A great way to break through that roadblock is to listen to leaders who jumped in early to use AI to transform outcomes. , a managing director of Finance in the Global IT division at Accenture, is one of those people.

The professional solutions and services company employs nearly 780,000 employees across 52 countries, who work with 350 partners to serve over 9,000 clients. The idea of transformation at Accenture鈥檚 scale might be intimidating to some, but not Lambert. He鈥檚 leading an聽ongoing transformation聽of Accenture鈥檚 finance function, which he calls 鈥渢he heartbeat鈥� of the company.

The results he鈥檚 achieved鈥� including saving the finance team a combined 57,000 hours annually by having AI generate narrative summaries for reporting鈥攕hine a spotlight on what鈥檚 possible. And he鈥檚 just getting started.

I had a chance to speak with him about how he became a leader in AI-driven transformation, and what others can learn from his achievements. This is a lightly edited version of our conversation

Q: As you know, innovating with AI is about reshaping how a business delivers value. But not every business leader is leading the charge. Some are watching and waiting. Why did you roll up your sleeves and decide to be on the forefront?

A: Taking a leadership position on AI is important to keep moving forward and shaping new services and capabilities. For example, across a company our size, even though we鈥檙e hyper focused on emerging technologies, we can find small problems across our technology landscape. There are processes and data living in different places and silos develop over time. Most large companies have this challenge. But those are valuable processes, and the business data we have is especially valuable. AI opens up new opportunities to bridge those gaps and deliver more end-to-end outcomes, so that our finance function can meet the growing business expectations of our stakeholders.

For many companies, the key to getting impactful results from business AI is to start with one function that鈥檚 central to business performance. Why was finance the right place for you to begin, and what did you want to achieve?

I always say finance is the heartbeat of our organization. I heard one of our global IT leaders use that phrase, and while it was inspirational, it also made me think, 鈥淟et鈥檚 not accidentally cause a heart attack for the organization.鈥�

Jokes aside; he was right. Your transactional and operational data flows through finance, and management decisions sit on top of it. Starting there gave us the ability to make end-to-end impact across processes that touch procurement, liquidity, forecasting, receivables, and more. And 麻豆原创 gives us a digital core where all that transactional data is harmonized.

The bottom line is that finance is the natural starting point if you want to move from reactive reporting toward more proactive, AI-driven insights that you can use to help move the business forward. So, we set out to unify data and transform finance processes in a way that scales across the whole value chain.

Cash and liquidity are so important in the finance function, and to an entire company. But managing it requires bringing together data, forecasting, and decision-making across many teams. How did AI help?

If finance is the heartbeat of a company, cash and liquidity are the lifeblood of your systems. Here鈥檚 a great example: Accenture engages in a lot of acquisitions, and we run operational cash in 50-plus countries, so it鈥檚 easy for decisions to default to historical, manual reviews. That鈥檚 what was happening at Accenture before a forward-thinking leader stopped by and asked if we could apply machine learning to the problem. Great leaders often ask great questions, and that one really got us thinking.

[AI] freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth.

Eli Lambert

We took inspiration from retail: how stores treat inventory based on discounts and sales. If you treat cash like stock, you can apply those same learning models to figure out how much you really need to hold onto at any point in time. That鈥檚 how we built what we call 鈥淚ntelligent Cash.鈥� It brings all the business data together into a single data mart, a repository for structured data for a specific department or line of business, and uses machine learning to generate recommendations that our teams can act on.

AI is so good at this, and here鈥檚 what鈥檚 incredible: It freed up 20% of our idle cash, which we could then move into global operations to fund acquisitions and strategic growth. Now what used to take months, or even more than a year to build, we can now do it in days or weeks because 麻豆原创鈥檚 data cloud brings [麻豆原创] Datasphere, Databricks, and our machine-learning workloads into one place. The result is faster decision-making, better visibility, and much more accurate forecasting.

I love hearing about how you were able to use gains, delivered through strategic AI innovation, and then channel those gains into a high-value activity for the organization.聽 I know you also worked on receivables, something that impacts cash flow and customer relationships. What pain points did you face, and how did automation and machine learning transform the process?

Receivables were highly manual compared to payables. Clearing was inconsistent, and reconciliation took a lot of time because payments often come incomplete or with partial data. Anyone who works in or near finance knows exactly what I鈥檓 talking about. So, we co-developed on the 麻豆原创 platform a machine-learning-based receivables solution. It more than doubled the automation rate for receivables processing and tripled automatic reconciliation, about a 300% improvement.

