How AI helps change how leaders sense, understand, and adapt to change in real time

Every transformation is designed to create value. Yet many organizations face challenges in realizing the full return on their investment and may encounter some speed bumps along the way. Technology isn’t necessarily the issue. Rather, organizations may lack timely insight into how people are experiencing change.

The information to support these insights exists, but much of it goes unseen or unused. Instead, many organizations continue to rely on periodic surveys, workshops, and retrospective reporting to understand adoption of transformation programs, technologies, processes, and so on. By the time resistance or confusion crops up during a change transformation, time and momentum may have been lost.

This is where change management is evolving. AI is making it possible to move beyond periodic assessments toward what I call continuous organizational sensing—the ability to continuously sense, interpret, and adapt to how people are experiencing change. Rather than relying on snapshots in time, leaders can develop an ongoing understanding of organizational dynamics and make better informed decisions throughout a transformation.

The challenge with change management isn't a lack of data

Most organizations aren’t short of information. They’re short on timely, actionable insights. Change management is one area where this is especially true.

Every transformation generates data, yet much of it remains hidden in plain sight. Collaboration platforms, help desk tickets, training feedback, meeting transcripts, and open-text comments all provide valuable signals about how change is unfolding. Too often, however, these sources are overlooked, analyzed only after the fact, or left untapped because of legitimate privacy and sensitivity considerations.

Traditional change management approaches create blind spots because information is often:

- Delayed

- Fragmented across systems

- Subject to individual interpretation

- Manually analyzed after the fact

Consider a bank rolling out new digital capabilities across its branch network. Traditional measures such as training completion and communications engagement may suggest the transformation is progressing well. However, those measures don't necessarily show whether bankers are using the tools effectively in customer conversations.

By identifying adoption patterns early on, frontline feedback and business outcomes can reveal where employees are struggling with fragmented access, competing priorities, or a lack of reinforcement. This gives change leaders the insight to intervene before those barriers become embedded in day-to-day behavior.

Reasonably, many change leaders are still building confidence in how to access, interpret, and use growing volumes of information responsibly. As AI becomes more integrated into transformation, data literacy is becoming a core capability for change professionals. Understanding what data sources are available (even if fragmented), what data is allowed to be used, and what the data is or isn’t telling us, are essential to making informed decisions.

AI impacts how we understand change—not why we lead it

AI doesn’t replace change management. It strengthens it. By analyzing patterns across large volumes of structured and unstructured information, AI enables continuous organizational sensing. Instead of asking change leaders to manually review thousands of comments, conversations, and support requests, AI helps surface meaningful insights that would otherwise remain hidden.

This is more than another technology capability. It represents a shift in how organizations understand change. Rather than relying primarily on retrospective reporting, change leaders can continuously sense what is happening across the organization, interpret emerging patterns, and adapt their actions before challenges become barriers to adoption.

Instead of asking, "How did the project go?" organizations can begin asking, "What is happening today?"

This shift enables change leaders to identify:

- Emerging resistance before it slows adoption

- Changes in stakeholder sentiment

- Opportunities to improve communication clarity

- Adoption barriers across different groups

- Hidden champions who can help accelerate change

When these insights are available in real time, organizations can intervene earlier and engage more effectively. This improves the likelihood of achieving their transformation objectives, while keeping costs low and productivity stable.

There are tools to support this. Take CGI’s dedicated change management assessments, for example, which can bring together data outputs into a single view, including fragmented change data, to show readiness, adoption, and risks.

The real opportunity is better decision-making

Technology alone isn’t the competitive advantage. The competitive advantage comes from combining AI-generated insights with experienced leadership.

Organizations that embrace continuous organizational sensing can make decisions with greater confidence because they have a clearer understanding of how change is unfolding across the business. Greater transparency also helps build trust by making it easier to explain why decisions are being made and where additional support is needed. This transparency provides leaders with more confidence when they need to act and sometimes make uncomfortable decisions.

As organizations strengthen training in both data literacy and change literacy, they create an environment where insight becomes part of everyday decision-making rather than an activity reserved for project milestones.

Continuous organizational sensing isn’t about replacing intuition. It’s about complementing experience with evidence, enabling leaders to make better decisions at the moments that matter most.

Where human judgment matters most

AI can recognize patterns at remarkable speed and scale, but insight alone doesn't lead people through change. Human judgement is essential to understanding what those patterns mean and determining how to respond.

Building trust, creating psychological safety, demonstrating empathy, and providing meaning during uncertainty are uniquely human aspects of change leadership.

From my experience, one principle continues to stand out: successful transformation isn’t about implementing change for its own sake. It’s about helping people navigate change in ways that are meaningful, practical, and sustainable.

This is why I see AI and human judgement as complementary. AI can help us see where attention may be needed. People bring the context, experience, and empathy to decide what action is needed.

As continuous organizational sensing evolves, human oversight and leadership are how data will be used responsibly and transparently.

The future of change management

The future of change management isn’t AI-driven. It’s evidence-driven.

Organizations that succeed won’t necessarily be those with the most advanced technology. They’ll be those that develop the capability for continuous organizational sensing and use AI to better understand how people are experiencing change while relying on leaders to provide judgment, empathy, and direction.

Technology can reveal patterns. People give those patterns meaning. That combination enables organizations to respond earlier, adapt faster, and realize greater value from their transformation investments.

In the end, the competitive advantage isn’t AI itself. It’s the ability to continuously sense, interpret, and adapt human behavior throughout a transformation.

If you're exploring how AI can strengthen change management in your organization, I'd welcome the opportunity for a conversation. I also encourage you to learn more about CGI's work in the areas of change management and artificial intelligence.

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Why bank-grade settlement is the missing layer in tokenized finance

In the first part of this series, Digital money: Not all tokens are created equal, I wrote about why stablecoins, tokenized deposits and central bank digital currencies (CBDCs) shouldn’t be treated as interchangeable forms of digital money.

Stablecoins have demonstrated demand for always-on value transfer, while tokenized deposits give banks a way to make commercial bank money programmable without giving up the deposit model. CBDCs, meanwhile, could provide a digital form of central bank money for settlement.

But making money programmable within one institution is only part of the challenge. The harder question is what happens when that money needs to move between institutions and how the transaction is ultimately settled.

This is where tokenization meets settlement.

