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Tools like Buzzsumo and Text Optimizer can give you a good insight into questions people tend to ask around your topic:

Note that Search Console labels featured snippet positions as #1 (SEO used to call them position 0). So when you see #1 in Google Search Console, there’s nothing to do here. Focus on #2 and lower.

MyBlogU (disclaimer: I am the founder) is a great way to do that. Just post a new project in the " Brainstorm" section and ask members to contribute their thoughts.

1. Aim at answering each question concisely My own observation of answer boxes has led me to think that Google prefers to feature an answer which was given within one paragraph. An older study by AJ Ghergich cites that the average length of a paragraph snippet is 45 words (the maximum is 97 words), so let it be your guideline as to how long each answer should be in order to get featured. This doesn't mean your articles need to be one paragraph long. On the contrary, these days Google seems to give preference to long-form content (also known as " cornerstone content," which is obviously a better way to describe it because it's not just about length) that's broken into logical subsections and features attention-grabbing images.  Even if you don’t believe that cornerstone content receives any special treatment in SERPs, focusing on long articles will help you to cover more related questions within one piece (more on that below). All you need to do is to adjust your blogging style just a bit: Ask the question in your article (that may be a subheading)Immediately follow the question with a one-paragraph answerElaborate further in the article

2. Be factual and organize well Google loves numbers, steps and lists. We've seen this again and again: More often than not, answer boxes will list the actual ingredients, number of steps, time to cook, year and city of birth, etc. Use Google’s guide on writing meta descriptions to get a good idea what kind of summaries and answers they are looking to generate snippets (including featured snippets). Google loves well-structured, factual, and number-driven content. There's no specific markup to structure your content. Google seems to pick up <table>, <ol>, and <ul> well and doesn't need any other pointers. Using H2 and H3 subheadings will make your content easier to understand for both Google and your readers. 3. Make sure one article answers many related questions Google is very good at determining synonymic and closely related questions, so should be you. There's no point in creating a separate page answering each specific question. Creating one solid article addressing many related questions is a much smarter strategy if you aim at getting featured in answer boxes. This leads us to the next tactic: 4. Organize your questions properly To combine many closely related questions in one article, you need to organize your queries properly. This will also help you structure your content well. I have a multi-level keyword organization strategy that can be applied here as well: A generic keyword makes a section or a category of the blogA more specific search query becomes the title of the articleEven more specific queries determine the subheadings of the article and thus define its structureThere will be multiple queries that are so closely related that they will all go under a single subheading For example: Serpstat helps me a lot when it comes to both discovering an article idea and then breaking it into subtopics. Check out its " Questions" section. It will provide hundreds of questions containing your core term and then generate a tag cloud of other popular terms that come up in those questions:

5. Make sure to use eye-grabbing images

How about structured markup? Many people would suggest using Schema.org (simply because it's been a "thing" to recommend adding schema for anything and everything) but the aforementioned Ahrefs study shows that there's no correlation between featured results and structured markup.

Further, generative AI is shown to be more useful as a co-pilot for senior employees that can sift through AI “hallucinations” — inaccurate information presented as fact — and take the output as an aid. Inexperienced employees, however, may not be discerning enough and need a path to develop this knowledge. The sentiment was echoed by Ted English, former CEO of TJX Companies and current executive chairman of Bob’s Discount Furniture, who told us leadership requires “a lot of instinct, experience, and knowledge. Some of it you can’t get from a machine. Technology reinforces and allows you to make a more confident decision.”

The first layer of the framework is intentionality. In this context, we mean that a company’s business model should be purposefully designed around AI capability, rather just applying AI to existing processes. Spencer Fung, president and CEO of Li & Fung, a global supply-chain and logistics company, gave us an analogy: “Companies acquiring AI without a new business model is like a company digitizing a horse and carriage — while the competition has created a digital automobile.”

Next comes integration across all functions of the enterprise, with horizontal communication and AI as the enabling layer — in other words, getting rid of silos.

The real challenge, however, is implementation. Garry Kasparov, former world chess champion, has written that winning performance does not come from combining the best technology with the best people — but from the best process of combining. To achieve this, talent must be familiar with AI capabilities and know how best to utilize them.

However, AI is an evolving technology, and that necessitates a business add slack to the system to allow opportunity for learning.

Competitive advantage cannot be achieved without humans in the loop. Rushing to replace talent with AI is a huge mistake. Why? First, AI is copyable. What is not copyable is a unique business model, processes, and thoughtful integration of humans.

Second, AI is based on historical data that may not hold true in a volatile global business environment.

Third, AI is subject to hallucination and “drift,” where output is either fabricated by the AI or simply inaccurate.