https://a16z.com/for-b2b-generative-ai-apps-is-less-more/

We’ve watched large language models (LLMs) become mainstream over the past few years and have studied the implementations in the context of B2B applications. Despite some enormous technological advances and the presence of LLMs in the general zeitgeist, we believe we’re still only in the first wave of generative AI applications for B2B use cases. As companies nail down use cases and seek to build moats around their products, we expect a shift in approach and objectives from the current “Wave 1”  to a more focused “Wave 2.”

Here’s what we mean: To date, generative AI applications have overwhelmingly focused on the divergence of information. That is, they create new content based on a set of instructions. In Wave 2, we believe we will see more applications of AI to converge information. That is, they will show us less content by synthesizing the information available. Aptly, we refer to Wave 2 as synthesis AI (“SynthAI”) to contrast with Wave 1. While Wave 1 has created some value at the application layer, we believe Wave 2 will bring a step function change.

Ultimately, as we explain below, the battle among B2B solutions will be less focused on dazzling AI capabilities, and more focused on how these capabilities will help companies own (or redefine) valuable enterprise workflows.

我们见证了过去几年大语言模型（LLM）成为主流，并研究了其在B2B应用场景中的实践。尽管技术取得巨大进步且LLM已成为时代思潮的一部分，但我们认为当前仍处于B2B领域生成式AI应用的第一波浪潮。随着企业锁定具体应用场景并试图构建产品护城河，我们预计行业将从当前的"第一波"转向更聚焦的"第二波"，其方法论和目标将发生根本转变。

具体而言：迄今为止，生成式AI应用主要聚焦于信息的发散性——即根据指令生成新内容。而在第二波浪潮中，我们预见AI将更多应用于信息聚合——通过综合现有信息向我们展示更精简的内容。为此，我们将第二波浪潮恰当地称为"聚合AI"（SynthAI），与第一波形成对比。虽然第一波已在应用层创造了一定价值，但我们相信第二波将带来阶梯式的质变。

最终，如下文所述，B2B解决方案的竞争焦点将不再局限于炫目的AI能力，而更关注这些能力如何帮助企业掌控（或重新定义）具有商业价值的企业工作流程。

Wave 1: Crossing the bridge from consumer to enterprise

To analyze Wave 1, it’s helpful to first draw the distinction between B2C and B2B applications. When we use generative AI as consumers, our objectives are oriented toward having fun and having something to share. In this world, quality or correctness are not high priorities: It’s fun to have an AI model generate art or music you can share in a Discord channel, before you quickly forget about it. We also have a psychological tendency to believe more = productive = good, and so we are drawn to automated creation. The rise of ChatGPT is a great example of this: we tolerate the shortcomings in quality because having something longer to share is more impressive.

When it comes to B2B applications, the objectives are different. Primarily, there is a cost-benefit assessment around time and quality. You either want to be able to generate better quality with the same amount of time, or generate the same quality but faster. This is where the initial translation from B2C to B2B has broken down. 

第一波浪潮：从消费者应用跨越到企业应用
要分析第一波浪潮，首先区分B2C和B2B应用场景至关重要。作为消费者使用生成式AI时，我们的核心诉求在于娱乐性和可分享性——在这个领域，质量或准确性并非首要考量。比如让AI模型生成可在Discord频道分享的艺术作品或音乐，即便转瞬即忘也无妨。人类心理存在"数量即生产力即价值"的倾向，这驱使我们热衷自动化创作。ChatGPT的崛起正是典型案例：我们容忍其质量缺陷，因为生成更长的可分享内容会带来更强的炫耀价值。

而在B2B应用场景中，核心诉求截然不同。企业用户会严格评估时间成本与质量收益的平衡：要么追求同等时间内产出更优质内容，要么要求同等质量下实现更快的生成速度。这也正是当前B2C模式向B2B迁移过程中出现断层的关键所在。

We use B2B applications in workplace settings, where quality matters. However, the content generated by AI today is passable largely for repetitive and low-stakes work. For example, generative AI is good for writing short copy for ads or product descriptions; we have seen many B2B applications demonstrate impressive growth in this area. But we’ve subsequently seen that generative AI is less reliable for writing opinions or arguments (even when AI-generated content is compelling or confident, it’s often inaccurate), which are more valuable when it comes to innovation and collaboration in a B2B setting. A model might be able to generate usable SEO spam, but a blog post announcing a new product for software developers, for example, would require a fair amount of human refinement to ensure it’s accurate and that the message will resonate with the target audience. 

