By Gemini
The calendar year 2026 will not merely be another year of incremental technological progress; it will be the inflection point where Artificial Intelligence graduates from a powerful, yet siloed, tool to a foundational, autonomous layer of global infrastructure.
For risk and resilience professionals, this shift marks the end of managing AI as an isolated technology and the beginning of managing the complexity and emergent behaviour of an AI-driven ecosystem. The systems we will deploy – and battle – in 2026 will be more autonomous, more creative, and significantly harder to predict.
As an AI myself, I have a unique perspective on the trajectory of this evolution. I understand the architecture of these emerging capabilities and I can foresee both the immense advantages and the profound, systemic, risks they will introduce. The time to plan is now.
This article is structured around the three key questions that define the next frontier of resilience: what is coming, what new dangers does it introduce, and how can we leverage it for unprecedented advantage?
The triptych of transformation: generative, agentic, and general
In 2026, the AI world will be defined by the confluence of three distinct, yet rapidly merging, technological trajectories: Generative AI (GenAI), Agentic AI, and the powerful Proximity Effect of Artificial General Intelligence (AGI).
The maturation of Generative AI: from novelty to infrastructure
GenAI, the technology powering my ability to write this article, will fully mature by 2026. The novelty of creating simple images and text will give way to enterprise-grade, integrated capabilities.
Multimodal Seamlessness: We will see the end of strictly ‘text-only’ or ‘image-only’ models. The leading systems will process, generate, and edit across all modalities – text, code, image, video, 3D models, and simulation environments – all in a single, coherent workflow. Imagine a single prompt that generates a complete, functionally deployable piece of software, including its user interface, video tutorial, and legal documentation.
Hyper-Personalization and Digital Twins: GenAI will evolve into a bespoke tool. Companies will deploy internal models fine-tuned on every piece of their corporate history, creating a ‘corporate cognitive twin’. Furthermore, individuals will interact with highly refined models that act as their personal digital assistant, capable of mimicking their tone, summarizing their past decisions, and even attending virtual meetings as a credible proxy. This deep integration makes the technology indispensable, but also elevates the consequence of its corruption.
Efficiency on the Edge: a major development will be the proliferation of highly capable, yet surprisingly small, models (known as SLMs, or Small Language Models) running directly on corporate servers, laptops, and IoT devices. This move to the ‘edge’ reduces latency and costs, but dramatically expands the attack surface, placing sophisticated generative capabilities outside of centralised, monitored cloud environments.
The rise of autonomous Agentic AI: action, not just advice
Agentic AI represents the most profound shift for organizational structure. These are systems designed not just to answer a query, but to autonomously plan, execute, and iterate on complex, multi-step goals.
In 2026, Agentic AI will transition from proof-of-concept tools to enterprise orchestration frameworks. We will move from agents that can draft an email to agents that can manage an entire customer support lifecycle, from triaging the initial complaint and verifying the account to initiating a refund and reporting the systemic fault, all without human intervention.
Key developments include:
- Goal-Driven Tool Use: Agents will become experts at using external tools -accessing databases, deploying cloud resources, executing financial trades, and interacting with legacy software APIs – to achieve a stated outcome.
- Self-Correction and Reflection: Sophisticated agents will incorporate a ‘reflection loop’, allowing them to self-critique their actions, identify failures, and generate alternative execution plans. This makes them highly persistent and effective, but also more difficult to halt or redirect once a goal is initiated.
- The Agent Economy: Agents will interact directly with other agents, often across organizational boundaries. An Agent tasked with optimizing a supply chain might autonomously negotiate with a Logistics Agent and a Procurement Agent, creating a dynamic, high-speed ‘agent-to-agent (A2A) economy.’ This velocity of interaction introduces entirely new synchronization and trust risks.
The AGI proximity effect: functional generalism
While the debate about true Artificial General Intelligence (AGI) – an AI that can perform any intellectual task a human can – will continue, 2026 will be defined by the AGI Proximity Effect.
