Agentic AI is transforming wealth management by blending human expertise with intelligent automation, boosting advisor efficiency, client trust, and personalized financial outcomes

The wealth management industry faces a dilemma. On one hand, a major demographic shift comes as a large percentage of advisors near retirement. On the other hand, the rise of advanced self-service generative AI (Gen AI) agents raises questions about future relevance of human advisors. Investment firms must now consider whether to invest in a new generation of professionals or transition toward AI financial advisors and hybrid advisory models that promise operational efficiency, despite lingering concerns over client trust in non-human financial interactions.

Advisors in the U.S. from aging to training 

The financial advisor industry is experiencing a notable aging trend which has various known and un-known impacts on the industry. Given that 48% of relationship managers (a role synonymous with financial advisor) are expected to retire by 2040,¹ it can have a significant impact on investor relationship and trust, leading to a decline in efficiency and diminishing AUM.  

While aging remains to be a significant concern, it is accompanied with other worrying trends, a major wealth transfer arising from doubling of population aged 65 and above² and an army of digital advisors (at times dubious) promising returns. 

To address the impending shortage and ensure that investors do not get alluded by new age investment advisors, the industry needs to attract and train a significant number of new advisors. Over the next decade, over 100 thousand advisors are expected to retire and the replacement from new advisors falls short with a 72% failure rate to perform the job well.³  

Even when investment firms decide to invest in the above success areas, there remain notable differences in knowledge and experience between aging advisors and new entrants. Older advisors tend to possess what is often called 鈥渋ntelligence to sell,鈥� the ability to understand client needs, build trust, and craft persuasive, tailored advice based on years of relationship management. In contrast, younger advisors often excel in 鈥渇luid intelligence,鈥� which refers to the capacity to learn quickly, adapt to new technologies, and apply innovative approaches to problem-solving.⁴ Bridging this gap requires embedding mentorship into day-to-day work, supported by a willingness to adopt fluid intelligence or offer it directly to tech-savvy clients. In both cases, leveraging digital tools across the advisory journey can capture and document the 鈥渋ntelligence to sell鈥� before retirement and make it usable through advanced agentic AI in finance tools. 

Enhancing efficiency in financial advisory with specialized gen AI workflow agents

We are witnessing the emergence of AI systems capable of autonomous decision-making, designed to specialize in specific tasks and take action. This is achieved by implementation of agentic AI, a specialized AI bot for a specific task, in our case performing various steps in the financial advisory journey.⁵ 

These agents learn from past behavioral data, including client interactions and transactions, and deliver personalized, automated services. They help advisors maintain trust through genuine human-to-human connection while introducing what we call augmented advisory: a model that combines human judgement with AI support. Agents can provide real time dialogue assistance, generate financial analysis, execute routine tasks, and produce clear summaries. Together, these capabilities enable proactive, intelligent actions that improve client outcomes. 

Time is a scarce resource for advisors and it takes immense effort to personalize each report, manage client asks, while being effectively able to summarize and persuade clients. Especially for new advisors, it is difficult for them to cope with multiple client requests which requires experience and knowledge in handling clients, here specialized bots come to aid offering support with real-time operations, dialogue, and research. We provide you with a glimpse of few emerging industry examples.

Categorization of AI agents for financial services 

Marketing Agent: Identifies prospective clients by life stage and preference. Delivers timely, personalized messages across channels. Tracks return on investment, analyzes click throughs and conversions, and helps lower acquisition cost while increasing retention through data driven interactions.

Master Agent: Provides a single dashboard for the advisor to monitor client activity and agent output. Orchestrates specialized agents, allocates work, and measures value. Prompts the advisor for manual steps and proposes automation as it learns the workflow.

Operations Agent: Collects client details, validates documents, and triggers the steps to open or amend accounts. Supports compliance reporting, captures minutes, and handles routine administration. Improves accuracy and reduces cycle time.

Service Agent: Tracks prior actions and conversations, then sends timely updates to clients and advisors. Triggers other agents to prepare documents and surfaces priority items such as scheduled portfolio reviews. Manages task lists so critical work is handled on time.

Sales Advisory Agent: Supports advisors during client calls with prompts on planning topics, product fit, and required compliance language. Shares clear next actions with the client, including documents to gather, portfolio details, and recommendations. Flags market linked changes and helps the relationship manager act promptly.

Research Agent: Generates research based on client goals and recent discussions. Provides market and portfolio analysis on demand for advisors and, where appropriate, for clients. Pulls from approved data sources to support recommendations.

