Three Developments Worth Watching

1. SentinelOne and Lenovo extend AI security: Integrating prompt protection into enterprise security offerings addresses growing risks from unauthorized AI use and data exposure.
2. Cognizant introduces AI Nexus: Managing AI costs, quality and outcomes becomes increasingly important as enterprises expand deployment.
3. Nutanix and Netris combine cloud infrastructure capabilities: Automated networking and multitenancy could help providers deliver secure GPU-based services more efficiently.

These developments demonstrate that successful AI adoption requires more than access to models. Enterprises need coordinated infrastructure, measurable performance and security controls that extend across the AI lifecycle.

5 IT and Technology News Stories

1. SentinelOne and Lenovo Extend Collaboration With AI Agent Security Capabilities

SentinelOne and Lenovo are expanding their partnership by integrating SentinelOne Prompt Security into Lenovo’s ThinkShield Security Portfolio, adding AI discovery, prompt protection and data loss prevention capabilities.

As employees increasingly use generative AI and autonomous agents, conventional endpoint security may not adequately address sensitive information entering external AI tools. Embedding AI governance into existing enterprise security offerings could help organizations adopt AI without relying on blanket restrictions. The challenge will be maintaining visibility across approved and unauthorized tools while ensuring policies keep pace with increasingly autonomous systems.

2. Cognizant AI Nexus Redefines Enterprise AI Infrastructure Governance

Cognizant AI Nexus focuses on helping enterprises govern AI costs, quality and outcomes as they deploy AI across business operations and technology environments.

As organizations expand AI adoption, fragmented deployments can make it difficult to track spending, compare model performance and demonstrate business value. A coordinated governance approach could help IT leaders make better infrastructure decisions and identify underperforming investments. The broader opportunity is to shift enterprise AI management from experimentation towards measurable operational outcomes. However, governance frameworks must provide actionable visibility without creating additional complexity for teams already managing diverse technology environments.

3. Nutanix and Netris Introduce Integrated Multitenant Infrastructure for AI Clouds

Nutanix and Netris are developing an integrated infrastructure stack combining compute orchestration, virtualization and network automation to support secure, multitenant GPU services for neocloud operators and enterprise data centers.

Demand for AI computing is increasing pressure on providers to deliver GPU capacity with reliable isolation, networking and self-service provisioning. Automating these layers could shorten deployment cycles and help providers monetize infrastructure more efficiently. For enterprise customers, stronger workload isolation may also support more secure shared environments. Commercial success will depend on interoperability, predictable performance and the economics of operating GPU-intensive services at scale.

4. Cloudflare Expands Its AI Workspace Platform

Cloudflare is expanding its AI Workspace ecosystem through partnerships designed to connect AI applications, edge inference, vector search, storage and enterprise security controls.

Businesses deploying AI assistants often encounter latency, fragmented data pipelines and concerns about exposing proprietary information to external models. Combining these capabilities within a connected platform could simplify development and improve access to enterprise knowledge. Edge-based inference may also benefit applications requiring responsive interactions. However, enterprises will need to assess model suitability, data residency, security controls and total operating costs before deciding which workloads belong at the edge or in centralized cloud environments.

5. S&P Global Strengthens Private Markets Analytics With New AI Capabilities

S&P Global is expanding its AI capabilities across private markets analytics, supporting investment professionals seeking deeper insights from complex financial and portfolio information.

Private markets involve fragmented data, extensive documentation and limited transparency compared with publicly traded assets. AI-powered analysis could help investors identify relevant information faster, evaluate portfolio risks and improve research workflows. Yet financial decisions require traceability and reliable underlying data. The competitive advantage will depend on whether these capabilities deliver verifiable insights while preserving confidentiality and analytical rigor, rather than simply accelerating document processing.

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Essential Enterprise IT Service Management Tools: How to Choose the Right ITSM Platform

The article examines how enterprises can select IT service management platforms based on workflow automation, incident resolution, asset visibility, integration and scalability. It highlights the importance of aligning platform capabilities with organizational requirements, service quality and budgets. As AI enters IT operations, businesses should also evaluate governance, usability and measurable improvements in service delivery.

The post ITDigest Weekly Roundup: AI Agent Security, Governed Enterprise Intelligence and the Next Phase of Cloud Infrastructure appeared first on ITDigest.

