“杰夫，Day 2（第二天）看起来会怎样？”
这是我刚刚从全体会议上收集到的问题。我一直提醒人们，Day 1（第一天）已经持续了几十年。我在名为Day 1的亚马逊大楼内工作，当我搬离这座建筑时，这个名字如影随形。我花了些时间思考这个话题。
（注：Day 1来自于亚马逊的CEO杰夫·贝索斯的理念，意即不管公司发展到什么程度，不管取得了多少成就，仍然要把每天当成是第一天，用心做好当下。亚马逊在西雅图的办公大楼也叫做Day 1。）
“Day 2是停滞期。接踵而来的是远离主业，然后是一蹶不振，业绩痛苦地下跌，然后是死亡。这就是为什么我们总是处于Day 1。”
可以肯定的是，这种下降将以极端缓慢的速度进行。一家卓有成就的公司可能要经历几十年的Day 2，但最终结果还是会到来。
我对这个问题很感兴趣，如何应对Day 2？什么是技术和战术？如何保持Day 1的活力，尤其是在一个大的组织里？
这样的问题不会有一个简单的答案，它涉及许多因素、多条路径和大量陷阱。我不知道完整答案，但可能知道其中几点。Day 1防御的首要因素包括：客户至上，抵制形式主义，积极适应外部趋势，以及高速决策。
以顾客为中心
有许多方法将业务集中于一点。你可以以竞争对手为中心、以产品为中心、以技术为中心、以商业模式为中心。但在我看来，到目前为止，以客户至上为中心是保持Day 1活力的最佳做法。
原因何在？
以客户为中心的方法具有很多优势，不过，它存在一大问题：即使客户口头声称他们很快乐，业务是一流的，但在心里，他们有种种不满，非常不满意。甚至连客户自己都不知道，他们总在要求更好的，而你要取悦于客户的愿望将驱使你为他们创造更大利益。亚马逊的高级会员计划并非为了迎合客户的要求，但显然是后者所渴望的，类似的例子还可以举出很多。
始终处于Day 1的心态需要你耐心尝试，接受失败，种植种子，保护树苗，并在看到客户的喜悦之际获得双倍回报。客户至上的文化最有可能为上述过程创造可能发生的条件。
抵制形式主义
随着公司越来越大，越来越复杂，管理代理的倾向应运而生。它的呈现方式各异，危险而微妙，与Day 2的特征极为相似。
常见的例子是形式主义。
良好的服务流程有利于你为客户服务，但是，如果你不加警觉，流程本身就会变成问题，这在大型企业中尤为普遍。工作流程没有为结果服务，而只是走过场。你不再关注结果，只是确保流程是正确的。这就让人倒吸一口凉气了！下面的情况并不少见：某位下级领导以“嗯，我们是在遵守流程”为理由来为一个坏的结果加以辩解。更有经验的领导者则会把它视为调查和改进流程的良机。流程本身不是问题，永远值得提出的问题是：是我们在按照流程操作？还是流程左右了我们的操作？在一个每况愈下的Day 2公司，你可能会发现答案是后者。
另一个例子：市场调研和客户调查可以流于形式。
这是特别危险的，尤其当你正在发明和设计产品的时候。“55%的测试人员对这一功能感到满意，比第一次调查中的47%满意率有所上升。”这种结论难以解读，可能会在无意中造成误导。
优秀的发明家和设计师深入了解他们的客户。他们耗费巨大精力发展这种直觉，并通过调查结论获取许多个人感受，而不仅仅只是平均数，并对此加以研究。他们与设计共存。
我不反对beta测试或调查。但你，作为产品或服务的主人，必须了解客户，富有远见，并乐于奉献。然后，beta测试和研究可帮助你找到盲点。卓越的客户体验始于心灵、直觉、好奇心、游戏、胆量和品味。这些在调查中都找不到。
积极适应外部趋势
如果你不会或不能迅速追随强大趋势，外部世界会把你推入Day 2。如果你采取对抗的姿态，你可能是在和未来对着干。拥抱趋势，你将顺势而上。
大趋势并非那么难以确定（他们为此谈论和撰写了不少内容），但是对于大型组织来说，迎合趋势却异常困难，令人匪夷所思。眼下，我们正处于一个显而易见的趋势之中：机器学习和人工智能。
在过去的几十年里，计算机被广泛用于完成自动化任务，后者往往通过清晰的规则和算法描述出来。如今，现代的机器学习技术允许我们在难以精确描述规则的领域完成同样的任务。