Call Agent: Understands caller intent and routes calls to the right agent or to the advisor. Reduces call time and improves first contact resolution. Sends real time nudges to the advisor and produces a short summary with key actions and follow up dates.

Snapshot of AI use cases in financial services

AI in finance services 鈥� specifically agentic AI 鈥� is changing advisory work. They deliver advanced analytics, personalized recommendations, automation of routine tasks, support for real time conversations, and help with content creation. AI agents can generate client reports, answer client queries, and surface market insights. They can also be tailored to niche tasks such as portfolio rebalancing. Together, these capabilities improve advisor efficiency and the client experience.⁶

Investing in future advisors 

The financial advisory industry in the U.S. is at a crossroads. To sustain growth and meet rising demand, firms should adopt agentic AI and related tools in ways that augment human judgment. This approach can bring experienced and new advisors together, raise service quality, and help clients achieve financial goals.

Ready to reimagine client engagement in wealth management?

乌鸦传媒 can be your end-to-end partner in harnessing Agentic AI to transform advisor-client interactions. We invite you to schedule a discussion to explore how our suite of Gen AI innovations and robust partner ecosystem can help elevate engagement and deliver intelligent, outcome-driven experiences.

As sustainability continues to evolve from a strategic ambition to a business imperative, the 2025 edition of A world in balance offers a profound reflection on where organizations stand today. It鈥檚 a moment of reckoning, where bold commitments must now be backed by credible and measurable action.

What stands out most in this year鈥檚 report is that the organizations see it as a driver of business value and a core future proofing strategy. The commitment remains strong,  but the pressure to demonstrate real progress is intensifying. Stakeholders, whether regulators, investors, or consumers, are no longer satisfied with distant net zero goals or glossy ESG reports. They want to see tangible steps, clear roadmaps, and evidence that sustainability is embedded in everyday operations.

This shift from ambition to accountability is reshaping how sustainability is perceived. It鈥檚 no longer just about compliance or corporate responsibility; it鈥檚 about resilience, innovation, and competitive advantage. What鈥檚 driving this transformation is not only the recognition sustainability鈥檚 business value, but also the growing urgency of climate impacts and the need to future-proof enterprises.

Organizations that treat sustainability as a business value driver  are already seeing returns, not just in cost savings or operational efficiency, but in brand loyalty, market differentiation, and long-term viability.

Yet, the report also reveals a troubling disconnect. Many organizations believe they are well prepared for climate risks, but their actions suggest otherwise. Planning is abundant; execution is scarce. Infrastructure upgrades, supply chain shifts, and product redesigns remain limited. This gap between perceived readiness and actual resilience is dangerous, especially as climate impacts become more frequent and severe.

Technology, particularly AI, is playing a growing role in enabling progress.  AI supports faster ESG reporting, smarter resource management, and predictive modeling for climate scenarios. Yet, its environmental footprint demands greater awareness. The enthusiasm for generative AI is tempered by concerns about energy consumption, water use, and e-waste. Responsible deployment of AI, balancing innovation with environmental stewardship, is now a critical part of the sustainability conversation and shouldn鈥檛 come at the planet鈥檚 expense.

Internal barriers also persist. Budget constraints, siloed operations, and fragmented data systems continue to slow progress. Externally, geopolitical tensions and economic volatility are diverting attention from long-term sustainability goals. These pressures are real, but they must not become excuses. The stakes are too high.

Perhaps most concerning is the decline in sustainability maturity. Fewer organizations are leading the charge, and progress on key areas like biodiversity and water stewardship is slipping. This regression is concerning. Sustainability is not a trend, it鈥檚 a transformation. It requires consistent effort, cross-functional collaboration, and a willingness to rethink business models from the ground up.

The report outlines clear business value drivers and actionable recommendations: focus on near-term deliverables, build consumer trust through transparent messaging, strengthen data infrastructure, and deploy AI responsibly. These are not just best practices, they are survival strategies in a world where environmental, social, and economic systems are increasingly intertwined.

As a sustainability leader, I believe we are at a pivotal moment. The path forward is not easy, but the direction is clear. We must move beyond promises and start proving our progress. We must embed sustainability into every decision, every process, and every product. And we must do so with urgency, integrity, and courage.

Let鈥檚 stop treating sustainability as a future goal and start treating it as today鈥檚 priority. Whether you’re a business leader, a policymaker, or a consumer 鈥� ask the responsible questions, demand transparency, and push for action.