With the adoption of enterprise AI shifting from rudimentary generative chat assistants to autonomous multi-agent systems, there is a critical obstacle for commercial firms in terms of cost, model behaviors, and data governance, which have not been managed. Cognizant AI Nexus overcomes the barriers of the enterprise through the creation of a standard operating layer that correlates AI uses to business measures and dynamic model routing.

“Many enterprises have already made their AI commitments,” said Ravi Kumar S, CEO, Cognizant. “What boards and finance leaders need now is evidence: what that spend returns, who is accountable for it and how to get more from every dollar. Cognizant AI Nexus gives them one governed place to answer those questions and a way to keep improving the answer.”

The Engine: Dynamic Routing, Real-Time Observability, and Agentic Governance

In the past, enterprise IT organizations trying to scale generative AI encountered an unpleasant tradeoff: either be bound to a single vendor’s proprietary foundation models at the risk of vendor lock-in and high costs of inference, or try to maintain their own multi-model integration which soon would become unmanageable. Moreover, according to the latest FinOps surveys, 97% of companies encountered unexpected costs and time The following five fundamental operational characteristics resolve the issues of the architectural design by addressing the friction points:

Cognizant AI Nexus Reveal: Links all AI tokens, prompts, and API dollars to the respective business units, projects, and people involved in them, giving leaders the full transparency of AI ROI (return on investment).

Cognizant AI Nexus Route: Behaves like the traffic control system by routing the prompts to the optimal AI model (regardless of whether it is a frontier LLM, a small language model, or a self-hosted open source one) and to the second-tier AI model in case of vendor outage.

Cognizant AI Nexus Coach: Analyzes the context, the execution process, and the output quality of the running AI flow and transforms the patterns of successful execution into reusable

Cognizant AI Nexus Engage & Orchestrate: Provides a secured sandbox for developers and analysts to create persistent memory-endowed AI agents capable of executing business logic along with certified human operators.

Audit-Ready Security & Human Control: Implements tight memory permissions, role-based access controls, and required human-in-the-loop approval.

Also Read: SentinelOne and Lenovo Extend Collaboration With AI Agent Security Capabilities 

Strategic Impact on the Information Technology and AI Industry

Deploying a modular, enterprise-wide operating layer across multi-cloud AI environments introduces fundamental realignments across the Information Technology (IT) and Artificial Intelligence landscape:

1. Transforming IT Operations from “Model Access” to “Cost and Quality Governance”

For the past few years, AI vendor assessments for IT departments have revolved around model performance and benchmark tests. But as more companies integrate enterprise-wide applications using AI that generate thousands of employee inquiries and background tasks each day, IT operations will need to transition to AI FinOps and quality control. The modular governance platform AI Nexus gives CIOs the ability to set cost-per-outcome criteria so that expensive frontier models are limited to analytics and budget models are used for automation tasks.

2. Quick Roll-Out of Self-Deploying Multi-Agent Systems

One of the major problems encountered during enterprise AI adoption is “agent drift” whereby the AI agents get trapped in loops, misinterpret context or call APIs in ways not intended by humans. Access to dedicated memory, digital identities, and policy enforcement layers will ensure that the IT organizations roll out multi-agent autonomous systems safely. This will enable enterprise software developers to develop intricate, autonomous workflows as opposed to just Q&A bots.

3. Dissolving Vendor Lock-In across Cloud Ecosystems

Hyperscale cloud operators usually develop proprietary AI systems to lock enterprise data in their cloud ecosystem. Adoption of an open and vendor-agnostic operating layer such as Cognizant AI Nexus decouples the enterprise logic from the LLM vendor. The IT organization has full control over its proprietary workflow while being able to swap LLMs as newer versions get developed.

Impact on Organizations Working in the IT and AI Sector

The launch of AI Nexus by Cognizant sets higher benchmarks for enterprise software providers, IT consulting firms, and technology buyers in the industry:

Requirements for AI Financial Management: Independent AI solutions that do not have real-time token monitoring, budget limits, and financial attribution measures will soon encounter increasing client rejection. Financial transparency and accountability will become mandatory during enterprise software procurement processes for every AI transaction.

Rise of Custom AI System Integrators: With foundation models moving towards becoming a commodity product, service providers and consulting firms will fight for the integration capabilities of AI in current mainframe, cloud, and ERP systems.