As part of that, we introduced high-confidence, one-click matching recommendations that reduce errors and cut down the manual work. We saw a seven percent uplift in auto-clearing with a cash application scheduler built on the 麻豆原创 platform that delivers matches about 77% faster. All of that adds up to a more efficient receivables process, improved cash-flow visibility, and better productivity for the team.

In a global organization like Accenture, reconciling financial data and surfacing meaningful insights can be a huge amount of work. You turned to generative AI to help, which is really smart. What led you to that approach, and how is it changing your team鈥檚 day-to-day experiences?

We were dealing with balance sheet reconciliations across 50-plus countries, and the process was decentralized. I know a lot of companies face this problem. So, first, we moved everything online. Then we brought in machine learning and generative AI to analyze cost categories, summarize data, and surface important shifts.

[Our] Intelligent Financial Advisor, built on the 麻豆原创 platform, can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That鈥檚 saved about 57,000 hours globally. Our teams can focus on higher-value analysis instead of manual reconciliation.

Eli Lambert

We then deployed an Intelligent Financial Advisor built on the 麻豆原创 platform that can generate narrative commentaries that are so accurate that over 90% are simply approved with little or no revision. That鈥檚 saved about 57,000 hours globally, just in controllership work, and helped us move to a three-day global close instead of five. The insights come faster and clearer, and the teams can focus on higher-value analysis instead of manual reconciliation. It鈥檚 also helping create more consistent roll-ups across regions and letting us use our talent more strategically.

I鈥檓 hearing this theme of not only measurable business gains from outputs, but the ability to better allocate time from manual, rote tasks to ones that deliver far more value for the business. That also applies to planning and forecasting. How did you bring AI into that part of the finance function?

Our planning work had grown too complex. Remember, we鈥檙e a large-scale, multifaceted global business. So, we replaced old models with 麻豆原创 Analytics Cloud, which gives us multi-year planning models enhanced by AI.

We applied it first to merger and acquisition modeling, where accuracy really matters. It lets us model very complex data sets and helps our finance team collaborate more easily across the business. The results have been more accurate forecasts, reduced risk of errors, and much better collaboration between executives and practitioners. Early results were strong, and that encouraged us to expand AI use in planning more broadly.

What advice do you have for leaders who are not as far along in using AI to supercharge business results?

First, start with a high-impact function tied to real outcomes. Then focus early on data quality and harmonization; it鈥檚 the foundation for everything that comes after. Then get your cadence right and your team working together. Hone in on the use cases that really matter to you鈥攖he best vendors can help you identify those鈥攁nd make sure to get the help you need from those vendors and their partners.

Use AI to spur growth. At Accenture, we鈥檝e been able to use AI to save significant cash in one area, which we then invest in another, high-growth process鈥攁cquisitions in our case. That鈥檚 how you use AI to really rethink your business and move it to the next level.

Eli Lambert, on advice to other enterprises

As you go, take a crawl-walk-run approach: start slow then increase the pace of scale and adoption over time. Be sure to invest in change management and upskilling as you go to spur learning and adoption. And partner closely with technology providers and system integrators who鈥檝e been there before. That accelerates everything.

The final suggestion I have is to use AI to spur growth. At Accenture, we鈥檝e been able to use AI to save significant cash in one area, which we then invest in another, high-growth process鈥攁cquisitions in our case. That鈥檚 how you use AI to really rethink your business and move it to the next level. And that鈥檚 possible today in ways that were not, even five years ago. Seize that opportunity.

I couldn鈥檛 agree more with Lambert. AI really does provide an opportunity to re-imagine entire business processes for greater impact.

To keep exploring what鈥檚 possible, at Accenture. Then see more AI use cases in and across all your , including procurement, supply chain, manufacturing, and more.

Brenda Bown is chief marketing officer for 麻豆原创 Business AI.

Over decades, our customers have built powerful digital foundations on 麻豆原创 to run end-to-end business processes across their enterprises鈥攐ften extending and customizing these systems to support their unique business needs. Now, many are entering the next phase of transformation: modernizing their 麻豆原创 landscapes to unlock the full potential of AI.

As companies move to cloud-based 麻豆原创 environments and clean-core architectures, they are preparing to embed intelligence directly into business processes. This enables new forms of automation, with AI agents that operate across enterprise systems and execute increasingly complex tasks.

Modernizing these systems while introducing AI at scale is a significant undertaking. It requires technologies that integrate with existing applications, operate reliably within mission-critical workflows, and meet the governance standards enterprises demand.