The time to act is now. Let鈥檚 build a world in balance, not just in vision, but in reality.

Artificial intelligence is transforming industries, but it鈥檚 also reshaping the cyber battlefield. While adversaries are experimenting with AI to accelerate attacks, forward-looking enterprises are turning AI into their most powerful defense.

This is the essence of continuous protection 鈥� layered defenses across IT, OT, and cloud that are always adaptive, always learning, and always on.

The challenge: Faster, smarter threats

Traditional defenses struggle to keep pace with:

• AI-enabled phishing campaigns that mimic trusted voices
• Automated vulnerability scanning that identifies weaknesses at scale
• Adaptive malware that changes behavior to evade detection.

These attacks move at machine speed, leaving manual defenses outpaced.

The opportunity: AI-enabled protection

AI doesn鈥檛 just keep up 鈥� it helps organizations stay ahead. Benefits include:

• Real-time anomaly detection across vast telemetry
• Automated incident response to neutralize threats faster
• Augmented SOC teams, freeing human talent for higher-value analysis.

Combined with layered defenses, AI enables enterprises to turn protection into a proactive capability.

乌鸦传媒鈥檚 role

We help clients strengthen protection by:

• Deploying AI-driven defenses across IT, OT, and supply chains
• Designing architectures that combine automation and human expertise
• Accelerating detection and response through global Cyber Defense Centers.

Bottom line: Protection is no longer static 鈥� it must be continuous, adaptive, and intelligent. With AI as a force multiplier, organizations can defend smarter, not just harder.

Learn how 乌鸦传媒 helps enterprises build AI-powered protection across IT, OT, and cloud:听/ch-en/services/cybersecurity/continuous-protection/

There鈥檚 no question that generative AI and AI agents have already changed the world significantly in the last six months, captivating our attention with images of futuristic skyscrapers swathed in plants and footage of humanoid robots competing in sporting events and scientific environments.

Innovations in Gen AI tools are also quietly but rapidly revolutionizing how businesses operate, anticipate disruptions, and adhere to security and regulatory requirements.

For the majority of organizations, one of the most powerful and practical benefits of using Gen AI seems mundane at first glance: its ability to dramatically reduce the time people spend on critical 鈥� but boring 鈥� tasks. 乌鸦传媒 in partnership with ServiceNow make gaining the benefit easy to accomplish, with an integration platform that unleashes the advantages of Gen AI, while creating time for other value-added activities.

Automate processes, eliminate errors, and strengthen compliance

Gen AI tools and AI agents can dispatch tedious and costly routines in a fraction of time it would take a massive team, effectively eliminating backlogs in the process. They can also eliminate the user and quality errors that often come from performing rote work. AI Agents can also proactively monitor changes in regulations, assess their impact, and recommend updates 鈥� ensuring continuous alignment with evolving standards. And there are more meta and detail use cases for improving security and compliance underway.

Imagine the time and labor savings of making analysis spreadsheets redundant, instead integrating datasets in ServiceNow modules, making them accessible to everyone in an organization. Or swiftly summarizing lengthy security and compliance documents and highlighting key points to make it easier to understand regulatory requirements. For example, analyzing the requirements of the EU鈥檚 Digital Operational Resilience Act (DORA) which came into force in January 2025, and using AI to get suggestions on how to update your policies and procedures. Getting more granular on this front, Gen AI tools can detect outdated security and compliance documentation globally, and also automate the process of ensuring consistency in compliance-related documentation like terminology, style, formatting, and language. This helps organizations stay ahead of security and compliance requirements and avoid penalties in the design phase.

These are just a few examples of how organizations can create a better lens on enterprise resilience and regulatory compliance through technology. The smart approach to widespread adoption of Gen AI is to take a rational, step-by-step approach, selecting a particular process for security and compliance , mapping out the essential high-level activities, and identifying more specific use cases to test scenarios for Gen AI, AI agents, RPA and, of course, the remaining human factor. Then after the design and build, organizations can continue to iterate and improve.

Leverage large-scale data analysis

But where to start? Ensuring data quality and consistency is a game changer that can create a competitive advantage for companies. And Gen AI鈥檚 ability for large-scale data analysis makes it easy to tackle data cleaning and improvement, which used to be an expensive and time-consuming task. This opens up exploration of how companies can embed data-driven intelligence into their end-to-end operations.

That could include crucial compliance tasks such as comparing existing policies and procedures against requirements to identify gaps and areas for improvement. Or analyzing feedback from stakeholders (e.g., legal, auditors) about the interpretation of a new regulation and incorporating relevant changes into compliance documentation, which in turn can be used to create an audit trail of scenarios and detail the choices.