在亚马逊公司，多年来我们一直致力于机器学习的实际应用。这项工作是高度可见的：我们自主设计的Prime Air空中交付无人机；亚马逊Go便利店使用机器视觉消除排队结帐现象线；还有Alexa——基于云的人工智能助理。（尽管我们尽了最大的努力，我们还是难以满足客户度Echo的需求。虽然听上去令人开心，但毕竟是一个问题。我们正在努力解决。）
但我们利用机器学习所做的大部分实践还不为人知。机器学习驱动我们的算法进行需求预测、产品搜索排名、产品和交易推荐、商品配售、欺诈检测、翻译，以及更多服务。虽然效果还不太明显，机器学习的影响大多如此——悄悄地、但有意义地提升核心业务。
保持决策的高速度
Day 2公司能够做出高质量的决策，但他们的高质量决策速度非常缓慢。为了保持Day 1的能量和活力，你必须以某种方式作出高质量、高速度的决策。无论对于初创企业还是大型组织来说，这都非常具有挑战性。
亚马逊的高管团队决心保持决策的高速度。商业中的速度问题至关重要，如果是高速决策，游戏就更有趣了。我们不知道所有答案，在此只提出一些想法。
首先，决不使用“一刀切”的决策过程。
许多决定是可逆的、双向的，对于这些决策，不必在公司内部大动干戈。而对于那些决定公司生死存亡的决定，我在去年的信中曾经更详细地写到了这一点。
其次，当你获得了七成所需信息后，大多数决策都可以成型。
如果你要求信息量达到90%，在大多数情况下，你的决策可能就有点慢了。另外，无论运用上述哪种方式，你都需要善于快速识别和纠正坏的决定。如果你擅长自我修正，那么，错误的成本可能比你想象的要低廉，而决策缓慢肯定意味着代价不菲。
第三，采用“保留己见、服从大局”的方式。
这句话虽然简短，却能节省大量时间。如果你对某个特定方向有信心，即使没有达成一致意见，你也可以说：“看，我知道我们对此意见不一，但你愿意和我赌一把吗？保留己见、服从大局？”到目前为止，没有人能确切知道答案，但你可能很快得到答案。
这不是一种单向的做事方法，如果你是老板，也应该这样做，我一直在保留己见、服从大局。
我们最近开拍了一部亚马逊工作室的原创剧。我告诉团队我的观点：不管它是否足够有趣，制作过程是否复杂，业务条款好不好……一切都可以讨论，我们还有很多其他机会。他们的态度完全不同，希望继续下去。我立刻写道，“我保留意见，服从大局，并希望它成为我们制作过的最具可看性的节目。”
请想一想，如果团队不得不说服我而不是简单地得到我的承诺，这个决策周期会有多么缓慢。请注意，我举这个例子并不是要说：我没有心里想过“嗯，这些人错了，没抓中重点，但这不值得我追究。”这是一次真正的意见分歧，我坦率表明我的意见，团队对我的观点加以权衡，并快速而真诚地承诺，他们要走自己的路。考虑到这个团队已经带回11个艾美奖、6个金球奖，以及3项奥斯卡奖，他们愿意让我留在房间里，已经让我好开心了！
第四，及早识别真正的错误问题，并立即使之升级。
有时候，团队内部存在不同的目标和截然不同的观点，无法调和。缺少大量的讨论和交流，难以解决深层次矛盾。如果问题没有逐步扩大，默认的争端解决机制将导致争议双方筋疲力竭，最终，谁更有耐力，谁就获得决策权。
在过去的几年里，我在亚马逊看到许多意见不合的例子。当我们决定邀请第三方卖家直接在我们自己的产品细节页面与我们竞争时，出现了一次大规模意见分歧。许多聪明的、善意的亚马逊人完全不赞同这一决策。这个重大决定涉及到数百个较小的决策，其中一些需要升级到高级团队。
“你把我拖垮了”是一个可怕的决策过程，进展缓慢，令人疲惫不堪。使矛盾快速升级反而效果更好。
那么，你是否只解决了决策质量问题，或者你也在意决策的速度？你的决策符合世界的发展趋势吗？你是工作流程的牺牲品，还是让它们为你服务？最重要的是，你让顾客喜笑颜开吗？我们可以同时具备大公司的业务范围和能力，小公司的精神和初心，但我们必须有所选择。
十分感谢每一位客户让我们为您服务，感谢股东对我们的支持，感谢世界各地亚马逊人的辛勤工作，感谢你们的努力、智慧和激情。一如既往，我附上1997年的一份信。我们仍然处于创业Day 1。
“Jeff, what does Day 2 look like?”