That鈥檚 why, over the past few years, we have partnered with to combine advanced AI technology with deep business context. Our goal is to help organizations accelerate modernization and apply AI across the applications and processes key to their success. This collaboration will be showcased at NVIDIA GTC.

Building the foundation for enterprise-grade AI

Through our collaboration with NVIDIA, we are accelerating the entire life cycle of enterprise AI鈥攆rom model development to high-performance runtime execution鈥攁nd powering AI scenarios across our portfolio. ,  which consists of open libraries such as and , helps accelerate large-scale model training across distributed RL environments. It enables teams to build and refine enterprise-grade AI models faster.

Models are hosted through  and , where our customers and partners leverage those best suited to their use cases. microservices optimize inference performance, and we have observed up to a 20% improvement compared to another popular open source serving engine. Enabled by NVIDIA GPUs and NVIDIA NIM, the increased performance allows organizations to combine advanced AI models with trusted 麻豆原创 business data and processes to ensure that AI operates within the workflows that drive business operations.

Modernizing the business logic that runs the enterprise

AI models trained on 麻豆原创 knowledge and accelerated using NVIDIA technologies are already helping customers tackle some of their most pressing modernization challenges. For example, evolving business logic embedded in the 麻豆原创 systems that run their operations.

For decades, organizations have extended 麻豆原创 applications with custom ABAP code that reflects how their businesses operate. That logic captures years of operational knowledge across finance, supply chain, service processes, and more. But modernizing these environments for the cloud and preparing them for the next generation of AI-driven innovation can be complex.

To help accelerate this journey, 麻豆原创 developed . a foundation model trained exclusively on real-world ABAP code and the business logic used across 麻豆原创 environments. The solution incorporates specialized models for code-related tasks, including StarCoder2 for code completion, and Codestral for deeper code understanding and explanations. These models are served through NVIDIA NIM microservices to deliver high-performance inference.

brings these capabilities into the developer experience, helping teams analyze existing ABAP code, understand how customizations interact with core business processes, and generate new code when needed. By making decades of embedded business logic easier to interpret and update, we help organizations accelerate modernization and preserves the knowledge that makes their operations unique.

Connecting AI to business operations

The collaboration between 麻豆原创 and NVIDIA also explores how AI can operate within enterprise workflows to help organizations apply intelligence across both physical operations and complex planning environments. One emerging area is embodied AI, in which intelligence extends beyond software systems into the physical world. By combining AI reasoning with sensors, robotics, and enterprise data, organizations can connect real-world observations directly with digital business processes.

For example, predictive maintenance alerts from can trigger robotic inspections that analyze equipment using thermal, visual, and acoustic signals. These signals are evaluated alongside asset histories and maintenance records to identify potential issues. then orchestrates follow-up actions through , prioritizing work orders and guiding technicians with the right operational context. By linking physical-world insights with enterprise workflows, organizations can turn physical-world signals into coordinated enterprise actions.

The same principle applies to complex planning environments. Supply chains today must manage a constantly shifting web of constraints, from supplier availability and transportation disruptions to evolving customer demands. With NVIDIA, we are exploring technologies, such as NVIDIA Metropolis and NVIDIA Cosmos, to bring the latest AI advancements into warehouse management, safety, and asset inspection.

Together, we are also bringing new capabilities to  that combine agent-based reasoning with the  GPU-accelerated optimization engine. This enables planners to simulate complex supply chain scenarios and evaluate alternatives with more speed and accuracy. By integrating advanced optimization with 麻豆原创鈥檚 supply chain planning capabilities, organizations can dynamically model constraints, adapt plans as conditions change, and make more confident decisions in increasingly complex environments.

Collaboration with large-scale 麻豆原创 customers helps identify real operational bottlenecks, paving the way for AI-driven solutions. . The Taiwan-based global electronics manufacturer and manufacturing solutions provider will work with 麻豆原创 to develop AI-powered innovations for manufacturing and supply chain operations.

By combining 麻豆原创鈥檚 enterprise applications and business context and Foxconn鈥檚 manufacturing expertise, organizations can enhance operational efficiency, increase resilience, and advance decision-making across complex production and supply networks.

Experience agentic AI at NVIDIA GTC

麻豆原创 is enabling Joule Agents across its application portfolio, helping organizations automate tasks and coordinate complex workflows within business processes. At NVIDIA GTC, visitors will see how these capabilities are extended using  on to build agents tailored to specific enterprise scenarios.

And because 麻豆原创鈥檚 AI architecture is model-agnostic, organizations can bring their own models into these workflows, in addition to those deployed through 麻豆原创 AI Core. The hands-on experience at NVIDIA GTC will demonstrate how organizations can build AI-driven workflows that operate directly within the enterprise systems that run their business.