Tokenized deposits: Useful, but limited

Tokenized deposits allow banks to introduce programmability while retaining the underlying deposit relationship. But their limitation becomes clear when money needs to move beyond the issuing bank. Bank A's tokenized deposit isn’t automatically Bank B's money.

Across banks, the receiving institution requires settlement before it can safely credit its customer. This is why tokenized deposits without tokenized settlement are useful but incomplete. They can digitize and program a bank’s own deposit ledger. However, they don’t, alone, solve the interbank settlement problem.

The missing layer: Tokenized settlement

Across banks, the neutral settlement asset is central bank money. In a tokenized financial system, that may mean tokenized reserves or wholesale CBDCs. The customer-money leg may be a tokenized deposit; the settlement leg should be central bank money.

Common transaction legs and their purpose

This table describes different banking transaction legs.

This layered model matters because the financial system doesn’t simply need tokens that can move. It needs debit, settlement and credit to complete together.

Initiatives such as Project Agorá from the Bank for International Settlements Innovation Hub are important because they explore this combination of tokenized commercial bank deposits with tokenized wholesale central bank money on shared programmable infrastructure.

The significance isn’t simply tokenization. It’s the coordination between commercial bank money and settlement money.

The token mint/burn problem is really a synchronization problem

Tokenized finance discussions often focus on mechanisms such as token minting and burning. The expensive and risky part, however, isn’t the computational act itself. The harder problem is synchronization. The system must ensure that debit + settlement + credit complete together.

Those movements must also reconcile with bank books, pass through the required controls and achieve legal finality. If one leg completes while another fails, the bank can be left with an operational, liquidity or legal problem.

Tokenization, therefore, doesn’t remove the need for trusted infrastructure. It changes where trust sits.

- Banks control bank money.

- Central banks control settlement money.

- A governed platform coordinates the transaction.

What this means for banks: Assessing the impact of digital currency

Digital money touches the entire banking model, not just payments. Banks need to understand how each form of digital money affects funding, liquidity, settlement, risk, controls and the customer experience.

The enterprise-wide impact of digital money

This table highlights how digital money could impact different areas of the bank.

This breadth is important. A digital-money initiative may begin as an innovation or payments program, but production deployment quickly becomes an enterprise banking issue.

Making digital money work: What to look for in a partner

A partner with both payments and technology expertise can help banks maximize the value of digital money in terms of usability, compliance, integration, resilience, auditability and business-need alignment. Look for these attributes in a partner:

- Strategy and readiness: Expertise in use case selection, regulatory impact, business case development and road mapping.

- Target operating model: Strong capabilities in the areas of onboarding, wallet controls, redemption, exception management and crisis playbooks.

- Architecture and integration: Solutions in core banking, payments, custody, token ledgers, APIs, ERP/TMS and data platforms.

- Risk and controls: Deep experience with anti-money laundering, sanctions, depeg monitoring, reconciliation, cyber resilience and legal finality controls.

- Data and reporting: Support for single liquidity views, legal classification, valuation, audit trail and regulatory reporting.

- Managed services: End-to-end monitoring, reconciliation support, incident response and operational resilience.

The market doesn’t need more proof that tokens can move. It needs proof that tokenized money can be governed, reconciled, audited, secured and integrated into banking infrastructure.

A pragmatic digital money roadmap for bank clients

Banks don’t need to solve the entire future-of-money architecture at once. However, they do need a clear view of where they stand and which capabilities should be built now.

Building a future-of-money architecture

This table lays out steps to take in building a future-of-money environment.

This gives banks a practical way to make progress without trying to solve the entire future of monetary architecture in a single program.

From tokenization to trusted settlement

Stablecoins have shown the demand for always-on programmable money. Tokenized deposits show that banks can respond without giving up the deposit model. CBDCs and tokenized reserves suggest a settlement anchor that may be needed for digital money to scale safely across institutions. The future of money isn’t just tokenized. It must be settled, governed and trusted.

If you'd like to discuss how your organization can prepare for the next phase of digital money—from tokenized settlement and interoperability to the operational capabilities required to support it—reach out to me for a conversation.

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As frontier AI models such as Anthropic's Claude Mythos and OpenAI's GPT-5.6 Sol continue to advance, much of the discussion has focused on cybersecurity operations: How quickly can vulnerabilities be discovered? How rapidly can exploits be developed? How should security teams adapt? These questions matter, but they’re only part of the leadership challenge.

In his blog, Defending at the speed of frontier AI: Lessons from the Fable shutdown and what to do in the next 60 days, Pierre Le Calvez outlines practical actions organizations can take now to strengthen preparedness and resilience. The next challenge is broader.

What happens when cyber risk becomes less about technology alone and more about an organization's ability to make decisions, coordinate action, and adapt at speed? Increasingly, that is the challenge frontier AI presents.

Pressure is expanding beyond the cybersecurity team

For years, cybersecurity leaders have focused on improving visibility through monitoring, threat intelligence, vulnerability scanning, and detection. Those investments remain essential, but frontier AI is changing where the pressure sits inside organizations.

As AI-assisted tools become better at discovering vulnerabilities, identifying weaknesses, and generating findings, the constraint increasingly shifts from discovery to decision velocity. Security teams may know about a vulnerability within minutes and understand its potential impact within hours. The harder question is whether the organization can decide, coordinate remediation, and implement mitigations quickly enough to keep pace.

In many organizations, the biggest delays no longer sit inside the security operations center. They sit in approval processes, competing business priorities, resource constraints, and fragmented ownership.

The bottleneck is increasingly organizational.

Why resilience matters more than ever

That is why resilience is becoming such an important differentiator. Organizations often think of resilience as recovery after a cyber incident, but it starts much earlier.

Resilience is the ability to absorb pressure, adapt to changing conditions, and continue operating effectively when the unexpected happens. In a frontier AI environment, resilient organizations tend to share several characteristics:

- Clear visibility into critical systems and dependencies

- Clear ownership and knowledge of key decision-makers

- Effective coordination across technology, security, operations, and business teams

- Regular testing of response processes

- A clear understanding of operational bottlenecks

Most importantly, resilient organizations can act quickly without sacrificing governance. That balance becomes more important as threat timelines continue to compress.

Governance is becoming a competitive advantage

Frontier AI is also introducing new governance questions. Organizations are increasingly dependent on external software providers, cloud platforms, open-source components, and AI services, while also introducing AI-enabled capabilities into business processes and operations. Together, these shifts create new forms of operational dependency and risk.