That’s a question I just got at our most recent all-hands meeting. I’ve been reminding people that it’s Day 1 for a couple of decades. I work in an Amazon building named Day 1, and when I moved buildings, I took the name with me. I spend time thinking about this topic.
“Day 2 is stasis. Followed by irrelevance. Followed by excruciating, painful decline. Followed by death. And that is why it is always Day 1.”
To be sure, this kind of decline would happen in extreme slow motion. An established company might harvest Day 2 for decades, but the final result would still come.
I’m interested in the question, how do you fend off Day 2? What are the techniques and tactics? How do you keep the vitality of Day 1, even inside a large organization?
Such a question can’t have a simple answer. There will be many elements, multiple paths, and many traps. I don’t know the whole answer, but I may know bits of it. Here’s a starter pack of essentials for Day 1 defense: customer obsession, a skeptical view of proxies, the eager adoption of external trends, and high-velocity decision making.
True Customer Obsession
There are many ways to center a business. You can be competitor focused, you can be product focused, you can be technology focused, you can be business model focused, and there are more. But in my view, obsessive customer focus is by far the most protective of Day 1 vitality.
Why? There are many advantages to a customer-centric approach, but here’s the big one: customers are always beautifully, wonderfully dissatisfied, even when they report being happy and business is great. Even when they don’t yet know it, customers want something better, and your desire to delight customers will drive you to invent on their behalf. No customer ever asked Amazon to create the Prime membership program, but it sure turns out they wanted it, and I could give you many such examples.
Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight. A customer-obsessed culture best creates the conditions where all of that can happen.
Resist Proxies
As companies get larger and more complex, there’s a tendency to manage to proxies. This comes in many shapes and sizes, and it’s dangerous, subtle, and very Day 2.
A common example is process as proxy. Good process serves you so you can serve customers. But if you’re not watchful, the process can become the thing. This can happen very easily in large organizations. The process becomes the proxy for the result you want. You stop looking at outcomes and just make sure you’re doing the process right. Gulp. It’s not that rare to hear a junior leader defend a bad outcome with something like, “Well, we followed the process.” A more experienced leader will use it as an opportunity to investigate and improve the process. The process is not the thing. It’s always worth asking, do we own the process or does the process own us? In a Day 2 company, you might find it’s the second.
Another example: market research and customer surveys can become proxies for customers – something that’s especially dangerous when you’re inventing and designing products. “Fifty-five percent of beta testers report being satisfied with this feature. That is up from 47% in the first survey.” That’s hard to interpret and could unintentionally mislead.
Good inventors and designers deeply understand their customer. They spend tremendous energy developing that intuition. They study and understand many anecdotes rather than only the averages you’ll find on surveys. They live with the design.
I’m not against beta testing or surveys. But you, the product or service owner, must understand the customer, have a vision, and love the offering. Then, beta testing and research can help you find your blind spots. A remarkable customer experience starts with heart, intuition, curiosity, play, guts, taste. You won’t find any of it in a survey.
Embrace External Trends
The outside world can push you into Day 2 if you won’t or can’t embrace powerful trends quickly. If you fight them, you’re probably fighting the future. Embrace them and you have a tailwind.
These big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace. We’re in the middle of an obvious one right now: machine learning and artificial intelligence.
Over the past decades computers have broadly automated tasks that programmers could describe with clear rules and algorithms. Modern machine learning techniques now allow us to do the same for tasks where describing the precise rules is much harder.
At Amazon, we’ve been engaged in the practical application of machine learning for many years now. Some of this work is highly visible: our autonomous Prime Air delivery drones; the Amazon Go convenience store that uses machine vision to eliminate checkout lines; and Alexa,1 our cloud-based AI assistant. (We still struggle to keep Echo in stock, despite our best efforts. A high-quality problem, but a problem. We’re working on it.)