It all happens at , taking place March 16-19, 2026. Join us to see how 麻豆原创 and NVIDIA are helping organizations modernize enterprise systems, accelerate AI adoption, and move toward the AI-native enterprise:

• Attend our session: on Tuesday, March 17, from 2:00-2:40 p.m.
• Visit the 麻豆原创 booth, #2001, to explore agentic AI in action, participate in hands-on vibe-coding with Joule Studio, and witness next-generation enterprise automation

.

Brenda Bown is chief marketing officer for 麻豆原创 Business AI.

New research shows how business AI delivers ROI

To better understand the opportunity to produce returns on investment from AI, 麻豆原创 commissioned that was shared at 麻豆原创 Connect. In partnership with Oxford Economics, we surveyed 1,600 executives in medium and large enterprises across eight countries.

We found that today, on average, organizations are benefiting from a 16 percent return on AI investments鈥攁 number they expect to nearly double within two years. In addition to impressive financial returns:

• 94% of business leaders say AI is improving innovation within their organizations
• 87% say AI is improving customer engagement
• 78% believe AI agents have the potential to transform their business operations

New deep research and AI assistants in Joule expand what is possible

These innovations were engineered based on what our customers tell us they need: to act quickly, simplify complexity, and unlock better, data-driven decisions across the lines of business and processes that matter. But most of all, our customers need AI that drives significant, measurable business outcomes.

To help every organization accelerate returns on investment from AI, at 麻豆原创 Connect we announced powerful new capabilities for , which makes business data and the entire 麻豆原创 Business Suite immediately accessible through the power of a simple conversation. And because Joule is grounded in your company鈥檚 data, it understands context and delivers insights that are specific, actionable, and relevant to your business.

Deep research in Joule, announced at 麻豆原创 Connect, is a new capability expands what people can do within the Joule interface. It goes beyond quick answers to deliver strategic analysis, reporting, and synthesis in a single, connected experience. It brings together internal 麻豆原创 data and external intelligence so people can ask complex questions and receive comprehensive research and recommendations鈥攚ithout ever leaving Joule. The deep research capability in Joule will be available in beta this December.

We also unveiled the new concept of role-based AI assistants in Joule, built to partner with people in their specific roles. These assistants connect to the right Joule Agents for the job, removing guesswork so teams can unlock new levels of productivity and insight. Whether it鈥檚 a finance leader forecasting working capital, a recruiter evaluating headcount needs, or a planner adjusting inventory, AI assistants surface the right intelligence, at the right time, in the right context. In the background, Joule Agents get work done for you.

These innovations mark the next chapter in how people interact with enterprise systems, helping every role across an organization seamlessly move from inquiry to insight to action.

New Joule Agents autonomously get work done

To help enterprises further accelerate business results, we introduced 14 new Joule Agents at 麻豆原创 Connect. They help people coordinate, decide, and execute tasks with greater precision. Joule Agents are in finance, HR, procurement, and supply chain, in a way that only 麻豆原创鈥攚ith our deep expertise in these functions鈥攃an deliver.

For example, rather than navigating multiple systems or running countless manual checks to release an order, a production manager鈥攁 key role in the supply chain function鈥攚ill be able to turn to our Production Planning and Operations Agent, planned for general availability in the Q1 2026. They can simply ask Joule to do it, safely and in real time. The agent will validate and release orders when conditions are met, accelerating production start times and shortening order-to-delivery cycles.

And starting December 2025, with the general availability of , now in beta release, customers will be able to create and deploy custom Joule skills and Joule Agents tailored to their unique business needs. Agent builder in Joule Studio is your command center for designing, building, and deploying enterprise-ready custom Joule Agents using the same powerful 麻豆原创 technologies鈥攊ncluding 麻豆原创 Knowledge Graph for deep business context, 麻豆原创 Business Data Cloud for comprehensive data access across 麻豆原创 and non-麻豆原创 sources, and 麻豆原创’s central identity and authorization services to ensure responsible agent behavior. These capabilities empower every enterprise to extend, customize, and personalize 麻豆原创 Business AI solutions.

New embedded intelligence across 麻豆原创 applications

To help our customers move faster and make better decisions where work happens, we continue to bring intelligence directly into the applications they rely on every day. These embedded capabilities extend the same AI-driven guidance that powers Joule Agents into core 麻豆原创 solutions, enhancing user workflows with context, clarity, and automation. We will have more than 400 of these AI use cases by the end of the year.

