Artificial intelligence is reshaping both commercial enterprises and federal agencies—unlocking new efficiencies, redefining workforces, and driving sustainable growth. At AI Nexus: Powering Innovation Together, industry and government leaders came together to explore responsible development, seamless implementation, and strategic use of AI. The event featured expert-led panels and real-world demos, covering agentic AI, compliance, clean data, and success stories. Attendees gained actionable insights and connected across sectors to advance enterprise AI adoption. It’s time to power innovation—together.
Explore what is possible and how these technologies can transform your organization. Reach out to our KPMG Ignition team to co-create a tailored experience that accelerates your journey.
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Some or all of the services described herein may not be permissible for KPMG audit clients and their affiliates and related entities.
The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act upon such information without appropriate professional advice after a thorough examination of the particular situation. The views and opinions expressed herein are those of the interviewees and survey respondents and do not necessarily represent the views and opinions of KPMG LLP.
© 2025 KPMG LLP, a Delaware limited liability partnership and a member firm of the KPMG global organization of independent member firms affiliated with KPMG International Limited, a private English company limited by guarantee. All rights reserved.The KPMG name and logo are trademarks used under license by the independent member firms of the KPMG global organization.
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Patrick Ryan National Managing Partner Advisory Strategy and Markets, KPMG LLP
Photos from the event
Session recaps
AI Journey to Value Creation
Exploring the journey of integrating AI into business, highlighting the challenges of ROI, the need for organizational change, evolving human roles, and key ethical and security considerations.
The Future of Agentic AI
A conversation with Microsoft Federal's Chief Technology Officer and their cutting-edge approach to integrating agentic AI into its platforms
Chief AI Officer Fireside Chat
The crucial role of CAIOs in AI implementation and activation.
Getting Your AI House
in Order
Hear how federal and commercial leaders are preparing their data for AI implementation.
The Future of Agentic AI
Moderator: Swami Chandrasekaran, Global Head of AI and Data Labs, KPMG LLP Panelist: Jason Payne, Chief Technology Officer, Microsoft Federal
Takeaway 1: AI Agent Lifecycle Requires More Than Just Technology—It Demands Change Management
Swami emphasized that as AI agents grow in complexity and scale, managing their lifecycle involves not just technical upgrades but also human trust and organizational processes. He projected a rapid evolution: “In 2025, maybe I’m going to build a multi-agent system with a dozen agents… by 2027, possibly hundreds, if not thousands of agents handling tasks over months.”
He warned that replacing agents isn’t as simple as swapping software: “We’ve built a level of trust… that change is going to be harder. That lifecycle has to be less IT and more change management.”
To support this, KPMG uses a three-part agent control system:
Technology: Data upkeep, accuracy, trust metrics
People: Agent managers and stakeholders
Processes: Onboarding, performance evaluations, retirement protocols
Takeaway 2: Government Innovation Is Slowed by Compliance—Platform Approaches Can Help
Jason highlighted that compliance and policy are the biggest barriers to AI innovation in government: “There is nothing that slows down innovation in the government more than compliance and policy.”
He advocated for a platform approach that supports different user personas—creators, customizers, and consumers of AI: “You need a platform that people can interact with based on their persona, responsibilities, and desired outcomes.”
This approach helps organizations meet diverse needs while maintaining governance and flexibility. Jason also reminded attendees of AI’s rapid improvement: “The AI you’re using today is the worst AI you’re ever going to use—it only continues to get better."
Takeaway 3: Trust in AI Agents Is Built Through Transparent Evaluation and Human Oversight
Swami acknowledged the challenge of measuring agent performance: “The jury’s out there in terms of how to measure agent performance.”
KPMG developed the Agent IQ score, which evaluates:
Goal understanding
Planning consistency
Tool usage
Human-in-the-loop involvement
Output quality
“Can I evaluate the goal, the plan consistency, the tools used, whether humans were involved, and whether the output was correct?”
He stressed that visibility into agent operations is key to building trust: “You have to provide the trust to the end users… see through their lens and provide visibility into what they’re looking for.”