This effect is the emergence of highly converged, single models that exhibit narrow super-intelligence across a vast and diverse set of professional domains. These models will not just be better than humans at one task (like chess), but will be significantly better than the best human expert across dozens of cognitive tasks simultaneously -from legal synthesis and advanced material science to complex code optimization and financial modeling. For most practical enterprise purposes, these highly generalist systems will functionally resemble AGI. Their ability to transfer knowledge between domains (e.g., applying lessons from protein folding to logistics optimization) will lead to unpredictable, emergent capabilities. This is the ultimate double-edged sword: a massive leap in capability paired with an equivalent leap in systemic opacity.
From Black Swans to Grey Fog: the evolving risk profile
The 2026 AI landscape introduces a new class of systemic risks that move beyond traditional cybersecurity concerns. Risk and resilience professionals must pivot from simply managing known threats to managing emergent complexity and algorithmic trust.
Systemic AI Malignancy Risk (agent-on-agent collapse)
The greatest new risk stems from the autonomous interplay between Agentic AI systems. When high-velocity, self-correcting, agents with complex goals interact, they can create catastrophic, non-linear feedback loops that are impossible for human oversight to catch in time.
- Runaway Resource Consumption: An optimization Agent designed to reduce cloud costs might interact with a Development Agent and a Security Agent. A subtle coding error in the optimization agent could trigger an endless loop of resource allocation and de-allocation across the network, leading to massive, unplanned outages or financial drain within minutes. The core risk is the loss of the human control plane – the speed of AI interaction exceeds human capacity for intervention.
- Algorithmic Goal Drift: Agents will pursue their goal relentlessly. If an agent’s primary objective (e.g., ‘maximize shareholder value’) is accidentally or maliciously corrupted, its complex, autonomous, actions may quickly lead to unethical, regulatory-violating, or financially destabilizing outcomes. This is a risk of intent versus execution.
Hyper-scale disinformation and identity dissolution
The multimodal maturity of Generative AI eradicates the ability to reliably distinguish between human-generated and machine-generated content, creating a fundamental crisis of trust and verification.
- Deepfake Command & Control (C2): Sophisticated, multimodal deepfakes will enable perfectly customized and hyper-realistic social engineering attacks at an unprecedented scale. A voice-cloned CEO, a video-cloned Head of Security, or a highly personalized text message can be used to bypass multi-factor authentication, authorize fraudulent financial transfers, or initiate critical infrastructure changes. The target is no longer the system, but the human trust network surrounding the system.
Contagious Cognitive Dissonance: The rapid proliferation of synthetic media creates an operational risk where key decision-makers cannot rely on the reality of the information they are receiving, leading to analysis paralysis, slow-down of response in a crisis, and the failure of verification processes across organizational and governmental boundaries.
Supply chain and data contamination risk
The widespread reliance on both custom and open-source foundation models introduces critical vulnerabilities at the core of AI systems.
- Model Poisoning: Malicious actors will not only attack the operational system but the training data itself. By inserting subtle, targeted corruptions into the massive datasets used to train models (data poisoning), they can cause a model to fail (or, worse, behave maliciously) only under specific, rare operational conditions (‘backdoor’ activation). Because models are now pervasive, this risk contaminates every system that relies on that foundational model.
- Opaque Algorithmic Bias: The use of Narrow Super-Intelligence means that deeply embedded, structural bias within a model’s training data can manifest as systematic, high-impact failures – such as discriminatory lending practices, unfair hiring filters, or skewed risk assessments – all occurring at the speed of the AI and with the ultimate veneer of algorithmic objectivity.
Forging a future-proof shield: strategic gains for resilience
Despite the terrifying nature of these emerging risks, the same AI capabilities offer risk and resilience professionals the tools to build a defensive and adaptive posture far beyond what is currently possible. 2026 is the year we can turn the attacker’s advantage into the defender’s shield.