For example, in , starting in February 2026, AI will orchestrate personalized interactions across HR, marketing, and service, bringing harmonized data, relevant context, and better business outcomes into every engagement. In the solution, planned for general release in the first half of 2026, AI will predict shortfalls and fulfillment risks, helping planners simulate and respond with speed and precision.

Additionally, the rebuilt 麻豆原创 Ariba source-to-pay suite, arriving in February 2026, brings a modern, cloud-native experience to every stage of procurement. Embedded AI guides people with intelligent recommendations, accelerates contract reviews, and surfaces supplier insights in real time. By simplifying sourcing decisions and improving compliance, it helps procurement teams strengthen supplier relationships and capture more value, faster.

Governing AI with visibility and control

As our customers continue to adopt these AI innovations embedded across their functions, we know they need transparency into how it operates, what it influences, and the value it creates. That is why we provide tools to give them the visibility they need to deploy AI with confidence and scale it responsibly.

麻豆原创 LeanIX AI Agent Hub helps CIOs and business leaders see their entire AI agents landscape at a glance. From one dashboard, they can understand where agents are deployed, what processes they touch, and how agents are performing. This allows teams to evaluate effectiveness, identify redundancies, and manage AI like any other enterprise asset: aligned to outcomes, governed by policy, and continuously optimized.

Complementing that, agent mining in gives organizations a powerful way to analyze how AI contributes to process performance. It reveals how AI agents decide and act, flags bottlenecks and non-compliance, and uncovers where automation adds value and how to fine-tune it for efficiency and impact. Together, these tools bring clarity to what has often been a black box: transforming governance from reactive oversight into proactive optimization.

What鈥檚 next for business AI

At 麻豆原创 Connect, we also shared a view into what鈥檚 coming and how our innovations will continue to redefine enterprise productivity. For example, we鈥檙e developing an outcome-driven user interface that adapts to context in real time. Instead of navigating menus or searching for data, people will simply express what they want to achieve, and the system will guide them through the right actions, insights, and tools.

We鈥檙e also extending Joule Agents beyond software into the physical world鈥攃onnecting your company’s business intelligence to robotics and industrial systems. This opens a new frontier for automation, combining state of the art electronics that utilize physical AI tools, such as computer vision and collision detection, with AI agents capable of reasoning through complex goals and planning multi-step workflows. this November to learn more.

Ready to start your AI journey?

We know AI is top of mind for most business leaders today. In fact, another finding from the research we commissioned with Oxford Economics is that 41 percent of tasks in global businesses will be supported by AI within two years鈥攗p from 25 percent today.

As you continue to explore what is possible with business AI, we鈥檒l keep innovating to deliver systems that are intelligent by design, trusted in operation, and, most important, measurable in their impact.

For us, it鈥檚 all about what you hope to achieve. We鈥檙e proud to partner with you on your AI journey. If you鈥檙e ready to take the next step, here are three things you can do right now:

• Learn how to bring the potential of AI into your organization by signing up for our .
• In our , get inspired about what鈥檚 possible by exploring how leading companies are transforming with AI.
• Watch 麻豆原创 Connect virtual sessions, , to see our latest innovations in action.

There鈥檚 more to come. Join us at on November 4 for the next wave of AI innovations that will transform the enterprise.

Brenda Bown is chief marketing officer for 麻豆原创 Business AI.

On top of that, you know AI agents are the new frontier and will allow you to automate processes that today absorb a large amount of time and resources. You want to know how to be among the first to use them to gain an early advantage.

To get these answers and discover how 麻豆原创 is uniquely positioned to help you leverage AI for business results, attend in 2025, being held October 6-8 in Las Vegas as well as virtually. It鈥檚 the destination for leaders ready to embed the latest from AI into the foundation of how their organizations run to produce real, enterprise-wide outcomes.

Learn how to become a leader in the era of AI

According to Boston Consulting Group, which the relatively small number of companies already scaling these advanced technologies, “AI鈥檚 greatest value lies in core business processes where leaders are generating 62% of the value. Leveraging AI in both core business and support functions gives these companies a competitive advantage.”

This is exactly our approach and position of strength at 麻豆原创. We infuse AI directly into the business processes, decisions, and data models that power finance, supply chain, HR, customer service, and more. That’s also how are enabling systems that anticipate needs, reason, act and learn and adapt in real time. And because these tools are built on (麻豆原创 BTP), they operate with the scale, security, and interoperability that enterprises demand.

麻豆原创 Connect is where this vision comes to life. Over three days, the program will move from strategic framing to hands-on guidance, tailored to every role in an organization, and with each day anchored on specific business needs.