Minimizing Compliance and Reputation Risks: Dynamic guardrails at runtime protect the enterprises against violations of regulatory laws (EU AI Act and NIST AI RMF) and assure protection of proprietary data and accuracy of customer-facing AI transactions.

Conclusion

The introduction of the AI Nexus by Cognizant is a very important step towards the maturity of enterprise-level AI infrastructure. Through the integration of dynamic model routing, token-based FinOps in real time, and multi-agent governance under one operational canvas, the platform serves as the bridge from the promise of AI to enterprise-level predictability. This product release sends a clear message to the IT and AI industries that scaling AI in enterprises requires robust governance and operational quality.

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The challenge which the announcement attempts to address is the key limitation in enterprise AI deployment. The matter refers to the transition from the individual AI experience by consumers to the secure, governed, and scalable enterprise execution process. Despite the fact that enterprises integrate generative AI into their business processes, the technology teams still have certain challenges related to inference latency, data pipelines scattering, and data governance requirements. In order to address the discussed issues, Cloudflare applies the AI Workspace Platform.

“AI tools shouldn’t force companies to choose between moving fast and staying secure,” said Matthew Prince, Co-founder and CEO of Cloudflare. “We built Cloudflare OS for our own teams first and saw how much faster and more safely we could move. Now we’re bringing that same experience to companies using some of the most widely deployed platforms in the world. The goal is simple: a secure, company-aware AI workspace from day one, without turning every rollout into a multi-million dollar engineering project.”

Under the Hood: Serverless Edge Compute and Native Ecosystem Partnerships

In past years, in order to apply AI at scale within enterprises, there were trade-offs enterprises had to make either way when implementing such technology, whether to route all user queries to central cloud data centers, which would create issues with extreme latency and costly bandwidth costs, or having to maintain costly specialized hardware infrastructure. Additionally, maintaining the security of corporate information while using public models caused many problems for CISOs.

Also Read: Grayscale and Republic Collaborate to Broaden Access to Digital Asset Investments

Cloudflare’s upgraded AI Workspace platform resolves these operational friction points through a four-part architectural framework:

Global Edge Inference (Workers AI): Executes AI model inference across Cloudflare’s vast global network spanning hundreds of cities worldwide, delivering low-latency responses by processing prompts close to the end user.

Integrated Vector Database & Storage (Vectorize & R2): Provides native vector search and unstructured object storage at the edge, allowing developers to build Retrieval-Augmented Generation (RAG) applications that query internal enterprise knowledge bases instantly.

Turnkey Application & Partner Integrations: Partners with leading software applications and developer platforms to embed pre-configured AI workflows, security policies, and developer toolkits natively into enterprise software stacks.

Zero Trust AI Governance & Privacy Controls: Enforces strict data loss prevention (DLP), access controls, and encryption layers around AI interactions, preventing corporate IP or sensitive customer records from being exposed or used in unauthorized model retraining.

 Strategic Impact on the Artificial Intelligence Industry

Bringing together the global edge infrastructure with enterprise AI application frameworks is bringing some profound structural changes to the overall Artificial Intelligence landscape:

1. Transitioning Away from Centralized Hyperscaler Data Centers to Edge-Native Inference of AI

Over the last several years, the domain of Artificial Intelligence was characterized by gigantic cloud data centers featuring massive foundation models. However, as the adoption of enterprise AI is shifting from model training to inference and agentic execution, there will be more and more problems associated with latencies and energy. Cloudflare’s growing AI Workspace platform is accelerating the transition toward edge-native execution of AI models with reduced bandwidth overhead and faster responses of real-time applications.

2. Addressing the Enterprise “RAG and Context” Issue

The main challenge associated with AI implementation in enterprises is the context retrieval, i.e., making an AI assistant or agent capable of accessing relevant real-time corporate information securely in order to answer any complex questions. Native operation of the vector search and storage at the network edge is solving the technological bottleneck connected to RAG architectures. Thus, developers can efficiently connect LLMs to databases without latencies, transforming foundation models into productive workplace assistants.

3. Standardizing Zero Trust Security for Autonomous AI Workflows

As organizations transition from basic chat interfaces to autonomous AI agents that make API calls and modify backend data, security risks scale exponentially. Cloudflare’s platform establishes a critical industry benchmark: Zero Trust AI Architecture. Enforcing identity verification, prompt inspection, and rate-limiting at the network layer ensures that enterprise AI agents operate within strict, auditable security perimeters without sacrificing execution speed.