Boards and executive teams are increasingly asking questions such as:

- How do we govern AI adoption responsibly?

- How do we maintain appropriate human oversight?

- How do we manage AI-related third-party risk?

- How do we balance innovation and pace with resilience?

- How do we make decisions quickly while maintaining accountability?

These aren’t purely technology questions. They’re leadership questions.

The organizations adapting most effectively aren’t necessarily creating entirely new governance structures. Instead, they’re evolving existing risk, security, and resilience frameworks to reflect the realities of an AI-enabled operating environment.

Defending at the speed of AI

The phrase "defend at the speed of AI" is becoming increasingly common across the cybersecurity industry. In practice, it means recognizing that human-scale processes alone may no longer be sufficient for machine-scale challenges.

Defensive applications of AI are already helping organizations improve threat detection, vulnerability analysis, code review, incident investigation, and remediation prioritization. Used appropriately, AI can help security teams process information faster, reduce manual effort, and accelerate response.

The goal, however, isn’t to remove people from the process. Human judgment, accountability, and governance remain essential. The most effective organizations combine AI-assisted speed with disciplined oversight and operational control.

The objective isn’t full autonomy. It’s faster, better-informed decision-making.

The path forward with frontier AI

Much of the attention surrounding frontier AI has focused on the capabilities of the models themselves. The more important question may be how organizations choose to respond.

The fundamentals of cybersecurity remain remarkably consistent:

- Reduce unnecessary exposure

- Strengthen identity controls

- Improve visibility

- Accelerate remediation

- Test response plans

- Understand dependencies

- Modernize legacy environments where the risk justifies investment

What changes is the speed and discipline with which these activities must now operate. Organizations that strengthen resilience, improve governance maturity, and increase operational readiness will be better positioned, regardless of which AI model or technology emerges next.

The future of cybersecurity won’t be defined solely by better tools. It’ll be defined by better leadership, stronger operational discipline, and the ability to adapt confidently as technology continues to evolve.

In the age of frontier AI, resilience is no longer only a cybersecurity objective. It’s becoming a core business capability.

Building resilience for the frontier AI era

Frontier AI is compressing the time organizations have to understand risk, make decisions, and act. Explore CGI's frontier AI capabilities to see how we can help strengthen governance, resilience, and operational readiness so your organization can move at AI speed with confidence and control.

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For health and life sciences organizations, the AI conversation is changing.

The question is no longer simply where AI can be applied. Leaders now need to determine which investments can deliver measurable value at enterprise scale while managing cost, risk and accountability.

This creates an important executive challenge: How do you scale AI without allowing its economics to scale unchecked?

Part of the answer lies in understanding and governing AI consumption.

AI introduces a different cost equation

Health and life sciences organizations face rising patient demand, talent scarcity, tighter budgets, pressure to speed up research and development (R&D) and changing regulatory requirements. At the same time, many are moving AI from pilots into production. Our 2026 Voice of Our Clients research shows that leaders are prioritizing high-value AI use cases and strengthening governance as they pursue measurable business outcomes.

While traditional technology investments can often be planned around relatively familiar cost structures, Generative AI (GenAI) changes the equation. Consumption varies based on how models are used, which models are selected and how agents and applications are designed.

Tokens, the units processed by many GenAI models, are one component of that consumption. Individually, their cost may appear small. At enterprise scale, however, leaders need to know where consumption occurs and whether it creates sufficient value.

This is particularly relevant in health and life sciences, where AI applications can span clinical and administrative workflows, claims, R&D and other information-intensive processes.

Without effective governance, organizations can struggle to understand AI spending, allocate costs to the appropriate business solutions and products and establish accountability. Costs can also escape scrutiny during development and testing, making overall AI spending harder to forecast.

AI cost management therefore cannot begin after deployment. Organizations need to build it into their operating model before starting pilots.

Moving from cost visibility to accountability

Effective AI cost governance connects technical consumption to business ownership.

Leaders need to understand which AI resources teams consume, who consumes them, which use cases they support and what outcomes those use cases are expected to achieve.

A practical governance model establishes clear ownership, cost allocation principles and accountability for AI resources. Metrics and reporting can then help leaders monitor consumption and make informed decisions.

Similar challenges around cost governance emerged during the early adoption of cloud services. As organizations scaled their cloud environments, many saw costs grow rapidly without the governance and visibility needed to manage them effectively. Cloud FinOps emerged in response, bringing financial accountability, governance and cost optimization into cloud operating models.

As enterprise AI approaches a similar inflection point, the lesson from cloud is clear: organizations should establish the financial and governance disciplines for AI early, before adoption outpaces their ability to understand, control, and optimize its costs.

For generative AI, token consumption can be part of that picture. Tracking tokens at the appropriate level can help teams identify inefficient usage and assess design choices. Leaders can then evaluate that consumption against the business metrics that determine whether a use case warrants further investment.

The objective is not to minimize token use. It is to optimize AI consumption relative to the value it creates.

Connecting governance with architecture

Cost governance is not solely a finance responsibility. Architecture decisions also influence how efficiently organizations consume AI resources.

Cloud architectures that do not support effective cost management, limited use of capabilities such as autoscaling and automatic shutdown and the application of on-premises practices to cloud environments can weaken cost control. As organizations scale AI, technical, financial and business decisions need to support the same objectives.

Data governance is equally important. According to our 2026 VOC research, 54% of the health and life sciences business and IT leaders interviewed report having a holistic data strategy across their internal enterprise, while only 17% report a centralized approach to internal data governance. Across external ecosystems of partners and suppliers, those figures fall to 32% and 14%, respectively.

This gap matters because enterprise AI depends on trusted data and governance that extends beyond individual experiments.

Making economics part of enterprise AI governance

For health and life sciences executives, AI governance needs to address more than security, responsibility and compliance. It also needs to determine whether AI is economically sustainable.

Three actions can help:

- Establish ownership before scaling: Define who owns each AI resource and use case, who is accountable for consumption and how costs will be allocated.

- Measure consumption alongside outcomes: Monitor relevant cost and usage metrics, including token consumption where appropriate, and connect them to business measures. A lower AI bill is not necessarily a better result if it reduces the value of the application.

- Build continuous optimization into governance: AI services, pricing and purchasing options continue to change. Establish an ongoing process for reviewing costs, architecture choices and consumption patterns rather than relying on a one-time business case.