An agenda focused on moving your business forward

Day one at 麻豆原创 Connect addresses strategy: how to turn geopolitical and market volatility into a catalyst for growth, unify your organization through shared data and synchronized purpose, and elevate your workforce with AI. Our will show you how to navigate uncertainty by leveraging enterprise-wide data capability, Joule Agents, and 麻豆原创 Business Suite to turn insight into action鈥攃reating a sustained advantage for your enterprise.

Day two focuses on how to use our latest innovations across 麻豆原创 Business Suite applications to bring AI-driven results to life within your enterprise, plus practical road maps across your lines of business.

Day three is about execution: how to leverage 麻豆原创 BTP and a unified data core to activate agile technologies, build interconnected ecosystems, and accelerate returns on innovation. Our will show you how to use a unified foundation to smoothly extend, integrate, automate, and innovate while leveraging AI and data to transform your company.

The core theme across all three days is how to build, adopt, and scale AI that spurs impactful business outcomes. For example, you will learn from companies using embedded AI to improve forecasting accuracy, unlock working capital, boost workforce productivity, and reduce operational risk.  These enterprise use cases are already delivering measurable value.

Three days of learning and leadership

For leaders looking to advance their knowledge on AI, the agenda at 麻豆原创 Connect is built for depth and relevance, and AI content and insights will be available across all tracks: spend management, supply chain, customer experience, and human capital management.

For example, in the session, you will learn how AI agents are transforming enterprise agility by autonomously executing tasks, adapting in real time, and driving productivity.

麻豆原创 Connect is a working session for those ready to lead the next phase of intelligent business. For leaders building AI strategies that are real, resilient, and ready to scale, this is the place to advance them.

, set up your , and take your seat at the center of the business AI movement.

Brenda Bown is chief marketing officer for 麻豆原创 Business AI at 麻豆原创.

I addressed this challenge in a conversation with Dan Newman at , hosted by and . We spoke about the blockers that leaders face when determining where to apply generative AI to move their businesses forward and what 麻豆原创 Business AI is uniquely bringing to market to help.

Flowing from that conversation, here are four steps you can take, among others we touched on, that will help you become an AI front-runner.

1. Prioritize use cases with the most promise

First, focus on areas of your business in which you can use AI to deliver fast, measurable value. Finance, HR, supply chain, and customer experience are among those AI front-runners often start with. As you assess your options, set aside the idea of a “proof of concept.” Instead, develop “proofs of value鈥� by using your and your team鈥檚 expertise, data, and imaginations to find areas where more value can be unlocked using automation or AI agents.聽

By the way, the term 鈥減roof of value鈥� was first coined by AI front-runner , vice president of IT at , in reference to an AI agent for accounts payable that his team designed in partnership with 麻豆原创. The key is to pinpoint what outcomes matter most to your business and choose use cases that quickly prove the value.

2. Deploy intelligent agents to simplify complex tasks

Another practice of AI front-runners is the use of AI agents that span departments and systems to solve end-to-end problems. Their autonomous abilities to handle whole processes is one of the differences between an AI skill and an AI agent. A skill is a single ability, such as the ability to write a message or analyze a spreadsheet and trigger actions from that analysis. An agent independently handles complex, multi-step processes to produce a measurable outcome. We recently an expanded network of to help foster autonomous collaboration across systems and lines of business. This includes out-of-the-box agents for HR, finance, supply chain, and other functions that companies can deploy quickly to help automate critical workflows.

AI front-runners, such as , also create customized agents that can tackle specific opportunities for process improvement. Now you can build them with , which provides a low-code workspace to help design, orchestrate, and manage custom agents using pre-defined skills, models, and data connections. This can give you the power to extend and tailor your agent network to your exact needs and business context.

3. Embed AI into daily workflows

To truly become an AI front-runner, you need AI woven seamlessly into how your teams work every day. You also need to ensure it works across your broader technology ecosystem. Because of these critical business needs, we created to be your natural language AI interface, built right into your 麻豆原创 systems. And we鈥檙e adding a new Joule action bar to make it even more context-aware and better integrated with third-party tools like ServiceNow and Microsoft Copilot. It doesn鈥檛 wait for you to tell it what you need. Instead, it can proactively follow your behavior and suggest helpful next actions in context across multiple 麻豆原创 and non-麻豆原创 applications. This helps remove friction, so your team members don鈥檛 have to toggle between tools or relearn interfaces.