Overall Impact of Businesses Working in the AI and Tech Sphere

Cloudflare AI Workspace expansion creates distinct trends in the operations and competitive landscape of AI startups, software providers, and enterprise technology buyers:

Reduced Time to Market for Enterprise AI SaaS: ISVs and AI startups developing B2B applications can take advantage of Cloudflare’s global edge network, rather than having to build a custom serverless backend or GPU clusters, thus reducing the cost of entry and operational expenses.

Decreased Total Cost of Ownership (TCO) for AI Processing: Sending AI inference through optimized, serverless edge compute saves bandwidth inflation and eliminates expensive cloud egress fees, making it possible for enterprises to scale AI deployment across thousands of employees while not incurring unexpected cloud infrastructure costs.

End of Standalone AI Applications Lacking Governance: Technology decision makers and IT procurement teams will be more and more inclined to refuse standalone AI applications that don’t have governance, edge security, and privacy built into their products. Market dominance will fall on the platforms able to deliver native AI infrastructure that is secure, fast, and compliant.

Conclusion

The new collaborations by Cloudflare with respect to its AI Workspace Platform is a very critical milestone in the development of the enterprise AI market segment. Through its approach in linking the global edge compute, instant vector search, and Zero Trust security all under one roof, Cloudflare is able to make a connection between the development of AI and actual enterprise productivity. In relation to the wider AI market, it becomes clear that future success in AI will hinge on this capability.

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Also Read: AtScale Unveils ‘AtScale for AI’ to Resolve Enterprise AI Business Logic and Accuracy Challenges 

“AI can be the great equalizer, but so far its biggest wins have gone to the Fortune 100. We want to give every enterprise the same opportunity,” said Rahul Pangam, co-founder and CEO of RapidCanvas. “Vibe coding has made it easy to start building with AI. Getting AI into production reliably and safely still remains the biggest gap. Our growth over the past six quarters is evidence that AI in enterprises is moving beyond pilots into production and real operations.”RapidCanvas will use the funding to expand its go-to-market operations, strengthen partnerships with private equity and advisory firms, and grow its AI engineering teams across the United States, Brazil, and India. It also plans to invest in platform development and governance capabilities.

Read More: RapidCanvas Raises $29M Series B to Bring Fortune 100-Grade AI to Every Enterprise

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Three Developments Worth Watching

1. DigitalOcean builds for self-improving AI agents: Managed memory, data services and feedback loops could simplify the development of autonomous applications.
2. NVIDIA and Cisco target AI infrastructure bottlenecks: High-density optical connectivity aims to support larger AI systems with greater efficiency.
3. HighByte and Databricks connect industrial data with AI: Governed, contextualized factory data could help manufacturers move from isolated experiments to operational use cases.

These developments matter because AI progress increasingly depends on what surrounds the model: reliable infrastructure, usable data and the ability to translate insights into action.

5 Technology News Stories

1. DigitalOcean Introduces Agent Learning and Data Services

DigitalOcean has introduced managed services for autonomous AI agents, combining persistent memory, vector data management and continuous feedback on its cloud infrastructure.

The move addresses a practical barrier to agent adoption: developers often need to assemble multiple services to maintain context and improve agent performance. Integrating these capabilities could reduce development complexity and make costs easier to manage, particularly for startups. However, self-improving systems still require evaluation, safeguards and human oversight. Easier deployment will matter most when agents perform reliably in real-world workflows.

2. NVIDIA and Cisco Collaborate on High-Density Optical Modules

NVIDIA and Cisco are developing a high-density, pluggable optical interface designed to connect increasingly large AI infrastructure and GPU clusters.

As AI workloads scale, networking bandwidth, power consumption and heat increasingly constrain data-center design. More efficient optical connectivity could help operators expand computing capacity without proportionally increasing networking overhead. A standardized approach may also simplify deployment across infrastructure ecosystems. The broader lesson is that AI scaling depends on more than faster processors. Connectivity and energy efficiency are becoming equally important considerations in infrastructure investment.

3. Xero Integrates Google Gemini for Financial Analytics

Xero is integrating its accounting intelligence with Google Gemini, allowing small businesses, advisors and bookkeepers to query financial data and generate reports through natural-language prompts in Google Workspace.