Conducting the AI Symphony
Top 5 "To Do Tomorrow" actions for AI reinvention
Trust in the AI Era
AI risks, regulations, and best practices to address
AI Journey to Value Creation
Speaker: Kevin Bolen, Partner, KPMG LLP
Takeaway 1: AI Value Isn’t Linear—It’s a Portfolio Kevin emphasized that capturing value from AI requires a strategic mix of opportunities rather than a simple ROI equation. Organizations must look beyond cost savings and explore how AI can enhance resilience and long-term competitiveness.
“The value is going to come in the form of a portfolio of value opportunities. It’s not going to be a completely linear math that says I spend $200 in AI, I got $400 back.” - Kevin Bolen Takeaway 2: Change Management Is the Hidden Barrier The biggest challenge in AI adoption isn’t the technology—it’s the human element. Kevin highlighted how failing to address workforce anxiety and workflow integration can stall progress, turning AI from a liberating tool into a source of uncertainty.
“The technology is outpacing our ability to adopt it into our workflows... Instead of a liberating tool, we’ve now introduced anxiety because they’re, wait, well, if it takes 10 hours now, what if it takes 20 hours?” - Kevin Bolen Takeaway 3: AI Will Reshape Organizational Structures With AI’s ability to outperform traditional systems, Kevin predicted a future where some roles may disappear or shift dramatically. Enterprises must prepare for structural changes and consider outsourcing AI capabilities when internal data depth is lacking.
“We’re going see a radically different set of organizations and enterprises in the next 5 or 10 years... Some of those roles may not exist going forward because it’s just no longer efficient to have it there.” - Kevin Bolen
Getting Your AI House in Order
Moderator: Jim Pettit, Director, Federal Advisory, KPMG LLP
Panelists: Mark Ramsey, Senior Director & Chief Data & Analytics Officer, Volkswagen
Scott McAllister, Director, Federal Advisory, KPMG LLP
Data Readiness Is Foundational Clean, well-organized, and domain-specific data is essential for AI success. Both Mark and Scott stressed that poor data quality—especially from third-party sources—can cripple even the most promising AI applications.
Jim Pettit: “AI is like a brilliant instruction manual, but it can’t do its job if it has to go through a mountain of useless, broken, missing pieces.” Mark Ramsey: “The lack of leveraging that data will impact us in terms of competitive advantage, new business opportunities, and customer experience.” Technology Alone Won’t Scale Scott warned against focusing solely on tech tools. Without aligning people, processes, and policies, AI platforms risk becoming siloed and unused. A holistic strategy is key to sustainable implementation.
“Only thinking about the technology is the most common like ubiquitous mistake I’ve seen. It’s going to sit on a shelf if you’re not thinking about the people, processes, and policies.” AI Is Driving Real-Time Transformation Mark shared how Volkswagen uses real-time vehicle data to improve customer experience and safety. This shift toward predictive and responsive AI applications is redefining roles and unlocking new business opportunities.
“We have more and more real-time data about our vehicles... We can not only use that data for predictive analytics, but also help them real-time, for example, with accidents or low-collision accidents.”
Onsite at the event, we leveraged our AI capabilities to enhance the speed and efficiency of capturing insights. We recorded the sessions and, in real-time, processed these videos through our AI application to extract key takeaways and quotes from each presentation. Additionally, we incorporated insights from the shared slides to provide a comprehensive summary of each session. To ensure the accuracy and reliability of these summaries, they were reviewed and refined by subject matter experts. This approach allowed us to condense what would typically take a week into just one day, demonstrating the power of AI in streamlining processes and delivering timely insights.
Balancing Innovation with Responsibility
Moderator: Aisha Tahirkheli, Managing Director and AI Leader, KPMG LLP Panelists: Adam Russell, Director of the AI Division, USC’s Information Sciences Institute
Scott McAllister, Director, Federal Advisory, KPMG LLP Mo Ezderman, Director of Artificial Intelligence, Mindgrub
Takeaway 1: AI Governance Is a Catalyst for Innovation—Not a Constraint
The panel emphasized that governance, when thoughtfully designed, can actually accelerate innovation by creating safe, ethical pathways for experimentation.
Mo Ezderman stressed the importance of embedding ethics from the start: “Ethics is something you need to think about day one.” He shared how a watermark feature—originally added for ethical reasons—became the most profitable part of their generative AI music platform.
Adam Russell brought a sociotechnical lens to governance: “Anthropology was a perfect discipline for AI because it begins to teach you to understand this technology from a much broader, almost sociotechnical perspective.” He advocated for adaptive governance frameworks that evolve with the technology and its risks.
