The rise of generative AI and agentic AI is an existential imperative, a foundational shift that threatens to redefine what software is, who wields it, and how nations generate wealth.
Mohammed Al-Qarni, an academic and consultant on AI for business, said, “This is a quantum jump in potential productivity, yet history warns us that success hinges entirely on the political and organisational will to design frameworks capable of seizing, not squandering, this opportunity.”
The chilling reality is that this transition is arguably more disruptive than the Software-as-a-Service revolution that preceded it. But what happens when corporations lack the courage to lead? The historical record of digital transformation is littered with the corpses of once-dominant giants, and Kodak serves as the perpetual, damning example.
Despite pioneering digital technology, the company’s strategic reluctance to scale its own innovation, driven by a fear of cannibalising its immensely profitable film business, proved a fatal weakness. Such a protectionist approach and internal cultural resistance led to a catastrophic delay, allowing competitors like Canon and Sony, which had adopted flexible and responsive digital strategies, to capture significant market share.
Survival in a disruptive era demands a willingness to disrupt your own established, profitable business models actively. The transformation required is a radical and holistic overhaul.
Today, the same pattern of institutional failure is visible. While 88% of organisations report utilising AI in at least one business function, showing a clear awareness of the threat, the majority remain dangerously vulnerable. Nearly two-thirds of them confess they have yet to begin scaling the technology across the enterprise, remaining confined to the experimentation or piloting phase.
Such a gap between acknowledgement and action is the single most dangerous vulnerability, demonstrating a failure to establish the strategic and organisational frameworks necessary to manage the disruption. For those who do manage to scale, the financial verdict is already in.
Organisations report an average return on investment of 1.7x on AI and generative AI investments, alongside cost reductions ranging from 26% to 31% across core functions like supply chain and finance. Executives cite tangible improvements, reporting 10% to 20% gains in accuracy, productivity, and time-to-market.
The barriers preventing such scaled adoption are not rooted in technical limitations but in human frailty and strategic myopia. The most frequently cited obstacle is the inability to define clear use cases or establish demonstrable business value.
Such a pattern reflects a failure of imagination, rooted in trying to apply AI to traditional, inefficient problems rather than focusing on “AI-native problems,” which are challenges that become uniquely tractable or profitable only through AI-first thinking.
Compounding this strategic deficit is the internal “human firewall,” and nearly half of CEOs report that employees are resistant or even hostile to AI adoption, often driven by profound anxiety over job security. To overcome this resistance, leadership must invest in upskilling, rewire organisational culture, and establish governance that instils confidence and trust.
Furthermore, even where the will exists, the technical foundation often fails. Businesses consistently identify data quality, availability, and the management of silos as the paramount technical barriers to implementation.
“Without clean, well-organised, and accessible data, advanced models underperform, undermining the entire investment. Agentic AI systems, which require continuous refinement, are particularly dependent on real-time data pipelines and robust governance capabilities often incompatible with rigid, older legacy infrastructure,” Al-Qarni stated.
Strategic autonomy
In an era of accelerating technological competition, the AI transition is fundamentally a geopolitical contest, where national strategy is the new competitive differentiator. The global economic benefits are colossal. There is $19.9 trillion projected to be injected into the global economy through 2030, a figure accounting for 3.5% of global GDP that year.
That injection is projected to create a permanent increase in economic activity, with compounded GDP levels potentially 1.5% higher by 2035. But here is the critical economic context: global growth is projected to decelerate, slowing from 3.3% in 2024 to 3.2% in 2025, while major development finance providers are cutting aid and adopting a markedly more transactional, geopolitical approach to investment.
The United States, the United Kingdom, France, and Germany have all simultaneously cut aid for the first time in nearly thirty years. Consequently, nations can no longer rely on traditional development finance; they must secure resources and advanced infrastructure through massive, proactive investment and strategic partnerships.
Moreover, the pace of AI innovation is inextricably linked to the regulatory landscape, and flexible regulatory environments, such as that in the United States, are already projected to outperform those with more rigid frameworks, confirming that policy itself is a critical competitive lever.
Against this backdrop of global competition and shrinking fiscal space, Saudi Arabia’s comprehensive strategy, anchored in the ambitious economic diversification strategy named “Vision 2030,” provides a clear, state-led template for achieving strategic autonomy and leapfrogging competitors.