Monitor operations in real-time and predict patterns

When Gen AI tools analyze vast volumes of business data, they can recognize patterns, detect deviations from the norm, and provide actionable insights to improve decision-making processes. This enables organizations to continuously monitor aspects of their operations in real-time while also using machine-learning algorithms to enhance problem-solving.

Look no further than the recent shifts in global tariffs that have disrupted supply chains, requiring companies to pivot quickly. AI agents can analyze historical data and identify trends that may indicate potential disruptions, to predict delays or shortages and suggest alternative suppliers or routes, mitigating risks before they escalate. Using that same type of historical data, retailers can predict changes in customer demand based on historical sales data, seasonal trends, and market conditions. This helps with optimizing inventory levels, reducing overstock and stockouts, and improving customer satisfaction. The same applies for the meta use cases for security and compliance, as companies can prevent resilience issues rather than fixing them later.

Maintain human-led expertise and oversight

Generative AI tools leverage machine learning algorithms to create outputs that mimic human creativity and problem-solving abilities. But unleashing Gen AI doesn鈥檛 mean there aren鈥檛 any guardrails. Human collaboration between AI experts and domain specialists is crucial for expertise, oversight such as regular auditing and monitoring of AI output, and to maximize the benefits of these tools. Organizations should also invest in training programs to upskill employees and foster a culture of continuous learning.

While technological advancements in Gen AI initially leapt ahead of regulations, ethical considerations such as data privacy, bias, and transparency have caught up. Although the US loosened regulatory barriers to AI innovation in January, new measures promoting the responsible design, development, and deployment of AI have been introduced by the EU, as well as Canada and China. Individual organizations are also increasingly following a framework of measures referred to as TRiSM 鈥� trust, risk, and security management 鈥� baked into AI platforms.

Improving operational resilience and regulatory compliance might not be headline-grabbing news, but it鈥檚 often these seemingly small shifts that can make the biggest collective impact. Even the most eye-catching skyscraper would topple without its underpinning of concrete and steel. In the same way, Gen AI tools and AI agents can help organizations shore up their foundations, build strength in a holistic approach to maintain business continuity, and minimize the impact of unexpected events.

Supply chains are no longer just operational backbones; they are the beating heart of global business. We live in a digitally interconnected world with supply chains operating just-in-time. From pharmaceuticals to automotive to consumer goods, supply chains are where innovation, efficiency, and sustainability ambitions come to life. But they are also where vulnerabilities now concentrate. In an interconnected world, there are interconnected risks.

As industries digitize at pace 鈥� integrating AI, cloud platforms, and connected ecosystems 鈥� their supply chains have become prime targets for cybercriminals. Attacks are no longer isolated disruptions; they ripple across industries, markets, and even national economies.

乌鸦传媒鈥檚 latest research confirms what we see in the field every day:听cybersecurity is now the number-one concern for supply chain leaders, cited by 74% of executives, outpacing cost pressures and digitization challenges[RA1]听.听This marks a pivotal shift.

Why supply chain cybersecurity is a market imperative

Between 2019 and 2022, supply chain cyberattacks rose by an alarming 742%. Industry reliance on third-party vendors, SaaS ecosystems, and globally distributed partners has created vast and complex risk surfaces. Organizations are as safe as their weakest link, and many times the biggest weakness is in a third party.

Consider the impact:

• A manufacturing shutdown caused by a ransomware attack can stall production for weeks, with ripple effects across automotive or electronics industries.
• A pharmaceutical supplier breach can jeopardize both regulatory compliance and patient safety.
• A logistics provider hack can paralyze retail operations during peak season.

Despite this, only 9% of organizations monitor cybersecurity across their entire supplier base. That leaves blind spots, especially in Tier 2 and Tier 3 suppliers, that attackers are quick to exploit.

Visibility is now the competitive differentiator

In an interconnected economy, visibility is the new currency of trust. Our research shows 79% of executives worry about their lack of cybersecurity visibility in global supply chains.

For industries where trust defines the brand, whether ensuring product authenticity in luxury goods or safeguarding patient data in healthcare, visibility gaps are no longer tolerable. Forward-looking organizations are now investing in:

• AI-driven monitoring and analytics for real-time supplier risk insights
• Collaborative cybersecurity frameworks that extend beyond Tier 1 vendors
• Integrated resilience planning that balances security with sustainability and agility goals.