Scott McAllister likened governance to a seatbelt: “Governance can be implemented in a way that supports rapid innovation without compromising security.” He described KPMG’s proxy system for managing API access to LLMs, which gives developers “golden paths” to innovate safely.
Takeaway 2: AI Agents Must Be Onboarded Like Employees—with Oversight and Accountability
Deploying AI agents isn’t just a technical task—it’s an organizational one.
Mo Ezderman proposed treating AI agents like new hires: “When you deploy an agent in your organization… onboard them like employees.” This includes access control, alignment checks, and regular reviews.
Scott McAllister emphasized the need for secure model context protocols and observability: “We need logging, monitoring, metrics, observability, and traceability.” He warned that current protocols connecting LLMs to systems are not yet secure.
Adam Russell used a vivid analogy: "We brought on an alien intern and now you're teaching an alien intern not to punch a hole in the Coke machine and take a soda. It's never seen this before, doesn't know what it's doing." This analogy underscores the need for careful management and continuous monitoring of AI agents to prevent unintended consequences and ensure they operate within ethical and secure parameters.
Takeaway 3: The Future of AI Is Natural, Collaborative, and Creativity-Enhancing
The panelists shared a vision of AI that enhances—not replaces—human creativity and collaboration.
Mo Ezderman predicted a shift in how we interact with technology: “In five years, 10 maybe… keyboard is out, glasses are on. We’re going to start lifting our heads up and start interacting with the world in a very natural way.”
Scott McAllister expressed excitement about academic breakthroughs: “I’m really pumped to see what our eggheads and universities churn out next.”
Adam Russell envisioned AI as a tool for amplifying collective intelligence: “AI gives us a chance to benefit from [our intelligence] at new scales that would have been impossible when we’re limited to things like the Dunbar number.”
Trust in the AI Era: AI risks, regulations, and best practices to address Panel Discussion with: Kelly Combs, Managing Director Advisory, Emerging Technology Risk Services, KPMG US, Phane Mane, Global Architect, Boston Scientific, Barbara Widholm, Vice President, Global Technology, State Street, Ben Lavallee, Director, Google Cloud
Takeaway 1: AI Governance Continuously Evolves, and It's Crucial for GenAI Implementation In the early days of AI governance, the focus was centered around a few key principles like accountability, explainability, and fairness. However, as AI technology advances, especially with the advent of generative AI, the focus of governance is shifting and expanding to include additional principles like safety, security, data privacy, and sustainability. "AI governance is not a new topic," Combs explained. "But as you move into generative AI, we really need to start thinking about additional principles that we may not have focused on in the past, or may not even be in our governance model." She emphasized that AI security is the hottest growing principle right now and is an area of particular skill set. KPMG has developed a strategic approach and framework called Trusted AI to design, build, deploy, and use AI solutions in a responsible and ethical manner. They prioritize values-driven, human-centric, and trustworthy AI implementation. KPMG's Trusted AI rests on ten ethical pillars across the AI lifecycle. These pillars include fairness, reliability, transparency, security, explainability, safety, accountability, privacy, data integrity, and sustainability. Combs also shared some of the initial activities that organizations need to focus on when setting up a governance program: creating a value statement, building a playbook of guidelines, maintaining an inventory and risk rating it, and training and awareness.
Takeaway 2: Navigating the Use of Sensitive Data in Early GenAI Applications Businesses are exploring a wide range of use cases for generative AI, from internal applications such as improving customer service and simplifying legal agreements, to external applications such as summarizing hotel reviews for customers. However, the adoption of generative AI for these use cases requires careful consideration of the associated risks and challenges, especially when sensitive data is involved. Phane Mane from Boston Scientific shared how they are experimenting with use cases that are internal and not customer-oriented for safer operations. "Obviously we are in the healthcare industry, so patient security information is very critical for us," Mane explained. "So, a lot of the use cases we're actually experimenting with include GenAI use cases that are not patient or customer-oriented, mostly for internal consumption." Barbara from State Street also shared possible use cases in the finance industry such as creating chatbots for clients to query about their holdings and impact of market news. However, she stressed the importance of data security and privacy, as well as the need for transparency in the use of AI.
