AI and Geopolitical Uncertainties Upend Business and Tech Norms
By Brian Pereira (creed_digital)
Geopolitical shifts and tech disruptions have rendered static long-term planning obsolete. To stay competitive, organizations must act with agility, take calculated risks, and rethink sourcing and supply chains. Artificial intelligence intensifies the disruption, reshaping business models, workforce structures and IP frameworks, yet most remain unprepared.
A study from infrastructure services management company Kyndryl titled “2025 Readiness Report” shows that less than a third, around 31 per cent, of organizations this year feel completely ready across external business risks. That’s only a marginal improvement from 29 per cent last year.
The study combines insights of 3,700 business leaders from 21 countries with data from Kyndryl Bridge, the company’s AI-powered business platform. There was representation from 24 industries, with a focus on eight core industries: banking and financial services, energy and utilities, healthcare, insurance, manufacturing, including consumer products, retail technology, and telecommunications.
“Compared to the 2024 Readiness Report, organizations still feel largely unprepared for future risks, especially as AI accelerates disruption and exposes existing gaps. Cyberthreats remain the top concern, but most organizations have also struggled with a wider set of outages, disruptions and evolving regulatory demands,” senior vice president at Kyndryl Consult, Ismail Amla told CIO.inc.
Organizations that participated in the study cited IT infrastructure upgrades as the most effective way to improve infrastructure resiliency and mitigate external business risks. They said upgrades deliver broad benefits, particularly in efficiency and innovation.
“Since modern cyber resilience strategies and emerging technologies like AI rely on strong foundational tech and data stack, reinforcing this foundation becomes essential for building trust, increasing business agility, maintaining uptime and defending against disruption,” Amla said.
Achieving AI Success
AI-enablement is seen as a key infrastructure upgrade, with three in four organizations investing in AI. Per the Kyndryl study, 33 per cent organizations have increased their AI investment over the past year. Yet 57 per cent say their company’s innovation often stalls after the proof-of-concept phase due to foundational issues in the technology stack, and 35 per cent percent do not see positive return on investment on AI and cite integration as a top challenge.
But 61 per cent feel more pressure to prove ROI on their AI investments vs. a year ago, with 68 per cent investing “heavily” in some form of the technology.
The gaps identified include unclear business outcomes, weak ROI frameworks, fragmented or poor-quality data, and the absence of production-grade engineering practices such as MLOps and model monitoring. Too often, business, IT and data teams aren’t aligned on ownership, governance or how the solution will be operationalized once it goes live.
Organizations often fail by relegating AI to side experiments instead of integrating it into core strategy. Research from IDC, in partnership with Lenovo, found that 88 per cent of observed proofs of concept don’t reach broader deployment. For every 33 AI POCs a company launches, only four graduate to production, IDC found. The reason is weak alignment with business outcomes. Three in five, 62 per cent, report they are still in the experimentation phase when it comes to AI.
To succeed, the study recommends that CIOs and CAIOs must treat AI as a long-term capability, not a one-off experiment. That means anchoring every use case to a measurable business problem, investing early in clean and accessible data foundations, and designing for production from day one.
AI for Business Value
Microsoft illustrates how an organization can turn AI initiatives into business value instead of isolated experiments. Its AI Strategy Roadmap is based on extensive research and interviews with business leaders, ensuring that every AI project is tied to a clear business outcome.
Microsoft’s AI Strategy Roadmap has five drivers:
Business strategy: Successful AI initiatives aren’t pursued in isolation as technical experiments. Instead, they should be tightly aligned with the overarching objectives of the organization. Projects are evaluated on business impact metrics such as productivity gains, cost savings or revenue growth – rather than abstract AI accuracy scores, technical performance and benchmarks.
Technology and data strategy: By turning data into a strategic asset and treating technology as a catalyst for growth, an organization becomes better positioned to innovate responsibly, adapt quickly and realize measurable value from AI.
AI strategy and experience: Without the right tools, processes and skills to turn plans into real solutions, companies struggle to gain meaningful business value from AI. Apart from defining a clear vision for success, organizations must also develop technical proficiencies and repeatable processes – making it possible for teams to develop and deploy AI solutions consistently and at scale.
Organization and culture: Technology alone doesn’t guarantee AI success. Without the right organizational structure and culture in place, even the most promising initiatives can stall or fail entirely. Businesses can prepare for AI transformation by aligning individuals at all levels of the organization, including employees, teams and leadership, with the diverse demands required to achieve AI-powered value creation at scale.
AI governance: As intelligent solutions become more embedded in business operations, governance provides the necessary guardrails to manage risks, ensure compliance and build trust. Effective AI governance is about coordination, not just control. It brings together legal, compliance, IT and business leaders to define how AI should be used and monitored across the organization.
Business leaders must define ownership, cost models and life cycle plans upfront. The organizations that get this right focus on reliability, scalability and continuous improvement rather than short-term success.
IBM is a strong example of an organization where leaders define ownership for AI projects, cost models and life cycle plans upfront, with a focus on reliability, scalability and continuous improvement rather than short-term wins.
It has built a robust framework that integrates AI governance, AI security, data governance and data security. This ensures that ownership and accountability are clearly defined across teams. Leaders at IBM emphasize long-term life cycle management of AI systems, including monitoring for bias, drift and compliance risks. A unified approach to governance prevents short-term fixes and supports sustainable and consistent scaling across the enterprise.
IBM’s model is iterative, with leaders continuously refining governance policies, risk assessments and accountability structures. This ensures AI systems evolve responsibly over time rather than chasing short-term ROI.