Cybersecurity as an industry growth driver

Encouragingly, we also see progress. 73% of organizations have deployed end-to-end cybersecurity tools, and nearly half report radical transformation as a result.

For leaders, this is more than protection, it鈥檚 a growth story. Cybersecurity-enabled supply chains are:

• More agile, adapting faster to geopolitical shocks
• More trusted, earning customer and regulatory confidence
• More sustainable, by ensuring continuity even under disruption.

Resilience must now be treated as core dimensions of the supply chain strategy.

Five imperatives to future-proof supply chains

Based on our market research and client work, we recommend organizations focus on:

1. Embedding cybersecurity controls across all supply chain tiers, not just Tier 1
2. Partnering with cybersecurity specialists to tailor strategies by industry
3. Leveraging AI and Gen AI to enhance visibility and accelerate detection
4. Building cybersecurity into supplier contracts for accountability
5. Educating internal teams and suppliers to strengthen the human defense layer.

The bigger picture: Agility, sustainability, and AI

Industry leaders know that the future supply chain must balance cybersecurity, agility, and sustainability. These three priorities are converging into one strategic agenda.

Organizations that succeed will not only withstand disruptions, but they will also turn resilience into a market advantage.

From risk to resilience

Supply chain cybersecurity is no longer a technical challenge; it is an industry-wide business challenge. The risks are escalating, but so are the opportunities for those who act with urgency.

Our research provides deep insights into how organizations across industries are approaching this challenge. To learn more, or to explore how we can help you secure your supply chain for tomorrow鈥檚 threats, connect with our experts.

Salesforce鈥檚 plan to acquire Informatica will unleash a powerful trifecta of technologies, making it easier for organizations to benefit from a new, human/digital hybrid workforce

Deploying autonomous AI agents at scale is poised to transform business operations. Enterprises across all industrial sectors are eager to leverage these agents 鈥� working alongside humans 鈥� to boost productivity, efficiency, and customer experience. However, to unlock the full value of the digital labor opportunity, it鈥檚 imperative that companies empower AI agents with broad access to organizational tools and data 鈥� and do so without sacrificing security or incurring massive integration costs. The recently announced plan by Salesforce to acquire Informatica is good news for enterprises as they address this significant challenge.

Overcoming deployment barriers

In Rise of agentic AI: How trust is the key to human-AI collaboration, the 乌鸦传媒 Research Institute projects AI agents could generate up to $450 billion in economic value by 2028, through revenue uplift and cost savings across the surveyed countries.

But from their interviews with 1,500 executives, 乌鸦传媒 researchers discovered only 14 percent of organizations have moved beyond pilot projects to partial or full-scale deployment of these agents. Trust is a key barrier, as those surveyed cited ethical concerns, lack of transparency, and a limited understanding of agentic AI capabilities. But organizational readiness 鈥� including the creation of an effective governance system 鈥� is also hampering secure, scalable deployments.

Salesforce is one of the leading technology companies helping enterprises to deploy autonomous agents, and it is taking steps to help organizations overcome these barriers. In the spring of 2025, Salesforce announced a major play to strengthen its capabilities in the form of an $8 billion deal to acquire Informatica. As a longtime Salesforce partner, 乌鸦传媒 believes this is an important development that will enable Salesforce to deliver AI agents that can operate with intelligence, context, and confidence across the modern enterprise.

Key assets, working together, will enable this.

Agentforce. The Salesforce approach starts with Agentforce 鈥� the company鈥檚 flagship AI agent platform. Agentforce integrates natively with an organization鈥檚 existing applications, data, and business logic so agents can securely take action across the enterprise 鈥� handling complex tasks automatically while working in tandem with human teams.

Early deployments of Agentforce have already demonstrated substantial gains. For example, companies using Agentforce have cut customer service case handling time by double-digit percentages and allowed AI agents to autonomously resolve the majority of simple support requests. At scale, these AI agents handle high-volume, repetitive tasks such as answering FAQs, processing form submissions, or triaging support tickets. This frees up human agents to focus on higher-value work.

Salesforce recently enhanced this solution with the Agentforce Command Center, which enables business leaders to monitor and control their AI agents鈥� activities in real time. This level of visibility and governance addresses critical hurdles to scaling AI agents across the enterprise.

Anthropic鈥檚 Model Context Protocol. To enable its AI agents to access diverse systems, tools, and data across the client鈥檚 organization, Salesforce has embraced Model Context Protocol (MCP) 鈥� an open integration standard from Anthropic. This addresses a major pain point in the AI deployment process 鈥� namely, that custom integrations, each using custom code and requiring unique maintenance processes, do not scale.