For more guidance on this topic, I invite you to read recent blogs from our global AI team: The CFO's AI token economy playbook: Why agentic cost management is becoming a boardroom issue and The AI token economy: Managing the hidden costs of agentic AI.

From AI experimentation to sustainable value

Health and life sciences organizations are moving from AI experimentation toward operational execution. Enterprise-ready AI will require modern foundations, meaningful metrics and human-led governance embedded in individual use cases.

Economic governance needs to be part of that approach. For years, organizations have relied on established governance structures, such as project management offices (PMOs) to oversee programs and projects, and enterprise architecture boards to guide technology and architecture decisions. Rather than creating entirely new governance layers for AI, organizations can build on these proven structures, embedding relevant AI considerations and decision rights into existing processes. This approach can strengthen AI oversight while minimizing unnecessary complexity and duplication.

As AI adoption expands, leaders need to understand both what their organizations consume and what they gain in return. Token consumption provides one useful measure, but the broader objective is to make informed investment decisions that connect AI costs with measurable outcomes.

For executives, the question is no longer simply, “How much AI are we using?” A more useful question is: “Are we governing AI consumption in a way that allows us to scale its value?”

If your organization is considering how to build economic governance into its AI strategy, I welcome the opportunity to compare perspectives. Reach out to me to discuss how health and life sciences leaders can plan for AI costs, accountability and value as they move from experimentation to enterprise scale.

Read Part 1 of this series, Digital reengineering in health & life sciences: From transformation ambition to measurable results, to explore why scaling AI with discipline is essential to turning transformation ambition into measurable business value.

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For more than a decade, organizations across the global trade industry have invested heavily in digitizing trade processes. Yet many trade transactions are still slowed by manual processing, disconnected systems and unnecessary risk. The problem isn't a lack of technology. It's a lack of trusted, interoperable data.

That challenge is reflected in CGI's 2026 Voice of Our Clients research. While 88% of corporate and transaction banking leaders say technology and digital acceleration are having a high impact on their organizations, fewer than half (47%) say their digitization strategy is delivering the expected results. Closing that gap requires more than digitizing documents. It requires trusted data that organizations can exchange confidently across the trade ecosystem.

Scanning and converting documents into PDFs was an important first step in modernizing trade finance, improving efficiency and reducing reliance on paper. Yet a PDF is still just a digital copy of a physical document. The information it contains often must be extracted, re-entered into multiple systems and verified manually before it can be used.

The next phase of digital trade requires moving beyond document images to trusted structured data that enables organizations to exchange verified information consistently across systems and organizations.

This is the foundation of trusted trade. It reduces risk, improves efficiency and supports more seamless cross-border trade.

Building a supply chain of trust

Just as goods move through a coordinated global supply chain, the data and trust that accompany them must move seamlessly as well.

Today, organizations often review, re-enter and verify the same trade data each time a shipment changes hands or a new party joins the transaction. Whether it's a bank, a customs authority or a buyer, each participant repeats much of the same work to confirm that the information is accurate. This duplication slows transaction processing, increases costs and creates friction throughout the trade ecosystem.

Trusted trade replaces this repetitive process with a simple principle: verify once, use many. Digital signatures, cryptographic technologies and supportive legal frameworks make it possible to verify information at its source and carry that proof throughout the transaction life cycle. Once verified, information such as an organization's identity or an employee’s authority to sign an invoice can be securely shared and reused by multiple parties, eliminating the need for repeated manual verification.

Digital identity: Establishing trust at the source

A trusted trade ecosystem depends on knowing exactly who you are doing business with and confirming that the instructing party is authorized to act on behalf of their organization.

For banks, corporates and other trade participants, verifying a counterparty has always been essential. Yet establishing an organization's legal identity and confirming that the individual signing an agreement is authorized to do so often remains a manual, time-consuming process. While many organizations have transitioned from paper-based processes to digital services, they continue to operate on a model of assumed trust, relying on usernames, passwords and one-time passcodes (OTPs) to establish identity. Without a higher level of assurance, organizations remain exposed to fraud, compliance risks and operational delays.

Digital identity addresses this critical gap by shifting the security model from assumed to verifiable trust. The legal entity identifier (LEI) provides a globally recognized means of verifying an organization's identity. Combined with the verifiable LEI (vLEI), it enables a standardized, cryptographically secure and automated way to verify both an organization’s identity and the authority of individuals acting on its behalf.

The result is greater confidence in every digital transaction. Participants can verify counterparties, confirm signing authority and reduce reliance on manual checks. That not only reduces risk but also makes digital trade faster, more transparent and easier to scale.

Breaking down digital islands: Connecting the trade ecosystem

Secure data and trusted digital identities are essential, but they are only part of the solution. Trade also depends on systems exchanging information with one another.

Today, many organizations operate on separate technology platforms that were never designed to work together. A shipping company's system may not easily exchange data with a bank's platform or integrate seamlessly with customs authorities or other trade participants. These disconnected systems create digital islands that limit the benefits of digitization.

The answer is not to move everyone onto the same platform. It's to adopt common data standards that allow different systems to communicate and exchange trusted information.

Organizations such as the International Chamber of Commerce's Digital Standards Initiative (ICC DSI) are helping advance this work by promoting globally recognized trade data standards. Just as the internet relies on shared protocols to connect billions of devices, digital trade depends on common standards that allow information to move consistently across organizations, industries and borders. A digital invoice created in Brazil, for example, should be interpreted the same way by a financial institution in Singapore. That level of interoperability gives organizations the flexibility to choose the technology that best meets their needs while enabling trusted data to flow across the broader trade ecosystem.

Turning trusted trade into reality

The transition from digital trade to trusted trade requires more than new technologies. Organizations will need to adopt common standards, strengthen digital identity and enable trusted data to move securely across the trade ecosystem. The building blocks already exist. The next step is broader industry adoption and collaboration.

Every stakeholder has a role to play:

- Corporate leaders and boards: Treat digital trade as a business transformation initiative, not just an IT project. Support the use of globally recognized identifiers, such as the LEI, and strengthen vendor onboarding and due diligence with trusted digital identity.

- Banks and financial institutions: Move beyond document digitization by embedding global data and identity standards into client and trade finance processes. Doing so can reduce manual intervention, improve compliance and create new opportunities for automation and financing.

- Technology providers: Build solutions on open, interoperable standards rather than proprietary ecosystems. Organizations benefit most when trusted data can move securely between platforms without creating new digital silos.