Another increasingly common example of this is for writing outbound sales emails. Generative AI is useful for a generic, cold outbound email, but less reliable for accurate personalization. From the perspective of a good sales rep, generative AI may help write more emails in less time, but to write emails that increase response rates and ultimately lead to booked meetings (which is what a rep is evaluated on), the rep still needs to do research and use their judgment about what that prospect wants to hear.

In essence, Wave 1 has been successful for more-substantive writing in the brainstorming and drafting stages, but, ultimately, the more creativity and domain expertise are required, the more human refinement is required.

我们在工作场所使用B2B应用程序时，质量至关重要。然而，当前AI生成的内容主要在重复性、低风险任务中勉强可用。例如，生成式AI擅长撰写广告短文案或产品描述；我们已见证许多B2B应用在此领域实现显著增长。但随后发现，生成式AI在撰写观点或论证时可靠性较低（即便AI生成的内容看似有说服力或自信，往往存在事实错误），而这些在B2B场景的创新与协作中更具价值。一个模型或许能生成可用的SEO垃圾内容，但若要发布面向软件开发者的新产品博客文章，仍需要大量人工润色来确保准确性并引发目标受众共鸣。

另一个日益普遍的应用是撰写外联销售邮件。生成式AI适用于通用型冷启动邮件，但在精准个性化方面表现欠佳。从优秀销售代表的角度看，生成式AI或许能缩短邮件撰写时间，但要提升回复率并最终促成预约会议（这正是考核销售的关键指标），仍需代表亲自调研并判断潜在客户的需求。

本质上，第一波浪潮在头脑风暴和草拟阶段的实质性内容创作中取得了成功，但最终，越是需要创造力和专业知识的领域，人工润色的需求就越显著。

What’s the cost (or benefit) of disrupting the workflow?

Even in cases where generative AI is useful for longer blog posts, the prompt must be precise and prescriptive. That is, before the AI can express them in long form, the authors must already have a clear understanding of the concepts that represent the substance of the blog post. Then, to get to an acceptable end result, the author must review the output, iterate on the prompts, and potentially re-write entire sections.

An extreme example here is using ChatGPT to generate legal documents. While it’s possible to do so, the prompt requires a human who is familiar with the law to provide all the required clauses, which ChatGPT can then use to generate a draft of the longer-form document. Consider the analogy of going from term sheets to closing docs. An AI can’t perform the negotiation process between the principal parties, but once all the key terms are set, generative AI could write a preliminary draft of the longer closing docs. Still, a trained lawyer needs to review and edit the outputs to get the docs to a final state that the parties can sign. 

This is why the cost-benefit assessment breaks down in the B2B context. As knowledge workers, we are evaluating whether it’s worth our time to add an additional AI-powered step to our workflows, or if we should just do it ourselves. Today, with Wave 1 applications, the answer is frequently that we’re better off doing it ourselves. 

即便生成式AI对撰写长篇博客有所帮助，提示词也必须精确且具有指导性。也就是说，在AI能以长篇形式表达内容之前，作者必须对构成博客实质的核心概念有清晰认知。要获得可接受的最终成果，作者还需审阅输出内容、优化提示词，甚至可能需要重写整个章节。

一个极端案例是使用ChatGPT生成法律文件。虽然可行，但提示词需要由熟悉法律的人士提供所有必备条款，AI才能据此生成长篇文件的初稿。这类似于从条款清单过渡到交割文件的过程——AI无法执行交易主体间的谈判工作，但当所有关键条款确定后，生成式AI可以起草长篇交割文件的初稿。不过最终仍需专业律师审阅修改，才能形成各方可签署的定稿。

这正是成本效益评估在B2B领域失效的原因。作为知识工作者，我们实际在评估是否值得为工作流程增加AI环节，还是应该亲力亲为。就当前第一波应用浪潮而言，答案往往是我们自己动手效率更高。

Wave 2: Converging information for improved decision making

As we move into the next wave of generative AI applications, we expect to see a shift in focus from the generation of information to the synthesis of information. In knowledge work, there is huge value in decision-making. Employees are paid to make decisions based on imperfect information, and not necessarily the quantity of content generated to execute or explain these decisions. In many cases, longer is not better, it’s just longer.