MCP eliminates the need for developers to build a custom integration every time agents need to connect to external systems, APIs, databases, and services. The result is faster development, lower integration costs, and the freedom to mix-and-match AI models with a wide variety of tools and data sources. MCP鈥檚 model-agnostic open standard 鈥� often referred to as 鈥渢he USB-C of AI鈥� 鈥� means businesses avoid vendor lock-in and encourages a broad ecosystem of integration. Salesforce鈥檚 decision to adopt MCP enables Agentforce agents to seamlessly interface with a vast and growing universe of enterprise systems and cloud services 鈥� without requiring custom code, and without compromising on security.

MCP-native agents. When Salesforce released Agentforce version 3 in mid-2025, it introduced built-in MCP interoperability. What鈥檚 more, more than 30 launch partners provide MCP integrations 鈥� spanning cloud platforms (AWS, Google Cloud), content and collaboration tools (Box, Notion), payments (PayPal, Stripe), data and AI services (IBM, Writer), and more. This means Agentforce can accomplish a vast variety of tasks.

The Salesforce vision is clear: to enable an open ecosystem in which Agentforce-powered AI agents can plug-and-play into business applications and services, regardless of source and with minimal setup. This represents a major leap forward in what these agents can do autonomously.

The Informatica toolset. The effectiveness of AI agents 鈥� no matter how intelligent or well integrated 鈥� is only as good as the data on which they operate. With its plan to purchase Informatica, Salesforce will acquire important enterprise-grade tools for data integration, data quality and cleansing, master data management, granular data governance and privacy controls, and real-time data observability across complex hybrid and multi-cloud environments.

From a business perspective, this acquisition will inject a powerful dose of data integrity, context, and governance into Salesforce鈥檚 AI ecosystem, ensuring Agentforce agents have access to clear, trusted, and actionable data. Enterprises will be able to track where data comes from, how it鈥檚 transformed, and how it鈥檚 used. Organizations will avoid mistakes due to using outdated or inconsistent data. And companies will deploy AI agents, confident that they will not run afoul of regulatory requirements or privacy laws.

A powerful trifecta

Agentforce, MCP, and Informatica form the three pillars of an AI-driven enterprise: an agent platform to act, a protocol to connect, and a data ecosystem that informs. Organizations that leverage all three will be well positioned to achieve unprecedented levels of automation and insight 鈥� transforming their enterprise into a smarter, more agile business in which humans and agents can collaborate seamlessly to enrich customer experiences and drive growth.

For many enterprises, this will make the vision of autonomous agents a practical reality. AI agents, working fluidly across systems, will handle routine processes in customer service, sales, marketing, IT, and finance. This digital workforce will answer questions, generate reports, update records, and flag issues 鈥� autonomously, and in real time. This will free up humans to focus on strategic, creative, and relationship-oriented work 鈥� activities at which humans excel 鈥� while supervising AI as needed.

乌鸦传媒 is excited by this trifecta and looks forward to working with its Salesforce clients to enable the ongoing value opportunity agentic AI represents. As a Salesforce partner for 17 years and one of the company鈥檚 global top five strategic partners, 乌鸦传媒 offers its clients expert knowledge of the Salesforce platform, the experience of more than 3,000 AI specialists and 50,000 AI-enabled engineers, strong integration capabilities, and sector-specific expertise in multiple industries. Assets include the 乌鸦传媒 Agentforce Factory 鈥� a hub for clients to explore real-world applications through interactive demos, hands-on training, and expertise guidance.

For more information, please contact: andy.forbes@capgemini.com

Security leaders thought zero trust was important before? Welcome to the AI century, where artificial intelligence (AI) isn鈥檛 just accelerating the adoption of zero trust, but is becoming the essential technology for outsmarting ever-more sophisticated cyber threat actors.

The rise of AI and zero trust 

In today鈥檚 digital world, cybersecurity challenges often seem insurmountable. As threats grow in complexity and scale, organizations are rethinking security strategies. Zero trust 鈥� built on the principle of 鈥渘ever trust, always verify鈥� 鈥� has become the gold standard for enterprise security. Yet, research suggests that only around 30 percent of Fortune 500 companies have a defined zero trust roadmap. For those still on the sidelines, the time to act is now.  

