在CUBE对Workiva的放大事件的报道中

在CUBE对Workiva的放大事件的报道中

企业AI治理正在进入决定自动化系统能做什么的数据，工作流程和控制。 由于agents在财务、审计、合规和可持续性方面开展相应工作，各组织需要基于业务背景、可追溯到其来源并受明确问责管理的成果。

CUBE对Workiva扩展事件的预览突出了人工智能从建议行动转向采取行动所需的控制。 根据CUBE Research的Krista案，值得信赖的自动化还取决于将信息与周围的商业条件联系起来。

“我所看到的是Workiva的更大机会就是创造这种背景，并成为跨越这些不同平台的连接组织，“它们历来在遵守和报告以及受到严格管制和审计的工作流程等方面非常强大。 它们开始在可持续性等领域采取一些步骤。”

在CUBE、SiliconANGLE媒体直播室独家播出期间，他们探讨了AI agents如何改变问责制、Workiva如何重新设计所管理的工作流程以及各组织如何决定哪些业务流程可以自动化。 (* 下文披露。 )

以下是CUBE与Krista Case及Alison Kosik的完整视频讨论：

以下是CUBE对Workiva放大事件的报道中你可能忽略的三点见解：

透视#1：企业AI治理必须定义agent自主的限度。

随着AI agents从协助员工转向从事规范的工作，治理必须超越基础数据的质量。 公司还需要一份可审计的记录，根据案例，在任务完成后支持问责制。

“我们需要了解以下情况：这些信息和。.. 这些见解来自哪里？谁批准采取特别行动？如果一个AI agent代表你采取行动，(原始内容存档于2018-07-22). Case 称。 “当你考虑金融和其他监管过程时，我们必须真正确保它能够得到证实。”

更难的设计问题是，在不放弃有意义的监督的情况下给予agent多大的独立性。 答案将取决于风险和业务使用情况，监测和升级有助于各组织保持控制。

她的称表示：“有趣的是，“我们需要在大赦国际协助一个人时，在一个人能够自行执行时，以及在需要批准时，作出一些决定。 我确实认为这些界限中有些仍在定义之中，我认为它们会随着时间演变和变化，特别是随着商业使用案例的发展。

以下是CUBE与Krista Case及Alison Kosik的完整视频分析：

透视2：治理和工作流程重新设计必须共同推进。

工作2026年年中执行官基准测试调查揭示了企业AI治理面临的一个中心挑战：信心和准备状态之间的差距。 84%接受调查的主管对年度报告中出现的未经人审查的AI产出至少有些自信，然而只有11%的称的数据质量足以供AI使用。 既定的财务控制可以帮助各组织通过记录四个要素，使AI协助的报告更加负责：数据来源地、谁触及数据、数据发生情况以及如何审查和核准的。 根据Workiva的副总裁和行业负责人Steve Soter的说法，随着大赦国际加快报告工作，这种问责制仍然是必要的。

“这实际上使得这个过程变得更加重要，因为加速一个过程，如果你没有基于。.....这四件事，“这确实成为一种赔偿责任。 它成为一种风险。”

有了这一基础，治理就能够支持更广泛的工作流程重新设计，而不是放慢其速度。 Agent Studio为商业用户提供了一个无码方式，为自己的流程构建和定制agents，早期团队在几天内开发了有用的agents，据Workiva首席信息官Kim Huffman(pictured)称。 该公司正在内部采用成果第一办法。 跨函数的员工定义了共享结果，然后确定agents在哪里可以完成部分工作。

“我认为AI是一种基本技术，“当你把它当成一个螺栓能力时，你可能会得到螺栓上的结果。”

以下是CUBE对Steve Soter的完整视频采访：

透视3号：AI准备状态必须经过过程评价。

在HP公司，可持续性数据支持在180多个国家销售的企业的监管反应、客户要求和产品决定。 根据HP公司高级副总裁Jen Huffstettler、首席可持续性干事和全球市场准入以及McKinsey & Company公司高级顾问Kristen Siemen的说法，这一比额表使源的一致性、保证和签售至关重要。

“如果我回想起我在通用汽车公司的时代，我们为可持续性数据和报告开展了与我们为财务所做的完全相同的进程，”Siemen 称。 “因此，在内部，我们的审查、我们的保证、进程和签署都是一致的。”

一致的标准并不意味着每个过程都同样可以实现自动化。 在Accel Entertainment Inc.中，近期采用的重点是证券交易委员会的报告和企业资源规划系统，各小组了解数据线条，可以及早让审计员参与。 Accel的首席会计官Christie Kozlik认为，在决定个人披露是否适合AI之前

“我向这个大赦国际提供了哪些投入？我期待的是什么样的产出？我试图解决的根源是什么？“了解这些投入、产出和期望变得极为重要。”

准备程度也决定了收养的速度。 在Newell Brands Inc.中，内部审计有助于区分低风险部署，例如客户订单状态agents与从事固定资产核算的agents. 根据Newell Brands副总裁兼首席审计执行官Keri Tracy的说法，随着这些部署的推进，审计工作还与商业和技术小组合作。

“我认为很多公司对改革管理概念的理解不够清楚，无法理解如果你不把人民和你们一起带到一起，“我认为许多转变失败了

以下是CUBE对Keri Tracy的完整视频采访：

为了更多观看CUBE对Workiva放大事件的报道

(* 披露：CUBE是Workiva扩展事件的付费媒体合作伙伴。) CUBE活动报道的主办方对CUBE或SiliconANGLE上的内容没有编辑控制。 )

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Three insights you may have missed from theCUBE’s coverage of Workiva’s Amplify event

Enterprise AI governance is moving into the data, workflows and controls that determine what automated systems can do. As agents take on consequential work in finance, audit, compliance and sustainability, organizations need results that are grounded in business context, traceable to their sources and governed by clear accountability.