4. Foster an ecosystem of interoperable, leading AI tools

Another way to become an AI front-runner is to tackle fragmented tools and solutions by putting in place an open, interoperable ecosystem. After all, what good is an innovative AI tool if it runs into blockers when it encounters your other first- and third-party solutions? This is why we recently announced a tighter integration with Microsoft Copilot for productivity and partnerships with and for flexible access to leading AI models. These, and many other partnerships, help teams combine multiple AI capabilities, share trusted data across systems, and drive business outcomes faster, without the headache of manual connections.

Ready to lead? Here鈥檚 how to get started

I want to encourage you to lead, not follow, in the AI era. If you鈥檙e ready to do that, there are a few ways to get started. First, go deeper on these subjects in the full . Then and .

Brenda Bown is chief marketing officer for 麻豆原创 Enterprise AI Business.

Every day, your people spend too much time aligning data, decisions, and actions across functional silos. AI agents can help bridge these silos, so that core processes run flawlessly and the entire organization operates more efficiently.

However, capturing this opportunity is not about creating lots and lots of siloed agents across the enterprise that help reinforce more functional independent tasks. Instead, it鈥檚 about having the right agents, grounded in the correct business context and data, that can work together, supporting human collaboration and improving end-to-end processes.

At 麻豆原创, we have been investing heavily to deliver the full promise of . From the start, we鈥檝e architected 麻豆原创 Business AI with a Suite-first principle that ensures an integrated AI strategy that brings exceptionally more value with every skill, feature, or scenario added to our portfolio of applications and platform.

Joule, our generative AI copilot, provides one seamless integrated experience across the suite, providing a unified user interface across all business functions and more than 1,300 skills to perform work across the organization. You can ask any question or present any business problem, and Joule will work across every part of your business to solve it like no other solution in the market.

These investments set a strong foundation for realizing our vision for Joule agents — a vision we first shared at 麻豆原创 Sapphire in 2024. Joule agents are uniquely capable of working together and with business users in various roles to execute complex cross-functional processes with speed and reliability.

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With today鈥檚 announcement of 麻豆原创 Business Data Cloud, the foundation for Joule agents becomes even stronger, because AI agents are only as powerful as the data in which they are grounded.

麻豆原创 Business Data Cloud equips Joule agents with a single trusted data layer that breaks down data silos, unifying data across 麻豆原创 and non-麻豆原创 sources. With 麻豆原创 Business Data Cloud, Joule agents access the most complete and context-rich data sets, allowing them to reason more deeply and act with more insight to solve problems.

麻豆原创 Knowledge Graph, previously announced at 麻豆原创 TechEd in 2024, serves as the semantic bridge between Joule agents and 麻豆原创 Business Data Cloud. 麻豆原创 Knowledge Graph reveals the connections between data and processes, helping Joule agents find all the most relevant data to ground their decisions and actions.

While knowledge graphs are not a new concept, combining them with new advanced technologies makes them extremely powerful. 麻豆原创 Knowledge Graph is rapidly advancing to make 麻豆原创’s unique 50-plus years of business process expertise available to Joule agents. This process grounding further enables Joule agents to be aware of the context in which they operate and, therefore, to solve more challenging problems that involve multi-step processes spanning supply chain, procurement, finance, and more.

All these innovations turn our long-held AI agent vision into a reality, with more innovation to come, faster. Today, we announced the availability of a collection of ready-to-use Joule agents across finance, service, and sales, with more across the 麻豆原创 Business Suite portfolio in 2025.

The announcement includes the planned first quarter availability of a cash collection Joule agent previewed at 麻豆原创 TechEd in 2024. The cash collection agent will analyze disputes and work across finance, customer service, and operations to validate details and recommend resolutions. This Joule agent exemplifies the full promise of agentic AI 鈥� delivering new levels of operational efficiency by working cross-functionally to complete a complex, multi-step process that usually takes hours in just a few seconds.

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罢辞诲补测鈥檚 announcement also includes new ready-to-use Joule agents that advance efficiency across multi-step sales and service tasks. This includes a Q&A agent that continuously monitors opportunities and customer cases, proactively spotting questions and surfacing relevant answers from approved knowledge sources; a knowledge creation agent that automatically identifies novel case resolutions and creates structured knowledge articles that scale expertise across your organization; and a case classification agent that understands case context — for example, recognizing a tax-related inquiry even if the word “tax” isn’t mentioned — and correctly routes the case to the correct team.