- Governments and regulators: Continue advancing legal and regulatory frameworks that recognize digital trade records and trusted digital identities while supporting secure cross-border data exchange.

Across the industry, the focus has shifted from digitizing trade to building trusted, interoperable trade ecosystems. I'll be exploring this topic during the panel session at Sibos 2026 in Miami, "From digital trade to trusted trade," and look forward to hearing how organizations across the industry are approaching this next phase of transformation.

I'd welcome the opportunity to discuss how your organization is approaching trusted trade and the next phase of digital transformation. Please reach out if you’d like to continue the conversation.

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In the first two blogs in this series, “The new manufacturing mandate,” and “Preparing the enterprise for AI-led transformation,” I explored how manufacturers are building greater resilience amid continuous uncertainty and why scaling AI requires a more adaptive enterprise foundation.

The next challenge extends beyond the enterprise. Competitive advantage will depend on how effectively manufacturers connect with suppliers, logistics providers, customers and technology partners to make better decisions and respond as conditions continuously change.

External signals matter, too. Changes in demand, weather events, trade barriers, tariffs and geopolitical developments can quickly affect manufacturing operations. Organizations that also bring those signals into decision-making will be better positioned to respond.

For manufacturing leaders, I believe two capabilities will increasingly shape that advantage: flow and modularity. Flow is the ability to move information, materials and decisions across the network with fewer barriers. Modularity is the ability to evolve technologies, capabilities or relationships without redesigning the entire network.

Together, they provide a way to think about the next stage of manufacturing transformation, creating the conditions for shared visibility, trusted collaboration and increasingly shared capability.

Supply chains are becoming value networks

Manufacturers have spent decades optimizing supply chains for efficiency, cost and just-in-time operations. Recent disruptions have exposed the limits of optimizing for efficiency alone.

Our 2026 Voice of Our Clients findings underline the pressure. Sixty-eight percent of manufacturing executives say shifts in global trade and economic order are having a high impact on their organizations.

Manufacturers are responding by increasing end-to-end visibility, diversifying sourcing and regionalizing supply networks. They are also using AI to improve forecasting and support faster decisions as conditions change.

Looking toward 2030, supply networks are likely to become more predictive and increasingly autonomous, using AI, digital twins and real-time collaboration to anticipate disruption and respond more effectively.

The result is a shift from linear supply chains toward adaptive value networks. Resilience will depend on how well the wider network can respond, not only on the strength of an individual enterprise.

Digital shape and trust determine ecosystem readiness

Information must move across organizational boundaries for these networks to work. At the same time, manufacturers need confidence in what they share and how others can use it.

Our research highlights the challenge. Fifty-four percent of manufacturers report having a holistic data strategy, yet only 48% say they can effectively locate their data assets. Eighty-four percent prioritize cyber risk assessment when selecting IT providers, while only 36% consider their IT/OT data convergence mature.

These figures point to a broader issue. Ecosystem participation requires what I call the right “digital shape”: the ability to connect through modular platforms, manage data securely, strengthen IT/OT convergence, and collaborate without losing control or sovereignty.

Manufacturers cannot participate effectively if fragmented systems or inconsistent data keeps them isolated. Nor can they build trusted relationships if they cannot assess the security of their partners and providers.

The leadership challenge is therefore not simply how to connect more systems. It is how to connect selectively and securely while maintaining the governance and control each participant requires.

AI and smarter data ecosystems

Data spaces offer one approach. They create governed environments in which organizations can exchange agreed data while retaining control over its use.

Applied effectively, they can support traceability, planning, sustainability reporting and faster responses to disruption, forming part of a new operating model for modern manufacturing.

While trusted data creates visibility across manufacturing networks and ecosystems, AI helps organizations act on that information.

Manufacturers can use AI to identify disruption earlier, anticipate variability and coordinate responses across planning, procurement, production and logistics.

Our research shows that 66% of manufacturers are already applying AI in core business and operational processes, and 73% are exploring agentic AI.

The next wave of AI value won't come from smarter analysis inside functional silos. It will come from orchestrated response across the network: connecting planning with procurement, production with logistics and visibility with faster decisions.

And the horizon extends beyond AI. Emerging technologies such as quantum computing could eventually expand what is possible in optimization, simulation, materials science and supply network design.

But it's not fully clear when, or how, these technologies will mature enough to act on. That uncertainty can create hesitation. Waiting too long can slow progress, but committing too early to a rigid architecture can limit future choices.

Manufacturers therefore need the kind of foundation that lets them introduce capabilities progressively as the technology and business cases mature. Modularity gives them a way to adapt without betting the entire architecture on trying to predict what comes next.

The next step requires shared risk, shared reward and trusted guidance

The next phase of ecosystem transformation will increasingly depend on shared risk and shared reward. Manufacturers will need partners that can help modernize foundations, shape AI strategies, strengthen security, support organizational change and scale execution without simply shifting the risk back onto the enterprise.

Talent pressures reinforce this shift. Forty-four percent of manufacturers report significant difficulty recruiting IT talent. Forty-one percent say talent shortages are having a high business impact, while 71% expect to rely substantially or fully on providers for IT services over the next three years.

This is more than a case for outsourcing. It is a strategic shift toward partner-enabled capability, where organizations can access specialist expertise while retaining the knowledge and accountability that differentiates the business.

In practice, this means reengineering how governance, architecture, security, adoption and value creation come together as manufacturers become more interdependent with their partners.

Next-generation manufacturing leaders will master flow and modularity

The manufacturers that lead won’t be those with the best factories, the lowest costs or the most AI pilots. They will be the ones who can participate confidently in trusted ecosystems and adapt as those ecosystems evolve.

This raises a different question for today's leaders: are we optimizing individual parts of the business, or creating the conditions for value to move across the network?

The answer will depend on how well manufacturers have built the foundations described above. Can information move when decisions depend on it? Can new capabilities be introduced without rebuilding the architecture? Can partners collaborate without compromising control?

These are practical tests of flow and modularity, and they point to a broader shift: leadership is no longer only about optimizing what happens inside the enterprise, but about creating the conditions for value to move beyond it.

If you are exploring how these changes are reshaping your manufacturing network, I would welcome the opportunity to compare perspectives.

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Traditionally, insurers have approached rating largely as an automation challenge: implement the rules, calculate the premium and update rates when needed. That approach is changing.