Takeaway 3: Companies need to focus on data readiness and ethical considerations when implementing generative AI Companies need to ensure that their data is organized, accurate, and AI-ready before implementing generative AI. They should also keep their governance principles for AI clear and focused. Ethical considerations and transparency in the use of AI should be at the forefront of any implementation. Ben Lavallee from Google Cloud addressed this by saying, “My personal belief is that most companies should very much focus on the data and the application layer, and really the data, because especially if you're going to be launching use cases that might be slightly sensitive whether it be in financial services or healthcare that require greater precision or accuracy, having your data estate as AI-ready as possible is definitely where I would focus your time and energy if you're just getting started.” Meanwhile, Barbara stressed the importance of taking people along the AI journey through continuous training and education, “How you bring people to the journey with you in terms of upskilling, reskilling and then evaluating what really resonated? What we did at State Street last year, was a hackathon where we basically brought in people from part of the organization that were non-developers, but they understood the business really, really well to figure out what are the pain points and understand existing challenges.”
Conducting the AI Symphony - Top 5 "To Do Tomorrow" actions for AI reinvention Q&A Forum with: Charles Arnold, Kevin Bolen, Mike DiClaudio, Richard Entrup
Takeaway 1: AI will not necessarily replace jobs but will transform them The panelists emphasized that AI isn't here to replace jobs, but to transform them. They noted that the use of AI and automation will enable employees to focus on higher-level, strategic tasks rather than mundane, repetitive ones. "Even if computers become perfect at text communication, there’s still so many in-person interactions that are hard to automate," said Mike DiClaudio. This indicates that while AI can automate certain tasks, complex social interactions and decision-making processes still require human input. Kevin Bolen further highlighted the transformative potential of AI, stating, "Agents will become really great at doing. That's a fundamental difference. And so, as we think about the Gen AI and its current form, it's very good at speaking to us, but it doesn't do much right now. It will."
Takeaway 2: AI can potentially streamline data preparation for businesses, but it depends on the organization's maturity and data strategy. The panelists discussed the role of AI in data analysis and noted that while AI can summarize and provide insights from data, it may not fully understand the context or provide a nuanced interpretation. "AI could be used to prepare the data that you currently have in order to prepare net-new models," said Richard Entrup. However, Kevin Bolen pointed out a limitation, stating "I'm not as excited as I would like to be at its ability to analyze data. So I think what I see is a lot of summarizing data, but it isn't telling me what's going on. It's telling me what it sees." A participant asked about AI's capability in speeding up the data preparation process for businesses that don't yet have all their data organized and ready for use. While panelists agreed that AI could be used to assist in preparing data, they also emphasized the importance of a sound data strategy. Charles Arnold suggested that, "You need good quality data in order to leverage AI. So your existing chief data officers and all of the efforts that have been put into developing sound data governance and data strategies and other data frameworks will be very important here, depending on the maturity of that." Kevin Bolen further added that the quality of AI tools can be measured by how often people use it, indicating the value it adds.
Takeaway 3: AI adoption requires careful planning and consideration of ethical implications One of the key takeaways from the discussion was the importance of careful planning and consideration of ethical implications in AI adoption. The panelists emphasized the need for a clear strategy and an understanding of the potential impact of AI on different roles. "None of us is as smart as all of us," said Kevin Bolen, highlighting the importance of collective intelligence in navigating the complexities of AI. He also stressed the need for a trusted framework and guiding principles in the AI adoption journey. Charles Arnold echoed these sentiments, stating, "Moral compass is non-negotiable. So integrating ethical considerations is key here." He also emphasized the importance of data strategy and leveraging cloud platforms for scalable AI adoption.
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John Capone Managing Partner, New England & Upstate New York KPMG LLP
The KPMG GenAI Executive Accelerator shed light on the transformative potential of generative AI and its implications for businesses. By embracing generative AI, organizations can unlock new levels of innovation, efficiency and productivity. However, to fully harness the power of AI, organizations must prioritize AI governance, optimize their data, and anticipate and address the challenges associated with AI implementation. As we navigate the AI era, it's crucial for businesses to adapt, innovate and embrace the opportunities presented by generative AI. With the right strategy and ethical considerations, generative AI can be a catalyst for business transformation.
