Article
Third-party ecosystems are the new risk perimeter
How supplier, cyber and reputational exposures can propagate across extended enterprise networks faster than traditional controls can respond.
Understand the risks created when technology begins generating, recommending or executing actions rather than simply supporting human work
We connect emerging technology use cases, autonomy and consequence to determine where new forms of risk require different controls, oversight and accountability.
AI and autonomous technologies introduce risks that do not fit neatly into conventional technology control models. Systems can generate unpredictable outputs, act across connected processes and scale errors faster than human review can respond. As autonomy increases, accountability and intervention become harder to define. Emerging-technology risk examines how these characteristics interact with the consequence of specific use cases. It distinguishes experimentation from applications where errors can affect customers, operations, safety or strategic decisions and establishes the level of oversight, testing and control appropriate to each.
Focus
Emerging technology risk begins where capability outpaces control
AI and autonomous systems introduce new exposures across decisions, data, accountability and system behavior.
Read nowStrategic Challenges
Which technology risks become material before controls catch up?
The challenge is identifying where rapid adoption creates exposure that existing governance and assurance were not designed to manage.
Read nowStrategic Impacts
A structured risk view makes emerging technology exposure easier to govern
Clear use cases, control gaps and ownership help leadership distinguish acceptable experimentation from unmanaged enterprise risk.
Read nowObserved Patterns
Technology programs often assess capability before testing failure modes
Potential value can dominate discussion while autonomy, misuse, model error and unclear accountability remain insufficiently examined.
Read nowStrategic Challenges
Which technology risks become material before controls catch up?
The challenge is identifying where rapid adoption creates exposure that existing governance and assurance were not designed to manage.
Read nowStrategic Impacts
A structured risk view makes emerging technology exposure easier to govern
Clear use cases, control gaps and ownership help leadership distinguish acceptable experimentation from unmanaged enterprise risk.
Read nowObserved Patterns
Technology programs often assess capability before testing failure modes
Potential value can dominate discussion while autonomy, misuse, model error and unclear accountability remain insufficiently examined.
Read nowPOV
Innovation speed is not a reason to weaken risk discipline
The less mature the technology, the stronger the case for explicit boundaries, ownership and conditions for use.
Read nowOur approach
Assess emerging technologies through the autonomy, consequence and reversibility of their use rather than applying one control model to every application
Our approach begins by mapping AI, autonomous and emerging-technology use cases and identifying where systems influence or execute consequential decisions. We assess autonomy, model uncertainty, human oversight, data dependency, failure modes and the ability to reverse or contain adverse outcomes. Use cases are segmented by risk rather than technology label, with controls calibrated to consequence and operating context. We then define testing, monitoring, intervention rights and accountability, ensuring governance evolves as systems move from experimentation into embedded operational or decision-making roles.
The data and estimates presented are indicative and intended for illustrative purposes. Actual outcomes may vary based on each company’s specific context, market conditions, operating model, implementation choices, and the quality and consistency of execution, including actions undertaken by the client.
Keypillars
Explore the key pillars that define this capability and shape how we create focused, measurable business impact.
Technology exposure
Identifies operational, legal, ethical, security, and control risks created by AI, autonomous systems, and rapidly evolving technologies
Control boundaries
Defines where human oversight, testing, approval, monitoring, and intervention remain necessary across high-impact technology use cases
Adoption governance
Aligns experimentation and deployment with risk ownership, evidence requirements, escalation criteria, and acceptable-use boundaries
Do you understand the risks emerging as AI and autonomous technologies move deeper into your business?
Get in touch with our AI, autonomous and emerging technology risk team to assess exposures, failure modes, controls and governance requirements.
Get in touchStrategic Framework
Explore our Strategic Framework
Explore our strategic framework applied to page_title and discover which model we apply to help you achieve your goals and objectives.
Discover our framework01. Map technologies
Identify AI, autonomous systems, agents, models, interfaces, and emerging technologies relevant to enterprise exposure
06. Monitor evolution
Track capability changes, incidents, regulation, adoption patterns, and emerging risk signals across technology domains
05. Stress systems
Test high-impact scenarios involving model failure, autonomous behavior, adversarial activity, or technology dependence
02. Trace failure modes
Assess misuse, model error, autonomy, data leakage, unsafe behavior, control loss, and unintended system interactions
03. Evaluate exposure
Determine where technology risks affect customers, operations, decisions, regulation, security, or enterprise trust
04. Set guardrails
Define control boundaries, oversight, testing, approval, monitoring, and escalation requirements by use case
How we help
Define how AI and autonomous technologies should be governed according to the consequences, autonomy and failure modes of their actual use
We provide AI, autonomous and emerging-technology risk analysis across enterprise use cases and operating environments. The work can include risk classification, autonomy assessment, model and agent failure scenarios, human oversight, accountability, testing and monitoring frameworks. Outputs identify where emerging technologies create material new exposures, which applications require stronger controls and what governance, intervention rights and safeguards should apply as systems move from experimentation toward increasingly autonomous operational roles.
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AI risk assessment
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Autonomous system risk assessment
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Generative AI risk assessment
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Agentic AI risk assessment
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AI model risk management
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AI use-case risk classification
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AI control framework
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Human oversight design
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AI third-party risk
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AI data risk
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AI security risk
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AI regulatory risk
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AI bias and fairness risk
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AI reliability risk
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AI explainability risk
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Emerging technology risk assessment
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Emerging technology risk monitoring
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AI incident response
Explore our FAQs
Find answers to the most common questions about this service, including key features, processes, and practical considerations. Explore our FAQs for additional insights and guidance.
Assess reliability, accountability, data, security, human oversight and the consequences of incorrect or unintended system behavior.
Controls should reflect decision impact, autonomy, data sensitivity and how easily errors can be detected, corrected or reversed.
When systems can make consequential decisions or actions without sufficient human review, operating limits or reliable intervention.
Use scenarios, bounded experimentation and explicit assumptions while updating risk estimates as operational evidence develops.
Dependence can create exposure to model changes, outages, data handling, security and limited visibility into underlying technology.
Match oversight to consequence and uncertainty, with clear intervention rights and accountability for material decisions.
When evidence shows that material risks cannot be controlled within the organization's accepted operating boundaries.
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Editorial overview
Articles
AI risk is becoming enterprise risk
Why governance of autonomous systems must connect technology controls with operational consequences, accountability and business appetite.
Read articleRisk management when risks no longer arrive one at a time
How enterprises can connect emerging risks, vulnerabilities and stress scenarios to understand where exposures interact and amplify.
Read articleFocus
Operational risk emerges where everyday execution can produce material failure
Processes, people, systems and controls can create exposure through breakdown, error, dependency or weak management discipline.
Risk mitigation is a choice between reducing exposure, consequence or uncertainty
Different responses change economics, flexibility and residual risk in different ways and should be compared explicitly.
Strategic challenges
Which market movements could materially weaken enterprise economics?
The challenge is separating normal volatility from exposures capable of changing liquidity, margins or commercial viability.
Can the enterprise still make good decisions under severe uncertainty?
The challenge is preserving decision quality and coordination when information is incomplete and consequences are moving quickly.