But much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type – quietly but meaningfully improving core operations.
Inside AWS, we’re excited to lower the costs and barriers to machine learning and AI so organizations of all sizes can take advantage of these advanced techniques.
Using our pre-packaged versions of popular deep learning frameworks running on P2 compute instances (optimized for this workload), customers are already developing powerful systems ranging everywhere from early disease detection to increasing crop yields. And we’ve also made Amazon’s higher level services available in a convenient form. Amazon Lex (what’s inside Alexa), Amazon Polly, and Amazon Rekognition remove the heavy lifting from natural language understanding, speech generation, and image analysis. They can be accessed with simple API calls – no machine learning expertise required. Watch this space. Much more to come.
High-Velocity Decision Making
Day 2 companies make high-quality decisions, but they make high-quality decisions slowly. To keep the energy and dynamism of Day 1, you have to somehow make high-quality, high-velocity decisions. Easy for start-ups and very challenging for large organizations. The senior team at Amazon is determined to keep our decision-making velocity high. Speed matters in business – plus a high-velocity decision making environment is more fun too. We don’t know all the answers, but here are some thoughts.
First, never use a one-size-fits-all decision-making process. Many decisions are reversible, two-way doors. Those decisions can use a light-weight process. For those, so what if you’re wrong? I wrote about this in more detail in last year’s letter.
Second, most decisions should probably be made with somewhere around 70% of the information you wish you had. If you wait for 90%, in most cases, you’re probably being slow. Plus, either way, you need to be good at quickly recognizing and correcting bad decisions. If you’re good at course correcting, being wrong may be less costly than you think, whereas being slow is going to be expensive for sure.
Third, use the phrase “disagree and commit.” This phrase will save a lot of time. If you have conviction on a particular direction even though there’s no consensus, it’s helpful to say, “Look, I know we disagree on this but will you gamble with me on it? Disagree and commit?” By the time you’re at this point, no one can know the answer for sure, and you’ll probably get a quick yes.
This isn’t one way. If you’re the boss, you should do this too. I disagree and commit all the time. We recently greenlit a particular Amazon Studios original. I told the team my view: debatable whether it would be interesting enough, complicated to produce, the business terms aren’t that good, and we have lots of other opportunities. They had a completely different opinion and wanted to go ahead. I wrote back right away with “I disagree and commit and hope it becomes the most watched thing we’ve ever made.” Consider how much slower this decision cycle would have been if the team had actually had to convince me rather than simply get my commitment.
Note what this example is not: it’s not me thinking to myself “well, these guys are wrong and missing the point, but this isn’t worth me chasing.” It’s a genuine disagreement of opinion, a candid expression of my view, a chance for the team to weigh my view, and a quick, sincere commitment to go their way. And given that this team has already brought home 11 Emmys, 6 Golden Globes, and 3 Oscars, I’m just glad they let me in the room at all!
Fourth, recognize true misalignment issues early and escalate them immediately. Sometimes teams have different objectives and fundamentally different views. They are not aligned. No amount of discussion, no number of meetings will resolve that deep misalignment. Without escalation, the default dispute resolution mechanism for this scenario is exhaustion. Whoever has more stamina carries the decision.
I’ve seen many examples of sincere misalignment at Amazon over the years. When we decided to invite third party sellers to compete directly against us on our own product detail pages – that was a big one. Many smart, well-intentioned Amazonians were simply not at all aligned with the direction. The big decision set up hundreds of smaller decisions, many of which needed to be escalated to the senior team.
“You’ve worn me down” is an awful decision-making process. It’s slow and de-energizing. Go for quick escalation instead – it’s better.
So, have you settled only for decision quality, or are you mindful of decision velocity too? Are the world’s trends tailwinds for you? Are you falling prey to proxies, or do they serve you? And most important of all, are you delighting customers? We can have the scope and capabilities of a large company and the spirit and heart of a small one. But we have to choose it.
A huge thank you to each and every customer for allowing us to serve you, to our shareowners for your support, and to Amazonians everywhere for your hard work, your ingenuity, and your passion.
As always, I attach a copy of our original 1997 letter. It remains Day 1.
Sincerely,
Jeff
Jeffrey P. Bezos
Founder and Chief Executive Officer
Amazon.com, Inc.
1 For something amusing, try asking, “Alexa, what is sixty factorial?”
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