This class of functionally focused Joule agents will become part of Joule鈥檚 collaborative agent architecture, making them available to team with other Joule agents to solve problems across cross-functional processes. For example, when the case classification agent identifies a customer billing dispute, it can route it to the cash collection agent, autonomously kicking off the dispute resolution multi-agent workflow. Through such , a dispute can not only be resolved in seconds but also within seconds of its receipt, further increasing process efficiency and delighting customers with unmatched response time.

In addition, 麻豆原创 also previewed a custom agent builder capability for Joule studio in 麻豆原创 Build. The new agent builder will make it simple for users such as citizen developers to create custom agents for their company鈥檚 unique business needs. A guided no-code workflow, informed by 麻豆原创鈥檚 business process expertise, helps ground custom AI agents in business processes and data, allowing them to solve problems through autonomous actions across a customer鈥檚 麻豆原创 and non-麻豆原创 applications. With the unique foundation provided by Joule, Joule agents, 麻豆原创 Business Data Cloud, and 麻豆原创 Knowledge Graph, the agent builder enables organizations to build powerful custom AI agents.

With Joule agents, Joule is not just an AI copilot, but becomes an AI orchestrator across all your organization. Joule can now adaptively assemble and orchestrate teams of agents — including out-of-the-box as well as customer鈥檚 custom-built AI agents — from multiple business functions to perform complex end-to-end processes. With Joule agents, teams work more seamlessly, work moves faster, and businesses operate more efficiently.

To learn more, visit the .

Brenda Bown is chief marketing officer of 麻豆原创 Business AI at 麻豆原创.

First introduced at 麻豆原创 Sapphire in 2024, this vision takes a significant leap forward as our companies expect to announce a limited preview of the integration by the end of the year.

This partnership bridges 麻豆原创 and Microsoft 365 business applications to deliver a unified experience that maximizes productivity. With Joule and Microsoft 365 Copilot integrated, users will soon experience a world where they can perform tasks and surface insights from both systems through either , eliminating the need to switch between applications. Imagine a future where the most-used 麻豆原创 tasks can be accomplished directly from Microsoft 365 鈥� and vice versa. This is the vision 麻豆原创 and Microsoft are advancing.

How the Integration Works

The end goal of this integration is a seamless user experience where employees can access either or to complete tasks and retrieve data across both 麻豆原创 and Microsoft environments. This means that users in Microsoft 365 can rely on Copilot to pull 麻豆原创 tasks and data through Joule, while those in 麻豆原创 applications can leverage Joule to access Microsoft 365 information and workflows. This two-way integration allows employees to work in the environment most effective for the task at hand, helping to ensure they have everything they need to get their work done efficiently.

Previewing the Potential at Ignite

At Microsoft Ignite, attendees are experiencing demo previews that showcase the early stages of this integration. For example, within Microsoft Teams, users can harness the combined power of Copilot and Joule to review tasks across both 麻豆原创 and Microsoft 365, engage in chats, exchange emails, check the delivery status on sales orders recorded in 麻豆原创 S/4HANA, submit employee promotion requests to 麻豆原创 SuccessFactors solutions, and more. These demonstrations provide a glimpse of how Joule and Microsoft 365 Copilot together can simplify complex workflows and empower users to manage tasks seamlessly across systems.

Expanding Possibilities with Joule鈥檚 Reach

With Joule鈥檚 skills rapidly expanding to support 麻豆原创鈥檚 most-used tasks, this integration can unlock new productivity potential across both 麻豆原创 and Microsoft environments. Imagine HR, finance, and supply chain professionals seamlessly accessing Microsoft 365 tools within their 麻豆原创 workflows, while Microsoft users gain access to 麻豆原创鈥檚 powerful data and task automation. From onboarding employees in 麻豆原创 SuccessFactors solutions to checking delivery statuses in 麻豆原创 S/4HANA or scheduling meetings in Outlook, users can accomplish critical tasks directly within their chosen environment.

Together, Joule and Microsoft 365 Copilot set a new benchmark for cross-functional productivity, making it easier than ever to drive real-world business impact.

Looking Ahead: Limited Preview

At Microsoft Ignite, we鈥檙e setting the stage for this integration鈥檚 next phase. With the expectation to announce a limited preview before the end of 2024, we are eager to showcase how Joule and Microsoft 365 Copilot will come together to empower users and redefine productivity across 麻豆原创 and Microsoft 365 applications. Those interested in experiencing this groundbreaking integration firsthand can register their interest for the limited preview at . Spots are limited; don鈥檛 miss this opportunity to explore how 麻豆原创 and Microsoft are reshaping the workplace.

Brenda Bown is chief marketing officer for 麻豆原创 Business AI at 麻豆原创.