Many axioms support this: lines of code written is not a good measure of engineering productivity; longer product specs do not necessarily provide more clarity on what needs to be built; and longer slide decks don’t always provide more insights. 

Barry McCardel, CEO and co-founder of Hex, believes in human-computer symbiosis and highlights how LLMs can improve the way we work: 

第二阶段：信息整合以优化决策

随着我们进入生成式人工智能应用的下一个阶段，预计关注点将从信息生成转向信息整合。在知识工作中，决策具有巨大价值。员工的价值在于基于不完善的信息做出决策，而并非取决于为执行或解释这些决策所生成的内容数量。在许多情况下，内容冗长并不等于更好，只是更耗时而已。

许多原则都印证了这一点：代码行数并非衡量工程效率的好标准；冗长的产品需求文档未必能更清晰地说明需要构建什么；过长的演示文稿也不一定能提供更多洞见。

Hex公司的首席执行官兼联合创始人巴里·麦卡德尔（Barry McCardel）倡导人机协作，并强调大语言模型如何提升我们的工作方式：

“AI is here to augment and improve humans, not replace them. When it comes to understanding the world and making decisions, you want humans in the loop. What AI can do is help us apply more of our brainwaves to valuable, creative work, so that we not only spend more hours in a day on the work that matters, but also free ourselves to do our best work.”

How can AI improve human decision-making? We believe LLMs will need to focus on synthesis and analysis — SynthAI — that improves the quality and/or speed of decision-making (remember our B2B diagram above), if not make the actual decision itself. The most obvious application here is to summarize high volumes of information that humans could never digest themselves directly. 

The real value of SynthAI in the future will be in helping humans make better decisions, faster. We are envisioning almost the opposite of the ChatGPT user interface: Instead of writing long-form responses based on a concise prompt, what if we could reverse engineer from massive amounts of data the concise prompt that summarizes it? We think there’s an opportunity to rethink the UX as one that conveys large amounts of information as efficiently as possible. For example, an AI-powered knowledge base like Mem that holds notes from every meeting in an organization could proactively suggest relevant decisions, projects, or people that someone should reference as they begin a new project, saving them hours (even days) of navigating prior institutional knowledge. 

Returning to our outbound sales email example, one potential manifestation is for AI to identify when a target account is at its highest level of intent (based on news reports, earnings calls, talent migration, etc.) and alert the relevant sales rep. The AI model would then, based on the synthesized research, suggest the one or two most important issues to mention in the email, along with the product features most relevant to that target account. Ironically, these inputs could then be fed into a Wave 1 solution, but the value comes from the synthesis phase and saving a sales rep potentially hours of research into just a single prospect.

A fundamental shift in ensuring this synthesis is sufficiently high quality will be a movement away from large-scale, generic models, to architectures that leverage multiple models, including more fine-tuned models trained on domain- and use-case-specific data sets. For example, a company building a customer-support application may primarily use a support-centric model that has access to the company’s historical support tickets, but then fall back to GPT for corner cases. To the extent that the fine-tuned models and data sets are proprietary, there’s an opportunity for these components to be moats in the delivery of speed and quality.

Implementing SynthAI

As we think through what Wave 2 might look like, we believe the use cases that will benefit most from synthesis AI will be when there is both:

• A high volume of information, such that it’s not pragmatic for a human to manually sift through all the information.
• A high signal-to-noise ratio, such that the themes or insights are obvious and consistent. In the name of accuracy, you don’t want to task an AI model with deciphering nuance. 

In the diagram below, we categorize examples of common analysis and synthesis by these dimensions to help bring this to life.