Market conditions, consumer behavior, competition and expanding smart data sources are increasing the need for faster, more precise pricing decisions. Insurers now have an opportunity to rethink rating as a connected pricing life cycle that brings together data, actuarial expertise, analytics, artificial intelligence (AI) and governance.

Why traditional rating cycles are changing

Several shifts are reshaping insurance pricing.

- Faster pricing cycles: Traditional rate changes can take days, weeks or even months because sophisticated pricing models require extensive analysis, validation and implementation. Today, insurers need to model, test and implement changes more frequently in days or, in some cases, hours. At the same time, risks are changing quickly. Consider a landscaping business that begins working in higher-risk locations or adds tree removal using a cherry picker. Its risk profile has changed, but an insurer may have limited information about those changes. Identifying evolving risks sooner can help insurers adjust coverage and pricing without adding unnecessary complexity for the client.

- A connected pricing life cycle: Modeling, testing, approvals, deployment, monitoring and refinement should work as one process rather than separate activities. Legacy systems require insurers to move data among different tools to test rates and analyze their potential impact. Modern pricing platforms can connect these activities, helping insurers shorten pricing cycles while reducing the complexity associated with moving data between systems.

- Greater business ownership: Low-code and no-code capabilities can give actuarial and business teams greater control over routine pricing changes while IT focuses on architecture, integration, security and platform reliability. AI could extend this capability by helping users interpret requirements, identify affected rules, run simulations and flag potential impacts before deployment. Built-in testing, approvals, version control and audit trails remain essential to maintaining appropriate oversight.

- Broader data and integration: External data, predictive models and simulations can supplement traditional rating factors. Specialized cloud-based platforms can also connect rating to policy administration, underwriting and distribution systems. The objective is not simply to use more data or technology, but to make better pricing decisions.

What AI changes in the insurance pricing life cycle

AI creates an opportunity to move beyond automating predefined rating processes.

Consider telematics in auto insurance. Insurers can already use information about how much, when and how safely a policyholder drives to supplement traditional rating factors. AI could analyze this information alongside factors such as road conditions and vehicle technology to provide a more nuanced view of risk.

For example, AI could help distinguish sudden braking caused by hazardous road conditions from a recurring pattern of aggressive driving. With appropriate validation, governance and controls, pricing professionals could use these insights to investigate risk, test scenarios and assess whether a pricing change is justified.

The value goes beyond calculating a premium. AI can help professionals understand what is driving risk and make more informed, explainable decisions.

From insurance rating technology to decision intelligence

The next stage of insurance rating is not simply about implementing rate changes faster. It is about helping pricing professionals understand, test and govern decisions throughout the pricing life cycle.

We are already seeing elements of this shift in our work with insurers. Connected pricing environments can give actuarial and business teams greater control while maintaining the governance insurers require.

AI adds another layer. Pricing professionals could increasingly use natural language to investigate unexpected results, compare formulas, explore data or run simulations. Instead of navigating separate systems and workflows, they could bring the information needed for a decision into one governed process.

When I meet with clients, one of my key questions is what they expect from AI. Their answers often return to practical needs: greater pricing agility, faster time to market and easier, smarter integration across platforms.

One need has been particularly interesting: preserving knowledge as experienced professionals retire. As a new generation moves into these roles, insurers need tools that help people understand why pricing decisions were made, learn from previous decisions and build expertise faster. These tools will also help the next generation of professionals get up to speed faster by simplifying processes and providing support.

That points to a larger opportunity: creating decision intelligence around pricing.

If the context behind each pricing decision remains available after deployment, insurers could compare assumptions and expected outcomes with actual performance. When results begin to diverge, AI could help assemble the relevant history and direct an actuary to areas that warrant investigation.

Instead of reconstructing decisions across multiple systems, actuaries could spend more time evaluating what happened, why it happened and what should change. Over time, pricing can become a continuous learning process in which previous outcomes inform future decisions.

Keeping people at the center of insurance rating

Greater speed cannot come at the expense of trust, particularly in a regulated industry.

We see AI supporting actuaries, not replacing their judgment or making consequential pricing decisions autonomously. AI can prepare analyses, identify opportunities and make recommendations. Actuaries remain in control, supported by appropriate business, actuarial and regulatory approvals.

Recommendations also need to be explainable and traceable. Pricing professionals need to understand how recommendations were produced and be able to test them.

CGI’s 2026 Voice of Our Clients insights indicate that organizations are looking beyond administrative applications: 64% intend to use AI in core business processes, compared with 17% in administrative functions and 15% in IT-only applications.

For insurance leaders, pricing is one area where strong technology, automation and data foundations can directly affect business performance and the client experience.

Building the next generation of insurance rating

The next generation of insurance rating will depend on how effectively insurers combine trusted rating technology, actuarial expertise, connected intelligence and responsible governance.

It will also require insurers and technology providers to shape new capabilities together. Through the Ratabase Product Advisory Council (RPAC), we are bringing insurance practitioners and CGI experts together to explore industry challenges, test emerging concepts and inform future product priorities.

For insurance leaders, the opportunity is to look beyond automating today’s processes. Where could better data and AI-assisted insights improve pricing decisions? Where could greater business control shorten the path from analysis to deployment while maintaining appropriate governance?

The goal is not automation for its own sake. It is a pricing environment that helps professionals make better-informed decisions, learn from outcomes and act with confidence while keeping people accountable for the decisions that matter.

I welcome conversations with insurance leaders about where AI can make a practical difference in pricing and how we can shape its role responsibly together.

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The conversation about AI in insurance customer service is moving beyond cost efficiency. As AI becomes mainstream, insurers are exploring how it can also improve the customer experience.

Agentic AI takes this potential a step further. Rather than simply generating an answer, a well-designed AI agent can understand a customer’s objective, coordinate actions across systems and people, and work toward the desired outcome, while escalating to a human when judgment is required.

That capability brings greater autonomy and a critical question: How can insurers give AI agents the authority to act while maintaining the human judgment customers need and trust?

When AI orchestrates the insurance customer journey

Agentic AI can shift insurance customer service from managing interactions to orchestrating outcomes. This will increasingly blur the boundaries between core and supporting functions, requiring insurers to rethink processes, data and technology together.

Consider a policyholder whose home has been damaged during a major storm. A conversational AI system may explain what the policyholder should do next. An agentic AI system could authenticate the customer, understand their coverage, open the claim and coordinate the journey. It could collect information, arrange services, initiate permitted payments and provide proactive updates while involving a human claim professional when judgment or additional support is required.

