translates into treating transparency as a product feature and selecting AI models that support explainability by design.
Trend 4: Localized AI and Cloud Sovereignty
AI sovereignty—an organization’s ability to control its AI models, data, and underlying infrastructure—has moved to the forefront of resilience planning. Nearly all executives say AI sovereignty will be a key component of their 2026 strategy.
Geopolitical concerns over data residency and cloud jurisdiction are prompting leaders to reassess where AI workloads run and where data is stored. The shift away from exclusive reliance on foreign cloud providers is accelerating, especially in regions with strict data‑localization regulations.
Best practices emerging from the study include:
- Deploying portable AI platforms that can operate across multiple cloud and on‑premises environments.
- Implementing continuous monitoring to ensure data‑privacy compliance.
- Prioritizing the physical location of data to meet regional legal requirements.
Ultimately, AI resilience hinges on an organization’s ability to maintain continuity and transparency, even as global technological and geopolitical landscapes evolve.
Trend 5: Planning for Quantum Advantage
Quantum computing is transitioning from long‑term speculation to near‑term experimentation. Early adopters are focusing on high‑impact domains such as optimization problems, materials discovery, and complex financial modeling, where quantum algorithms can deliver measurable performance gains.
The study recommends that enterprises identify a narrow set of quantum use cases with clear business value and join ecosystem partnerships early to share development costs and accelerate knowledge transfer.
By positioning themselves at the intersection of AI, cloud sovereignty, and quantum experimentation, forward‑looking leaders can create a differentiated technology portfolio that drives growth while mitigating emerging risks.
Original article, Author: Samuel Thompson. If you wish to reprint this article, please indicate the source:https://aicnbc.com/13918.html