The urgency has only intensified with the rise of artificial intelligence. Since the launch of ChatGPT in late 2022, AI adoption has been rapid and widespread. What began as a consumer phenomenon is now reshaping enterprise security. AI is no longer an add-on 鈥� it is becoming the core enabler of zero trust, helping organizations scale defenses and respond to threats in real time.  

CISO鈥檚 perspective: Continuous transformation in security 

This shift is most significant for chief information security officers (CISOs). Already under the pressure to protect enterprise assets, while enabling digital transformation, CISOs have seen cloud adoption, hybrid work, and new regulations stretch traditional defenses to their limits. Now, the acceleration of AI adoption is adding another layer of urgency. CEOs are driving the vision for AI use cases while CISOs are responsible for delivering on that aspiration.

In this context, zero trust is no longer a theoretical 鈥� it鈥檚 the future state for enterprise security. The real question is how AI can help get them to the zero trust future. Artificial intelligence has shifted the cybersecurity landscape in fundamental ways. At its core, zero trust requires granular access controls, continuous authentication, and real-time monitoring. Historically, scaling these principles across a vast enterprise was a daunting task. AI changes the equation by automating detection, response, and analysis, making it possible to enforce zero trust at every level (i.e., endpoints, applications, infrastructure). AI delivers speed, but more importantly, it helps enterprises do security faster.  

Financial services as a proof point

The financial services sector illustrates this shift clearly. With valuable data, critical operations, and strict compliance requirements (PCI DSS, SOX, GDPR), financial institutions face constant attack. For them, zero trust is not optional, it is foundational. Here AI is proving its worth. It empowers banks, insurers, and investment firms to implement adaptive identity verification, anomaly detection for fraud prevention, and rapid incident response at scale. For example, AI algorithms can flag suspicious transactions across millions of accounts in real time, while continuous authentication ensures that only legitimate users gain access to critical systems. Investment firms are integrating AI insights into zero trust frameworks to detect anomalies faster and reduce fraud losses.

The lesson is clear: where the stakes are highest, AI and zero trust together are delivering tangible results. Other industries can draw confidence from this example.

Practical considerations for enterprises

For CISOs and enterprise clients, the journey toward AI-powered zero trust doesn鈥檛 have to be overwhelming. It begins with a few practical steps:

• Assess your security architecture: identify where AI can close gaps or enhance scalability.
• Establish clear AI policies: ensure safe usage while meeting GDPR and standards such as ISO/IEC 42001.
• Upskill your teams: build AI knowledge and monitoring expertise into cybersecurity functions.
• Partner strategically: few enterprises can operationalize AI for zero trust alone; trusted partners accelerate progress.
• Continuously optimize: as attackers evolve, so must AI models and security controls.

AI as the essential technology for zero trust 

The convergence of AI and zero trust marks a pivotal moment in the evolution of cybersecurity. As threat actors become more advanced, the tools to defend against them must be equally sophisticated. AI isn鈥檛 just enhancing zero trust 鈥� it鈥檚 enabling it at scale, making previously unattainable levels of security possible. For CISOs and security professionals, embracing AI is no longer optional; it鈥檚 imperative. By adopting AI safely, embedding it into zero trust strategies, and striving for operational excellence, enterprises can stay ahead of the curve and safeguard their digital future with confidence.  From strategy to scale 鈥� discover how 乌鸦传媒鈥檚 Gen AI security suite accelerates your zero trust journey

Post-quantum cryptography (PQC) migration is often presented as a clean, linear process: inventory your cryptographic assets, prioritize them, build a roadmap, and migrate one by one.

In practice, it鈥檚 rarely that simple. You might think you鈥檝e got crypto discovery covered 鈥� until you realize it鈥檚 a highly-dimensional, deeply-embedded, and often undocumented part of your infrastructure. Or perhaps you鈥檙e the one championing the cause, but you lack the mandate, budget, or executive support to move forward. Maybe there are just too many other priorities.

So how do you get started? The answer: start small, but think big. PQC migration doesn鈥檛 have to be overwhelming. You can start your PQC migration journey by focusing on four key areas. These aren鈥檛 sequential steps. You can begin where it makes the most sense for your business 鈥� and build momentum from there.

1. Develop readiness and capability

In my experience, most organizations already have someone who understands the urgency of PQC. If you鈥檙e reading this, that person might be you. But even with technical know-how, the biggest challenge is often organizational: no budget, no priority, and no clear mandate. Even when a small team is capable, implementing cryptographic upgrades across dozens of DevOps teams 鈥� each with its own backlog 鈥� is a different story. Ironically, the hardest systems to migrate may not be the crown jewels, but the forgotten legacy systems no one wants to touch. So where do you begin?