The integration could reduce the time small businesses spend switching between accounting dashboards, spreadsheets and documents to understand cash flow or investigate financial trends. It also brings AI closer to everyday decision-making instead of requiring users to learn separate analytics tools. The key test will be whether answers remain accurate, secure and useful enough to support financial decisions without replacing proper verification.

4. Atos and Fortinet Expand Their Global Cybersecurity Partnership

Atos and Fortinet are expanding their partnership into a globally coordinated cybersecurity model for hybrid and multicloud environments, combining security technology with consulting and managed services. The arrangement draws on Atos’s network of 17 security operations centers.

For multinational organizations, a more unified approach could reduce fragmented security policies, inconsistent regional contracts and visibility gaps across cloud environments. It also reflects demand for managed protection as security teams face complex threats and talent shortages. However, vendor consolidation only helps if organizations retain clear oversight, consistent service quality and measurable improvements in threat detection and response.

5. HighByte and Databricks Make Industrial Data More Accessible for AI

HighByte and Databricks are extending their integration to stream contextualized industrial data into the Databricks Data Intelligence Platform, supporting analytics, machine learning and agentic AI applications.

Manufacturers often hold valuable operational data across disconnected systems, making it difficult to use consistently in AI projects. Connecting industrial data with governed analytics infrastructure could reduce custom integration work and help teams apply insights to quality, productivity and maintenance. The challenge remains data quality and governance. AI cannot produce dependable operational decisions when underlying factory data is incomplete, inconsistent or poorly contextualized.

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Future of Digital Payments: How Fintech Disruption Is Reshaping the Global Financial Ecosystem

Digital payments are evolving through real-time transfers, digital wallets, embedded finance and AI-driven fraud detection. This article examines how these technologies are changing customer expectations, business cash flow and financial inclusion. It also explores persistent challenges, including cross-border friction, security risks, regulation and system integration. Its central argument is that faster payments create value only when businesses combine convenience with reliability, interoperability and customer trust.

ITDigest Weekly Observations

This week’s announcements point to a shift from isolated technology upgrades towards connected systems built for practical use. AI agents need reliable memory and governed data; AI infrastructure needs efficient networking; businesses need financial and operational insights they can act on; and cybersecurity must scale across increasingly complex environments.

The common challenge is execution. Integrations can reduce friction, but they cannot compensate for weak data, poor governance or unclear business objectives. As these technologies mature, the next question is: Which organizations will turn these connected capabilities into measurable productivity gains, and which will remain stuck in implementation?

The post ITDigest Weekly Roundup: Self-Improving AI, Smarter Infrastructure and the Future of Digital Finance appeared first on ITDigest.

Also Read: NetSuite Unveils Free AI-First Financial Platform to Accelerate Startup Scale from Idea to IPO

“The AI industry keeps telling enterprises to buy a smarter model, but a model can’t reason its way to your definition of revenue,” said Chris Lynch, CEO of AtScale. “Enterprises need to define what an answer means, compute it correctly, and prove it holds up. AtScale for AI brings those capabilities together so accuracy becomes something businesses can measure, improve, and demand.”The platform aims to address a common challenge in enterprise AI: models may interpret metrics such as revenue differently unless organizations provide clear definitions and consistent calculation rules.

Read More: AtScale Launches AtScale for AI to Close the Enterprise AI Accuracy Gap

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Also Read: Zones and Thoughtworks Partner to Bridge the AI Execution Gap for Enterprise IT

Deutsche Telekom uses Xurrent’s incident response platform to help maintain service reliability during major live events and keep customer-facing services running smoothly.”Engineers have AI to help them write code, but when that code breaks, they are still left searching through logs, dashboards and past incidents to understand what happened,” said Brian Wenngatz, CEO of Xurrent. “The next step is bringing that same level of AI support to incident resolution, giving engineers the context they need to move faster while keeping their expertise and judgment at the center.”Xurrent includes its AI capabilities across the platform without additional charges for premium AI features.

Read More: Xurrent Extends Sera AI Into an AI SRE teammate to Help Engineering Teams Resolve Incidents Faster

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Also Read: FIS Integrates Agentic AI Across Financial Intelligence Network to Help CFOs Forecast Cash Flow and Mitigate Market Risks

The workflow examines customer-specific requirements, reviews orders, and recommends next steps, while employees continue to make the final decisions. “Customers are challenged with figuring out how AI can make a difference in their business,” said Jeff Cobb, SVP, Integrated Solutions, Zones. “They may have pilots underway, but don’t always have the strategy, infrastructure, skills or resources to take the next step. Through our partnership with Thoughtworks, we’re helping customers identify the right opportunities and then giving them the technology and expertise to turn those opportunities into results that matter to their organization.”