Generative AI is rapidly transforming our industries, functions, and productivity. The Boston KPMG GenAI Executive Accelerator event brought together industry leaders and KPMG SMEs to discuss the importance of governance, data security, optimization, customer experience, and implementation challenges.
See event highlights in this 2-minute video
See event highlights in this 2-minute video
AI Nexus:
Powering Innovation Together
Washington, D.C. | 09.10.25
National Managing Partner
Advisory Strategy and Markets, KPMG LLP
Patrick Ryan
Principal Federal Advisory
KPMG LLP
Tom
Frame
Principal
Advisory
KPMG LLP
Martin
Kaestner
Balancing Innovation with Responsibility
Ensuring ethical and secure use of AI technologies, focusing on data governance, transparency, and compliance.
AI Champions Panel
Discover real-world use cases of successful AI implementations in both commercial and federal organizations.
AI Champions Panel
Moderator: Martin Kaestner, Principal, KPMG LLP Panelists: Adam Branch, CEO, Rhino.ai Mia Jordan, Digital Transformation Executive, Salesforce Adarryl Roberts, CIO, Defense Logistics Agency (DLA)
Takeaway 1: AI Adoption is Accelerating Across Sectors, but Human Oversight Remains Essential
Government and commercial organizations are actively exploring AI integration, though most are still in the early stages. Adarryl Roberts (DLA) acknowledged that even large agencies are just beginning their AI journeys: “Like most government agencies, we're early in the journey… I think commercially at scale, everyone's still learning.”
This cautious approach reflects a broader industry trend: while AI tools are being deployed, organizations are prioritizing human validation and measurable impact. Mia Jordan (Salesforce) emphasized that adoption hinges on user experience: “I build these AI apps… how do I create a user experience that is actually value added and drives adoption?”
The panel agreed that successful AI implementation isn’t just about deploying technology—it’s about ensuring it’s intuitive, trusted, and aligned with real-world workflows.
Takeaway 2: The Future of AI is Agent-driven, Flexible, and Mission-specific
Panelists forecasted a shift from traditional SaaS models to agent-to-agent architectures, where AI systems autonomously collaborate while humans oversee and validate outcomes. Adam Branch (Rhino.ai) described this evolution: “Agents talking to agents and the humans are doing the orchestration and validation.”
Adarryl Roberts reinforced that AI should augment, not replace, human decision-making: “We are going to demand that we still keep humans-in-the-loop… AI should help me execute and deliver on a business process from start to end.”
Mia Jordan added that deployment flexibility is key—especially in federal environments with varying security and infrastructure needs: “Some things you need on-prem depending on your mission set… but it’s SaaS incorporating native AI capabilities.”
This vision of AI is modular, adaptive, and human-centered, designed to meet diverse operational demands while maintaining control and accountability. Takeaway 3: Edge Computing is Vital for AI Resilience in Disconnected Environments
The panel emphasized the growing need for AI systems that can operate independently of constant connectivity—especially in federal and emergency response contexts. Adarryl Roberts (DLA) explained that in scenarios like cyber conflicts or declared emergencies, disconnected operations are essential: “We can't be disconnected. We can't have decisions being held up because there's not connectivity.”
He illustrated society’s digital dependence with a relatable example: “Take away Amazon… how many people carry cash? How many places accept cash? We are so digitally connected and dependent.”
Mia Jordan (Salesforce) added that agencies like the National Weather Service face similar challenges in rural areas: “They have thousands of people in the field… but they’ve been asking about disconnected mode because there have been many instances where you can get people in, but you need to be able to send information out.”
These insights underscore the importance of edge computing and offline-capable AI for mission-critical operations where real-time decision-making must continue—even without internet access.
Demo Derby
Experience the power of AI in action through five interactive demonstrations.
Demo Derby
Demo 1: METIS – AI-Enabled Situational Report Tool Presenters: AJ Pasagian & Isaac Sutor
Key Takeaway METIS helps compress decision-making timeframes in high-stakes environments by automating the collection and synthesis of relevant information.
Highlights
Designed for crisis response and operational agility.
Enables rapid situational awareness using AI to surface key data points.
Ideal for disconnected or decentralized operations.
Demo 2: Audit Concierge – Knowledge Management Tool Presenters: Jon Edge & Isaac Sutor
Key Takeaway Audit Concierge bridges the gap between junior and senior staff in regulatory-heavy environments by using AI to surface institutional knowledge.