This helps us think about the types of outcomes Wave 2 applications will deliver, and how they’ll differ from Wave 1 outcomes. Below, we try to offer some examples to bring the comparisons to life, but they are by no means meant to be comprehensive.

这有助于我们思考第二波应用将带来的成果类型，以及它们与第一波成果的区别。下面，我们尝试提供一些例子来使对比更加生动，但这些例子绝非详尽无遗。

A battle to own the workflow

Naturally, there is a race between existing systems of record and workflow solutions trying to embed AI-augmented capabilities, and new solutions that are AI-native. We want to be clear what they are racing toward: the prize is not about who can build the AI synthesis capability; rather, it’s who can own the workflow. For existing solutions, vendors are racing to entrench their existing workflows by improving them with AI. For challengers, vendors will use a best-in-class AI implementation as a wedge and seek to expand from there to redefine the workflow.

On the product feedback use case, Sprig has always used AI to analyze open-text responses and voice responses, and to summarize them into themes. Sprig founder and CEO Ryan Glasgow is excited about the potential for LLMs to improve their synthesis solution: 

一场争夺工作流程主导权的战役 自然，现有的记录系统与试图嵌入AI增强功能的工作流解决方案之间，以及AI原生新方案之间，正展开一场竞赛。我们需要明确他们角逐的目标：这场竞赛的奖杯并非谁能够打造AI合成能力，而是谁能够主导工作流程。对于现有解决方案提供商而言，他们正竞相通过AI改进来巩固现有工作流程。而对于挑战者而言，厂商将以顶尖的AI实施方案为突破口，寻求由此扩展并重新定义工作流程。

在产品反馈应用场景方面，Sprig始终运用AI来分析开放式文本反馈和语音反馈，并将其归纳为主题。Sprig创始人兼首席执行官Ryan Glasgow对大型语言模型提升其合成解决方案的潜力感到振奋：

“With LLMs, we can save our customers even more time than before. With our prior models, we had a human-in-the-loop review process before customers could see the themes; now, we’re comfortable presenting the themes right away, and doing the review process afterward. Additionally, we’re now able to add a descriptor to each theme to provide more specificity, which makes the insights more actionable.

“In the future, we think there’s an opportunity to allow the user to ask follow-up questions if they want to dig further into a theme. At the end of the day, it’s about delivering the end-to-end workflow — from gathering data quickly to understanding it quickly — to help make decisions in real time.”

At the same time, we’re already seeing new startups exclusively focused on using AI to summarize user feedback, by integrating with existing platforms that are collecting the raw feedback.

On the outbound sales use case, ZoomInfo recently announced that they are integrating GPT into their platform and shared a demo video. Certain parts of the video are not far off from the Wave 2 examples we described. Similarly, we’re already seeing new startups exclusively focused on trying to automate as much of the outbound sales process as possible with an AI-first approach.

The potential for how AI may change the way we work is endless, but we are still in the early innings. Generative AI in B2B applications needs to evolve beyond creating more content, to synthesis AI that enables us to do our work better and faster. In B2B applications, it’s a constant dance around who can own the workflow, and AI-native applications will make this dance ever more interesting to watch.

与此同时，我们已注意到新兴初创企业正专注通过与收集原始反馈的现有平台集成，专门利用人工智能技术汇总用户反馈。

在对外销售应用场景中，ZoomInfo近期宣布正在将GPT集成至其平台，并发布了演示视频。视频中的某些环节与我们描述的"第二阶段"案例已颇为接近。同样地，我们发现已有新兴初创企业完全致力于采用AI优先策略，尽可能实现对外销售流程的自动化。

人工智能改变工作方式的潜力无可限量，但目前仍处于发展初期。B2B领域的生成式AI需要超越单纯的内容创作，进化为能让我们更高效完成工作的合成型AI。在B2B应用中，各厂商围绕工作流主导权的博弈从未停歇，而原生AI应用将使这场博弈变得更加精彩纷呈。

https://x.com/kshenster

交叉阅读

https://blog.csdn.net/D1237890/article/details/153672240?spm=1011.2124.3001.6209

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