TheCUBE’s preview of Workiva’s Amplify event highlighted the controls needed as AI shifts from recommending actions to taking them. Trustworthy automation also depends on connecting information with the business conditions surrounding it, according to theCUBE Research’s Krista Case.

“What I’m looking at as Workiva’s bigger opportunity is creating that context and being that connective tissue across those disparate platforms and bringing in that more tribal knowledge … to make sure that as we are starting to ask AI to actually execute functions, it can be trusted because it does increasingly have that context behind it,” she said. “They are historically very strong in things like compliance and reporting and very regulated and audited workflows. They are beginning to make some steps into areas like sustainability.”

Case and co-host Alison Kosik spoke with executives and practitioners at Workiva’s Amplify event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They explored how AI agents are changing accountability, how Workiva is redesigning governed workflows and how organizations are determining which business processes are ready for automation. (* Disclosure below.)

Here’s theCUBE’s complete video discussion with Krista Case and Alison Kosik:

Here are three insights you may have missed from theCUBE’s coverage of Workiva’s Amplify event:

Insight #1: Enterprise AI governance must define the limits of agent autonomy.

As AI agents move from assisting employees to carrying out regulated work, governance must extend beyond the quality of the underlying data. Companies also need an auditable record that supports accountability after a task is completed, according to Case.

“We need to understand things like: Where did this information and … these insights come from? Who approved a particular action? Can you maybe trace what happened if an AI agent is taking an action on your behalf?” Case said. “When you think about finance and these other regulated processes, it’s not really good enough for the action or the response to just look plausible. We have to really make sure that it can be substantiated.”

The harder design question is how much independence to grant an agent without surrendering meaningful oversight. The answer will depend on the risk and business use case, with monitoring and escalation helping organizations maintain control.

“What’s interesting is that going back to the conversation around speed, if a human has to review and approve every action, then really the whole point or much of the value is moot,” she said. “We need to kind of make decisions around when AI is assisting a human, when it can execute on its own [and] when it needs approval. I do think that some of these boundaries are still being defined, and I think they’re going to evolve and change over time, especially as the business use cases evolve.”

Here’s theCUBE’s complete video analysis with Krista Case and Alison Kosik:

Insight #2: Governance and workflow redesign must advance together.

Workiva’s 2026 Midyear Executive Benchmark Survey exposes a central challenge for enterprise AI governance: the gap between confidence and readiness. Eighty-four percent of surveyed executives were at least somewhat confident in AI outputs appearing in annual reports without human review, yet only 11% said their data quality was sufficient for AI use. Established financial controls can help organizations make AI-assisted reporting more accountable by documenting four elements: where data originated, who touched it, what happened to it and how it was reviewed and approved. That accountability remains necessary as AI accelerates reporting, according to Steve Soter, vice president and industry principal at Workiva.

“It actually makes it even more important because accelerating a process, if you don’t have it grounded by … those four things, then speed doesn’t become an asset,” he said. “It really becomes a liability. It becomes a risk.”

With that foundation in place, governance can support broader workflow redesign rather than slow it down. Agent Studio gives business users a no-code way to build and customize agents for their own processes, and early teams developed useful agents within days, according to Kim Huffman (pictured), chief information officer of Workiva. The company is applying an outcome-first approach internally. Employees across functions define a shared result, then determine where agents can perform parts of the work.

“I think AI is a fundamental technology that’s going to reshape the landscape of technology portfolios in every organization,” she said. “When you think of it as a bolt-on capability, you’re probably going to get bolt-on outcomes.”

Here’s theCUBE’s complete video interview with Steve Soter:

Insight #3: AI readiness must be evaluated process by process.

At HP Inc., sustainability data supports regulatory responses, customer requests and product decisions across a business that sells in more than 180 countries. That scale makes source consistency, assurance and sign-offs essential, according to Jen Huffstetler, senior vice president, chief sustainability officer and global market access at HP, and Kristen Siemen, senior advisor at McKinsey & Company Inc.

“If I think back to my time at General Motors, we ran the exact same processes for sustainability data and reporting that we did for financial,” Siemen said. “So, internally, our reviews, our assurance, the process and the sign-offs were all consistent.”

Consistent standards don’t mean every process is equally ready for automation. At Accel Entertainment Inc., near-term adoption is focused on Securities and Exchange Commission reporting and enterprise resource planning systems, where teams understand the data lineage and can involve auditors early. It’s critical to rate individual disclosures by risk before deciding whether they’re suitable for AI, according to Christie Kozlik, chief accounting officer at Accel.

“What is the input that I am feeding into this AI? What is the output that I’m expecting? What is the root cause that I’m trying to solve for?” Kozlik said. “Knowing those inputs, outputs and expectations becomes so important.”

Readiness also determines the pace of adoption. At Newell Brands Inc., internal audit helps distinguish lower-risk deployments, such as customer order-status agents, from agents working on fixed-asset accounting. Audit also works with business and technology teams as those deployments advance, according to Keri Tracy, vice president and chief audit executive of Newell Brands.

“I think a lot of companies don’t understand the concept of change management well enough to understand that if you don’t bring the people along with you, you could have the best plan, you could have the best technology, and it won’t work,” she said. “I think a lot of transformations fail because they don’t bring the people along.”

Here’s theCUBE’s complete video interview with Keri Tracy:

To watch more of theCUBE’s coverage of Workiva’s Amplify event, here’s our complete video playlist:

(* Disclosure: TheCUBE is a paid media partner for Workiva’s Amplify event. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

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