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提高代理人分析的见识质量
分析agents产生不合格见解的顶端原因是缺乏商业环境，促使投资于语义层和治理，而不是更大的模型。
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在短短几年的时间里，基因AI改变了企业分析的面貌，特别是让全组织用户都能获取分析。 Agentic AI承诺做更多的工作，方法是将整个工作流程自动化，并根据agent的洞察力采取行动。 但有一个问题：agents目前产生的洞察力质量尚有不少有待改进之处。
不到半数的agent为本报告所调查的组织提供的见解导致业务改进。 虽然大多数受访者赞扬其agents描述性见解的质量，但对其指令性见解(即作为行动基础的见解)的审查却明显有好有坏。
未能产生改善企业的见解，使各组织丧失了对代理分析投资的预期回报。 更糟糕的是，引发不正确行动的有缺陷的洞察力可能造成声誉或财政损害。 本报告衡量公司如何看待其分析师agents产出的质量，并探讨各组织如何努力提高洞察力质量，使agents为企业更努力地工作。
报告中的一些主要结论包括：
代理分析才刚刚开始平均而言，在被调查公司的agents分析中，有48%的见解导致了改善业务的行动。 这比一年前答复者所说的37%的平均水平有明显进步。 但这意味着今天分析师agents所产生的一半以上的见解仍然未能帮助企业。
更高质量的洞察力导致明显的商业优势。 AI agents在少数组织的表现比一般好得多，在这个团体中，超过70%的agent洞察力导致了改善业务的行动。 agents的见解质量比总的受访者要高。 在说明性见解方面，质量差距特别大。
环境不足降低了洞察力的质量。 当分析师agents提供不合格质量的见解时，主要原因是缺乏商业环境，55%接受调查的主管都引用了这一点。 根据45%的数据，零散或无法获取是另一个主要因素。 较少的有见地的领导人说他们的agents与上下文的接触相争。
透视超载进一步抑制了agent的有效性。 在大多数接受调查的组织(52%)中，分析agents产生太多的见解，无法适当评价，因此无法采取行动。 不到四分之一(24%)的人能够评价所有agent的见解；大多数是逐一评价，目前这一过程是人文密集型的。
施政和语义是改进见解的重点领域。 接受调查的公司正在通过强化人工智能和数据治理(60%被引用)，优先采取举措，使agent有更好的见解。 他们还在数据结构中增加了一个语义层(45%)。 我们访谈的专家一致认为，语义层对于确保高性能的agents至关重要。
这些内容是由麻省理工学院技术评论的自定义内容分支Insights(Insights)制作的，而不是其编辑人员。 它由人类研究并撰写，拥有任何AI工具，可能仅限于人类监督下的生产过程。
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监视塔日益由大赦国际提供动力，它承诺对过境点作出更迅速的反应。 我们调查了塔楼附近的死亡事件发现塔楼未能交付
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Sponsored
Raising the quality of insights in agentic analytics
The top reason analytics agents produce substandard insights is a lack of business context, prompting investment in semantic layers and governance, rather than bigger models.
In partnership withSnowflake
In just a few years, generative AI has changed the face of enterprise analytics, notably by making analytics accessible to users across the organization. Agentic AI promises to do even more, by automating entire workflows and taking actions based on agent insights. But there is a problem: The quality of insights that agents currently generate leaves much to be desired.
Fewer than half of agent-generated insights at the organizations surveyed for this report lead to business improvement. And while most respondents praise the quality of their agents’ descriptive insights, reviews of their prescriptive insights—those on which actions are based—are decidedly mixed.
Failure to produce insights that improve the business deprives organizations of their desired return on investment in agentic analytics. Worse, flawed insights that trigger incorrect actions can cause reputational or financial damage. This report gauges how companies view the quality of the outputs produced by their analytics agents and explores how organizations are seeking to boost insight quality and get their agents working harder for the business.
Some key findings from the report include the following:
Agentic analytics is only just starting to deliver. On average, 48% of insights generated by analytics agents at the surveyed companies lead to actions that improve the business. That is a marked advance from the 37% average the respondents say generated such actions one year ago. But it means that more than half of the insights generated by analytics agents today are still failing to help businesses.
Better quality of insights leads to clear business advantage. At a small cohort of organizations, which we term “insight leaders,” AI agents perform much better than the average. In this group, more than 70% of agent insights lead to actions that improve the business. The insight leaders also give the quality of their agents’ insights higher marks than do the total respondents overall. The quality gap is particularly wide when it comes to prescriptive insights.
Insufficient context reduces insight quality. When analytics agents deliver insights of substandard quality, the main reason is a lack of business context, as cited by 55% of surveyed executives. Fragmented or inaccessible data is the other major factor, according to 45%. Fewer insight leaders say their agents struggle with access to context.
Insight overload further inhibits agent effectiveness. At a majority (52%) of surveyed organizations, analytics agents generate too many insights to be properly evaluated and therefore acted upon. Less than one-quarter (24%) are able to evaluate all agent insights; most do so on a case-by-case basis, and that process is currently human-intensive.
Governance and semantics are areas of focus for improving insights. The surveyed companies are prioritizing initiatives that enable better agent insights by strengthening AI and data governance (cited by 60%). They are also adding a semantic layer to their data architecture (45%). The experts we interviewed agree that the semantic layer is crucial to ensure high-performing agents.
This content was produced by Insights, MIT Technology Review’s custom content arm, not its editorial staff. It was researched and written by humans, with any AI tools that may have been used limited to production processes under human oversight.
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