Before pushing for a full-on roadmap of strategy, create a short, compelling internal document that outlines the urgency and opportunity. Give people something to rally around. Identify what the quantum risk means to your industry and company, and formulate it in the language that resonates with leadership and practitioners. You may find it helps to get experts on board, too.

2. Rethink inventory: it鈥檚 not a prerequisite, it鈥檚 a process

There are two common misconceptions:

• One tool will give you full visibility.
• You need a complete inventory before you can start migrating.

Neither are true. Cryptographic assets have many dimensions. Their owner may be internal or vendor-supplied. They may be deployed on-premises or in the cloud. They may be legacy, active, or still in development. They may have different levels of exposure, risk characteristics, scope, and more. No single tool will capture everything. Doing so requires a combination of TLS traffic analysis, filesystem scans, cryptographic bills of materials (CBOMs), questionnaires, and more. Each method has its strengths and blind spots. Don鈥檛 expect one solution to be the holy grail, but instead, start where you already have visibility and build from there.

Second, cryptographic inventory is not a prerequisite. In one case, I worked with an organization that prioritized TLS traffic, only to find that 99% of assets were marked high priority. Denoting everything as a high priority nullifies the need for prioritization. Additionally, cryptographic inventory is never going to be finished, so waiting for it to be done won鈥檛 get you far. It鈥檚 not a one-time task either. It鈥檚 a continuous process that鈥檚 essential for prioritization, compliance, and incident response.

3. Begin migration where it makes sense

Another common misconception is that the technology isn鈥檛 there yet. It鈥檚 actually a nuanced picture. PQC algorithms were standardized in mid-2024 after years of global vetting. Since then, vendors have rapidly integrated PQC into OpenSSL, TLS, HSMs, and other products. Nonetheless, scrutiny continues. In 2022, side-channel vulnerabilities were found in Falcon, one of the PQC algorithms, after five years of development and vetting.  It鈥檚 a reminder that algorithms deemed secure may one day face vulnerabilities. Nonetheless, the same is true for any cryptographic algorithm. This doesn鈥檛 mean they aren鈥檛 secure.

There are also still wrinkles in software packages implementing PQC. For example, when testing BouncyCastle, we found it lacked native PQC support, requiring C-based implementations and custom compatibility layers. We also found a lack of standards, forcing us to define custom nomenclature. This raised an important question: would it have been easier to wait a few years for the technology to mature?

For some systems, perhaps it would have been. For new technologies and non-critical systems, one could wait until more documentation is available and more experience is available. But you can鈥檛 wait forever. By choosing not to wait, early adopters gain experience, influence standards, and uncover risks sooner.

There are also plenty of smart, low-risk actions you can take in the short term 鈥� steps that make sense regardless of where you are in your PQC journey. For example, organizations can adopt best practices related to automated key and certificate management and rotating keys regularly or automatically. They could also upgrade to TLS 1.3 or design modular, update-ready systems. Perhaps the most sensible thing is to look at what鈥檚 already on the roadmap. If a system is being upgraded, ensure it鈥檚 done with PQC and crypto agility in mind.

4. Engage your ecosystem and dependencies

PQC migration is an organizational problem, full of complex dependencies. You depend on vendors who may not yet support PQC, policies with customers that may assume cryptographic lifetimes of decades, and standards that vary by region. You may want to consider negotiating terms with your vendors but lack the capacity and knowledge to do so effectively. You may want to align with regulators and governments, but ambiguous and diverging polices complicate the matter. How should you get started?

Foremost, start the conversation. Talk to your peers. Collective pressure is more effective when negotiating with vendors or influencing standards. Talk to your vendors. Include crypto agility clauses in contracts 鈥� especially during renewals 鈥� and talk to policy owners to challenge assumptions about key lifetimes and update cycles.

Conclusion: action over perfection

PQC migration is complex, and it鈥檚 hard to see the full picture from the start. But one thing is clear: inaction is not an option. The good news? You don鈥檛 need to solve everything today. No-regret moves are possible. Rather than overcomplicating cryptographic discovery, start with existing visibility and build a more complete inventory from there. Oversee the migration roadmap. If a system is being upgraded, ensure it鈥檚 done with PQC in mind and adopt best practices around crypto agility. Finally, engage your ecosystem and initiate discussions with peers, vendors, and policy makers. Whatever you do, lean towards action instead of perfection. The time is ticking as quantum computers mature.