Artificial intelligence is positioned as the core technology driving economic diversification beyond oil and building a knowledge-based economy. The National Strategy for Data and AI (NSDAI), established in 2020 by the Saudi Data & AI Authority (SDAIA), sets extremely aggressive, non-negotiable targets to rank among the world’s top 15 nations in AI by 2030.
Massive financial and infrastructural commitments underpin that ambition. The Kingdom aims to attract SAR 75 billion ($20 billion) in AI investments by 2030, covering both local funding and foreign direct investment (FDI) for data and AI initiatives.
Such committed capital is necessary to secure the foundational computational power, demonstrated by strategic partnerships already accelerating the buildout, including the $10 billion, five-year collaboration between AMD and Humain to deploy up to 500 megawatts of AI infrastructure by early 2026, and a $5 billion-plus “AI Zone” partnership with Amazon Web Services (AWS) and Humain.
By aggressively attracting billions in investment from global leaders, the Kingdom is designed to mitigate dependency on transactional global aid and secure continuous access to advanced chip technology, thereby establishing critical strategic autonomy in the global AI race.
Critically, the NSDAI also prioritises policy flexibility, aiming to enact “the most welcoming legislation” for data and AI businesses and talent, including fast-track approvals and IP protections.
Furthermore, recognising that infrastructure is meaningless without talent, the strategy mandates training over 20,000 data and AI specialists to transform the national workforce. Such a comprehensive approach to investment, infrastructure, policy, and human capital serves as the blueprint for securing strategic advantage.
Human-AI value shift
To capture the true value of AI, organisations must discard incrementalism and adopt an AI-first operating model rooted in autonomy. The process begins with an “automation-first mindset,” redesigning processes to embed AI capabilities as core mission enablers, while ensuring systems are modular and interoperable to avoid vendor lock-in.
The primary goal is to streamline workflows and reduce manual effort, unlocking operational savings that can be strategically reinvested into high-value, mission-critical areas. The real disruption lies in embracing agentic AI. There are autonomous agents capable of complex decision-making and orchestrating workflows that rigid legacy systems simply cannot support.
The transition requires disciplined execution; the failure of projects like Volkswagen’s Cariad highlights the danger of strategic overreach, where an attempt is made to deliver a complete, custom technology stack without necessary sequencing and ruthless scope control.
The economic consequences of the transition are profound, resting on the fundamental restructuring of service value. As automation commoditises efficiency, the value proposition shifts dramatically. Professional services will become the most valuable service line, transitioning from transactional execution to strategy-first advisory, guiding organisations on how to architect and implement these complex, autonomous systems.
Simultaneously, managed services will ascend to focus on autonomous orchestration, while support services experience heavy automation at the core, refocusing human expertise onto the premium edges—complex diagnostics and bespoke problem-solving that require critical thinking.
For nations like Saudi Arabia, targeting the training of 20,000 specialists, this predictive shift confirms that training must prioritise advanced advisory, architectural, and integration skills, the core competencies of the high-value professional services sector, to ensure the nation captures the top tier of economic value.
Such a transformation is fundamentally about engineering a robust partnership between human judgment and machine intelligence, establishing systems that are more creative, resilient, and adaptable than either could be in isolation.
While AI excels at processing vast datasets and identifying patterns, it cannot critically apply human judgment, question assumptions, and navigate ethical complexities. Consequently, the most valuable human skills in the AI era will be critical thinking, ethical reasoning, and domain expertise, which assess, refine, and guide AI outputs.
Crucially, the strategic deployment of AI acts as a powerful mechanism for improving overall workforce performance. Studies show AI tools provided a 43% performance increase for lower-performing consultants, compared to 17% for high performers, demonstrating their power to lift the operational baseline of the entire organisation. To realise these systemic productivity gains, organisations must move beyond informal “shadow IT” use.
For chief strategy officers and chief digital officers, the path forward is clear. They must redesign for autonomy, prioritise human-AI complementarity by formalising adoption and reskilling the workforce, and govern and measure strategically.
Only by moving beyond basic ROI and aggressively tracking “Trust and Adoption Velocity” can organisations ensure they are building sustainable, resilient competitive advantage in the new economic epoch.