Read More: Zones Expands AI Services with Thoughtworks Partnership to Help Customers Turn AI Into Business Results

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This collaborative solution offers B2B manufacturers and distributors with a governed, centralized, and auditable approach to pricing, as well as related deal and margin risk analysis, directly on their enterprise data lakes they are already using in Databricks. By connecting pricing analytics to the lakehouse, organizations can realize the full potential of their data investment.

Bridging the operational gap between enterprise lakehouses and thriving Commercial Execution.

The transactional transaction history, product costing matrix, customer history, and market-aware telemetry for the biggest B2B transactions is in Databricks. Until now, offloading that data into a third-party pricing system has required complex custom ETL pipelines, schema mappings, and reporting loops that are slow to catch up with the real-time price changes in the market.

No more technical friction with Pricefx-native Databricks integration your commercial users have fresh, high-quality financial data and your IT and governance owners retain control and sovereignty of data with Databricks Unity Catalog.

Also Read: Xero Launches Google Gemini Integration, Enabling Natural-Language Financial Analytics for Small Businesses

Pricefx is to expand the integration over three separate operational milestones to Cater to different levels of corporate data maturity:

Pricefx Data Connect for Databricks: Enables Pricefx to directly connect to Databricks as a governed enterprise data source. Pricefx accesses the user’s Unity Catalog-based auto table discovery to refresh price lists, active quotes and rebate approvals on demand or via scheduled triggers all without custom pipeline code.

Pricefx Zero-Copy for Databricks (Coming Soon): Allows Pricefx applications to execute directly on lakehouse data with open zero-copy data sharing. Eliminates the need for copy-and-sync of datasets, between, databases, and gives pricing, finance, and analytics teams all a common single source of truth with fully auditable price points.

Pricefx Agents on Databricks Marketplace (Coming soon): Delivering domain focused pricing AI agents and a new MCP app with their own dedicated set of tools straight into the Databricks Marketplace. Analysts will be able to use Databricks Genie to analyze price waterfalls, margin leakage and deal structure using natural language prompts within their workspace.

Unfiltered Industry Statements and Executive Perspectives

“Enterprises have invested heavily in Databricks to bring their data together, and pricing is where that investment should pay off directly in margin,” said Ronak Sheth, CEO of Pricefx. “This partnership lets our customers price on the data they already trust, without customized integrations or waiting on stale reports. Whether a company wants to start by connecting its data or run Pricefx natively on its lakehouse, we’re meeting them where their data lives and turning it into profitable growth.”

“Customers want to get more out of the data they have already unified and governed in Databricks, and pricing is one of the most direct routes from data to business outcomes,” said Jason Pohl, Built-On CTO and Founder of Databricks Labs at Databricks. “By building on the Databricks Data + AI Platform, Pricefx brings AI-powered pricing to that data while it stays under Unity Catalog governance. That gives our joint customers a new, high-value use case on the platform they already run, and a faster path from insight to action.”

Unlocking Autonomous Agentic Workflows for Commercial Teams

At all stages of the integration, commercial users have unrestricted access to core Pricefx functionality (Price Setting Quoting Rebate Management, Agreements, and AI Deal Tuning), and IT teams maintain all security and permissions in Databricks.

With enterprise context validated in real time, the partnership creates the foundation for scalable agentic pricing, providing companies with a way to automatically flag deal outliers, optimize pass-through pricing and book up profitable revenue growth.

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Also Read: FIS Integrates Agentic AI Across Financial Intelligence Network to Help CFOs Forecast Cash Flow and Mitigate Market Risks

“We have already demonstrated that AI can deliver meaningful impact across Manulife, from improving experiences for customers, agents, and colleagues to driving measurable business value,” said Phil Witherington, President and CEO, Manulife. “The platform positions us to move beyond individual solutions to a shared enterprise capability that encourages and enables teams around the world to innovate, collaborate, and scale AI responsibly. This is a critical next step toward our strategic priority to become a truly AI-powered organization.”

Read More: Manulife Launches Enterprise AI Platform to Scale AI Solutions and Deliver Business Value Across Global Operations

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