As routine work becomes increasingly automated, insurance professionals can focus their time on complex exceptions, vulnerable customers, disputes, judgment and oversight of AI-enabled journeys.

Beyond individual cases, agentic AI could also help insurers move from reactive to proactive service by identifying coverage gaps, preventing policy lapses, anticipating risks and addressing issues before they impact the customer.

But are insurers ready to make the shift? CGI’s 2026 Voice of Our Clients research shows that AI-enabled customer experience is a top priority, but delivering on that ambition depends on core modernization, automation and strong data foundations. Front-end innovation relies on back-end modernization. While 71% of insurance executives say digitalization is affecting their operating model, only 19% believe their operating model is sufficiently agile to support digital transformation.

With AI now widely accessible, the challenge for insurers is turning its potential into measurable, repeatable and controlled business outcomes.

Trust must be engineered, not asserted

Delegated authority has long been fundamental to insurance. Underwriters have limits, claims professionals have settlement authorities, and payments above defined thresholds require additional approval. The same principle should apply to agentic AI.

Each AI agent should operate within clearly defined boundaries: what it can access, recommend, change, approve or initiate, and when it must stop and involve a human.

Trust and governance therefore need to be designed from the start. Regulatory expectations are moving in the same direction. In Europe, key EU AI Act requirements, including transparency obligations, became applicable in August 2026. In the U.S., a growing number of states have adopted the NAIC Model Bulletin on AI use by insurers, while Canada's OSFI is strengthening expectations around AI and model risk.

The frameworks differ, but the direction is consistent: greater autonomy requires greater accountability, transparency, human oversight and operational resilience.

Why technology ecosystem choice matters for insurers

No single technology provider will supply every capability an insurer needs for an agentic operating model. The architecture may combine cloud infrastructure, foundation models, customer service platforms, core insurance systems, identity services, data platforms, cybersecurity controls and specialized industry solutions.

The same principle applies to conversational and agentic AI in customer service. CGI has delivered client initiatives with platforms such as NiCE Cognigy, but the starting point is not the technology itself. Technology choices need to reflect the customer experience an organization wants to create, its target operating model and the business outcomes it aims to achieve.

The governing principle is straightforward: technology selection should follow the target operating model and the desired business outcome, not the other way around.

A disciplined path from experimentation to scale

Insurers should not begin with the most complex claims or the most autonomous agents. A more measured approach starts with simple, bounded use cases that establish reusable foundations for trusted delegation before expanding autonomy.

Agentic AI will not eliminate the need for human insurance professionals. It will change where their time and judgment create the most value. It will also change what customers expect an insurer to know, explain and accomplish during a single interaction.

By combining agentic AI with modern customer platforms and trusted customer data, insurers can move beyond simply improving service productivity. They can begin to orchestrate customer journeys proactively, anticipate needs and coordinate actions across systems and teams, with the goal of improving customer satisfaction, loyalty and retention.

As insurers explore where agentic AI can create value, the conversation should start with the right balance of autonomy, human judgment and trust. Connect with me to discuss what that balance could look like for your organization.

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“Where we’re going, we don’t need roads.” —Dr. Emmett Brown, Back to the Future

When Back to the Future Part II imagined 2015, it gave us flying cars, self-lacing shoes, and hoverboards. Today, we can envision a different future—one shaped by AI agents that act on our behalf.

Imagine a Tuesday morning in 2029. You don‘t open a banking app or compare mortgage rates. Instead, over coffee, you say: “Find me the best mortgage for a $650,000 home. Fixed for 10 years. Flexibility matters more than the lowest payment.”

Within seconds, an AI agent compares lenders, checks affordability, reviews fees, and recommends three banks. Those banks speak not to you, but to your agent.

This may sound futuristic, but a decade ago, asking AI for financial advice instead of searching Google seemed equally improbable. The interface is changing again, including who banks communicate with and how decisions are made.

The strategic question is no longer whether AI will transform banking. It‘s: What happens when banks compete not only for customers, but for the agents representing them?

When customer experience meets agent experience

Banking has reinvented itself with every new customer interface—from branches and call centers to online and mobile banking. Now another interface is emerging: not another screen, but another decision-maker.

Banks have spent decades refining the customer experience (CX). As AI agents increasingly act on customers’ behalf, they may need to focus just as intently on agent experience (AX).

These agents won’t be influenced by an award-winning app. Instead, they’ll compare, calculate, and negotiate based on whether product information is structured, APIs are reliable, pricing is transparent, and lending criteria can be interpreted by software.

This makes APIs the new front door. A bank’s next “customer” may never see its interface; instead, an AI agent might assess its offering in milliseconds, reject its API, and move on to a competitor. The API becomes the storefront, the product catalog becomes the sales pitch, and data quality becomes the bank’s reputation.

As one banking executive observed, “We’ve spent years polishing the front door while nobody looked at the loading dock.” In an agent-driven market, APIs may become the loading dock—the route through which financial products are discovered, evaluated, and accessed. API quality is therefore no longer simply an IT metric. It’s a business capability and a source of competitive advantage.

In this sense, AI agents are the DeLorean of banking—not because they transport us into the future, but because they allow us to examine today’s business model through tomorrow’s lens.

The customer journey is shrinking

Banks have optimized every stage of the customer journey, from awareness and comparison to approval and onboarding. AI agents can compress much of that journey into seconds. The journey becomes an evaluation, and the evaluation becomes a recommendation. Competition moves from winning attention to earning algorithmic preference.

A European mortgage lender recently asked us, “How do we make our mortgage journey more digital?” The deeper issue was whether future customers would complete that journey themselves—or delegate it to an AI agent.

Together, we reframed the discussion around information architecture, APIs, and product transparency. What began as a UX discussion became a strategic conversation about the bank’s future distribution model.

A new kind of trust

Banking has always been built on trust. As customers increasingly use AI to support financial decisions, banks may need to earn the trust not only of customers, but also of the systems advising them.

That trust will come from transparent products, consistent decisions, reliable data, secure interactions, and information that intelligent systems can readily understand.

From AI inside the bank to AI beyond it

CGI’s Andy Schmidt recently argued that banking has moved beyond AI experimentation. The challenge is no longer proving what AI can do through isolated pilots—it’s redesigning the business around AI. (The science fair is over: Building what’s next in banking | CGI.com).