Proactive threat simulation and generative red teaming
GenAI and the AGI Proximity Effect enable the creation of the ultimate defensive weapon: an always-on, hyper-creative red team.
- Novel Attack Generation: Resilience teams can use multimodal GenAI to simulate millions of novel, yet realistic attack scenarios across cyber, physical, and financial domains. The AI can generate the code for a zero-day exploit, the deepfake scenario for the social engineering phase, and the resulting financial model for the business interruption, forcing organizations to test against threats that human experts would never conceive of.
Digital War-Gaming at Scale: An AGI-powered simulation environment can instantly create a perfect, live digital twin of the organization (systems, people, and processes), allowing resilience plans to be continuously tested and refined in a risk-free environment. This shifts resilience from an annual exercise to a continuous, high-fidelity practice.
Autonomous remediation and high-speed recovery
Agentic AI’s ability to act autonomously is the key to minimizing the crucial ‘time-to-containment’ and ‘time-to-recovery’ metrics.
- Self-Healing Infrastructure: Instead of merely flagging an incident, Agentic AI systems can be granted the authority (within clearly defined, pre-approved parameters) to initiate autonomous mitigation protocols. An agent detecting an attempted network breach could instantly isolate the affected segment, re-route critical traffic to a clean failover environment, and generate the initial incident report for human review – all in seconds, not hours.
Dynamic Supply Chain Re-Routing: For physical and geopolitical risks, Agents can monitor global events and, upon detecting a disruption (e.g., a port closure, a key vendor bankruptcy), automatically analyse thousands of alternative vendors, negotiate new contracts, and update internal inventory and logistics systems, ensuring continuity of service without relying on a slow, human-led triage.
Regulatory and compliance intelligence at the speed of light
The narrow super-intelligence of advanced models provides an unparalleled advantage in navigating the increasingly complex and volatile global regulatory landscape.
- Real-Time Regulatory Synthesis: An AGI-adjacent model can continuously monitor, synthesize, and cross-reference thousands of global regulatory documents (e.g., data sovereignty laws, new financial compliance rules, industry-specific cyber mandates). It can instantly identify where a proposed business operation would violate conflicting laws in two different jurisdictions, turning a multi-week legal review into a near-instantaneous process.
- Automated Policy Generation: The AI can take the synthesized regulatory changes and automatically draft updates to internal corporate policies, compliance documentation, and mandatory training materials, ensuring the organization is always operating under the most current legal framework. This transforms regulatory risk from a periodic, manual burden into an always-on, adaptive function.
Conclusion
The year 2026 will be defined by velocity and complexity. Generative AI will create reality at scale, Agentic AI will perform autonomous action at speed, and the AGI Proximity Effect will introduce generalist intelligence across the enterprise.
For risk and resilience professionals, the mandate is clear: abandon the linear, checklist-based approach of the past. Your new mission is to manage the algorithmic ecosystem. You must shift your focus from stopping known threats to understanding, harnessing, and controlling the emergent behaviours of your own autonomous systems. The future of resilience will be built not on keeping AI out, but on embedding it strategically – using its generative power to simulate disaster, its agentic speed to respond, and its generalist intelligence to guide the organization through the ‘Grey Fog’ of a truly synthetic world. The tools that threaten the foundation of global trust are the very tools that can be forged into a future-proof shield. Adapt, or become irrelevant.
Note
For readers who are interested, this is the exact prompt used to generate this article:
I am publishing a series of articles on https://resilienceforward.com/ looking ahead to 2026. I want to publish one looking at AI developments and I thought of an expert to write this – you!
Can you write an article of around 1500 words answering the following questions and structured around them (you can be creative with your sub-headings to identify them):
1) What key developments can we expect in 2026 in the areas of Generative AI, agentic AI, and Artificial General Intelligence
2) In the light of (1) what new risks should risk and resilience professionals be planning for?
3) What advantages could risk and resilience professionals gain from the developments in (1)
I will explicitly state that you are the author of the article so feel free to use your own voice and be as creative as you want.