Highlights
Uses LLMs to summarize audit issues and provide guidance.
Allows users to interact via natural language to resolve flagged items.
Enhances decision-making and compliance through intuitive interfaces.
Demo 3: Software Development Lifecycle Automation
Presenter: Thilina Gunarathne
Key Takeaway Gen AI accelerates software development by automating repetitive tasks and guiding developers through the lifecycle.
Highlights
Applies generative AI to streamline code generation, documentation, and testing.
Reduces manual effort and improves development velocity.
Supports secure and scalable deployment across platforms.
Demo 4: Intelligent Forecasting Presenter: Tim Haley
Key Takeaway AI enhances financial forecasting accuracy by modeling economic signals and enabling scenario planning.
Highlights
Identifies sensitivity of business metrics to economic drivers.
Supports scenario planning with external data feeds (e.g., recession, tariffs).
Traditional models like ARIMA outperform LLMs in low-data environments.
Demo 5: Contract Leakage Tool Presenters: Andy Neville
Key Takeaway The Contract Leakage Tool detects pricing mismatches and contractual discrepancies in real time, helping prevent fraud and reduce costs.
Highlights
Uses OCR, NLP, and LLMs to compare contract terms with invoices.
Flags discrepancies and provides summaries for remediation.
Saves clients 2–10% in costs by identifying leakage issues.
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KPMG LLP
Philip
Wong
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More info here
KPMG LLP
Peter
Irwin
Principal
Supply Chain Leader
KPMG LLP
Mary
Rollman
Profile
Department
First_Name
Last_Name
Managing Director
C&O Digital Insurance
KPMG LLP
Alissa
Ristic
Profile
Department
First_Name
Last_Name
Managing Director and Head of Emerging Solutions
Enterprise Innovation KPMG LLP
Richard
Entrup
Profile
Department
First_Name
Last_Name
Principal
Advisory - Strategy
KPMG LLP
Per
Edin
Profile
Department
First_Name
Last_Name
Partner
Audit
KPMG
Samantha
Demty
Deputy Chief Data and Artificial Intelligence Officer
U.S. Department of the Navy, Office of the CIO
Stephanie
Woodring Tutko
Managing Director
Trusted AI
KPMG LLP
Aisha
Tahirkheli
Director of AI Division
USC Information
Sciences Institute
Adam
Russell, Ph.D
Chief Information Officer Defense Logistics Agency
Adarryl
Roberts
Senior Director &
Chief Data &
Analytics Officer
Volkswagen
Mark
Ramsey
Director
Federal Advisory
KPMG LLP
Jim
Pettit
Chief Technology Officer
Microsoft Federal
Jason
Payne
Director
Federal Advisory
KPMG LLP
Scott
McAllister
Principal
Advisory
KPMG LLP
Martin
Kaestner
Digital Transformation Executive
Salesforce
Mia
Jordan
Principal Federal Advisory
KPMG LLP
Tom
Frame
Director of Artificial Intelligence
Mindgrub
Mo
Ezderman
Principal
Global Head of AI & Data Labs KPMG LLP
Swami
Chandrasekaran
CEO
Rhino.ai
Adam
Branch
Partner
Boston, MA
KPMG LLP
Kevin
Bolen
National Managing Partner
Advisory Strategy and Markets, KPMG LLP
Patrick Ryan
Speakers and Discussion Leaders :
Some or all of the services described herein may not be permissible for KPMG audit clients and their affiliates and related entities.
The information contained herein is of a general nature and is not intended to address the circumstances of any particular individual or entity. Although we endeavor to provide accurate and timely information, there can be no guarantee that such information is accurate as of the date it is received or that it will continue to be accurate in the future. No one should act upon such information without appropriate professional advice after a thorough examination of the particular situation. The views and opinions expressed herein are those of the interviewees and survey respondents and do not necessarily represent the views and opinions of KPMG LLP.
© 2025 KPMG LLP, a Delaware limited liability partnership and a member firm of the KPMG global organization of independent member firms affiliated with KPMG International Limited, a private English company limited by guarantee. All rights reserved.The KPMG name and logo are trademarks used under license by the independent member firms of the KPMG global organization.
visit.kpmg.us
learn about us
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