Most banks ask, “How can AI improve our operations?” A more strategic question is, “How will AI change who interacts with our bank?” Increasingly, the first interaction may come from software acting for a customer.

AI creates opportunities to rethink how banks serve customers, make decisions, and deliver value across an increasingly intelligent ecosystem.

Six questions worth asking today

Whether this future arrives in 3 years or 10 is almost beside the point. Leadership teams should already be asking:

- Are our products machine-readable?

- Can AI accurately understand and compare our offerings?

- Are our APIs designed for automated interactions?

- Is our pricing transparent enough for algorithmic decision-making?

- Can external AI systems interact with us securely?

- If an AI agent had to recommend one bank tomorrow, why would it choose ours?

These aren‘t simply technology questions. They‘re fundamental business strategy questions.

CGI’s latest Voice of Our Clients research highlights the opportunity and the work ahead. While 75% of banks are exploring agentic AI, 39% remain in the proof-of-concept phase. At the same time, 72% say responsible AI and digital innovation are accelerating digitization.

The next challenge is scaling AI beyond the enterprise and preparing for customers who rely on agents to evaluate products and make decisions for them.

Preparing for the next interface

Every major banking interface has seemed obvious after it arrived: online, mobile, and open banking. AI agents may become the next interface—not replacing people or advisors, but increasingly acting on customers’ behalf to discover, evaluate, and select products.

The most important question may therefore no longer be, “How should our bank use AI?” It may be, “How ready is our bank for customers who increasingly rely on AI?”

There is no established playbook, which is precisely why the conversation matters. Over the last five decades, CGI has helped banks navigate profound change by identifying where innovation creates lasting value—from modernizing core platforms to reimagining digital customer experiences.

Today, that means preparing for customers whose financial decisions are increasingly supported—or delegated—to AI. It requires anticipating changes in customer behavior, distribution models, and market expectations before they become competitive reality.

If this blog leaves you with more questions than answers, that may be where the conversation should begin. I welcome the opportunity to discuss what this future could mean for your organization and how to prepare.

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While many banks have clearly defined their transformation priorities, translating them into results at scale requires the right operating model, capabilities and organizational alignment.

To strengthen execution and accelerate results, banks are increasingly turning to global capability centers (GCCs). By providing access to specialized talent, expanding delivery capacity, and supporting strategic transformation initiatives, GCCs can help banks move from lofty ambition to sustained impact.

However, simply launching a GCC is no longer a differentiator. Today, the more important question is whether the GCC can evolve beyond a delivery organization to become a strategic capability that helps the bank execute faster, innovate more effectively, and achieve measurable business outcomes.

Based on our GCC experience, the highest-performing GCCs share several attributes that distinguish them from those that struggle to deliver their full potential.

1. They own outcomes, not activities

The value of many GCCs is measured primarily through operational metrics: headcount growth, utilization rates, cost savings, and project volumes. While these indicators provide useful operational insight, they don’t answer the most important question: is the GCC helping the bank achieve its strategic objectives?

High-performing GCCs are increasingly evaluated based on business outcomes rather than delivery activity. Their success is tied to the initiatives they accelerate, the capabilities they enable, and the value they help to create across the enterprise.

Instead of focusing solely on how much work is completed, leading banks measure how effectively that work contributes to modernization, growth, resilience, and the customer experience.

The conversation shifts from activity to impact.

2. They become capability hubs, not talent hubs

Access to talent remains one of the primary reasons banks establish GCCs. However, the most mature centers are no longer viewed simply as sources of additional capacity.They’ve become enterprise hubs for critical capabilities.

AI, data engineering, cloud transformation, cybersecurity, digital product development, and platform modernization increasingly require specialized expertise that can be difficult to scale across traditional operating models. Leading banks use GCCs to concentrate and develop these capabilities, creating centers of expertise that support strategic priorities across the organization.

As transformation demands continue to grow, capability depth becomes a greater competitive advantage than workforce scale alone.

3. They participate in enterprise decision-making

Many GCCs remain heavily involved in execution while having limited influence on planning and prioritization. This creates a disconnect between strategic objectives and delivery outcomes. High-performing GCCs operate differently.

Their leaders participate in enterprise planning discussions, understand business priorities, and contribute to decisions that shape transformation roadmaps. As a result, delivery teams gain greater context, priorities become clearer, and banks can respond more effectively to changing business needs.

The GCC becomes an active contributor to strategic execution rather than a downstream recipient of work. In short, they make the bank more agile.

4. They strengthen organizational resilience

Banks face increasing pressure to deliver transformation while managing regulatory complexity, evolving customer expectations, and ongoing talent shortages. In this environment, resilience matters as much as efficiency.

The most effective GCCs help banks build resilience by diversifying delivery capabilities, strengthening knowledge continuity, and creating greater flexibility to respond to changing business conditions. They provide additional capacity during periods of accelerated transformation and help reduce concentration risks associated with relying on a limited set of locations or talent pools.

As a result, the GCC contributes not only to execution, but also to the bank’s ability to adapt and sustain performance over time.

5. They continuously evolve their mandate

One of the clearest differences between average and high-performing GCCs is how their role changes over time. Many centers begin with a narrow focus on delivery support. However, the strongest GCCs view this as only the starting point.

As trust grows and capabilities mature, a GCC’s responsibilities should expand. What begins as a delivery organization can evolve into a strategic capability center, an innovation partner, and ultimately a key contributor to enterprise transformation.

This evolution doesn’t happen automatically. It requires intentional investment in leadership, capability development, governance, and operating model alignment.

However, when executed effectively, the GCC becomes increasingly valuable as business priorities evolve.

The future of banking GCCs

The banking industry is entering a period where execution capacity may become as important as strategy itself. AI adoption is accelerating. Modernization remains a priority. Regulatory expectations continue to increase. Competition for specialized talent persists.

In this environment, banks need more than additional delivery capacity. They need operating models that can consistently translate strategy into outcomes.

The most successful GCCs aren’t defined by location, size, or labor arbitrage. They’re defined by their ability to strengthen enterprise execution, accelerate transformation, and contribute to measurable business value.

The question is no longer whether banks should establish GCCs. The question is whether they can evolve them into strategic assets that help close the execution gap.

CGI works with banks worldwide to design, establish, and evolve high-performing GCCs aligned with their strategic priorities. Explore our global capability centers (GCC) capabilities or connect with me to discuss how a GCC could advance your bank’s transformation agenda.