Why nobody should put AI in the corner

The hot potato problem 

Hannah Maude spent 20 years on the inside of retail at Asda, Sainsbury’s, and Unilever, before moving to the other side as an AI and business transformation consultant. In that time, she has seen the same pattern play out across large organisations: AI gets handed to the CTO, a consultancy runs a pilot, and leadership waits for a report. 

Her point is simple: AI should not sit in isolation, passed around like a hot potato between the CTO, a consultancy, and whoever is fielding board questions that week. It should serve the business strategy, the bottom line, and it should be solving the biggest problems the organisation is already sitting with. It should also be treated as something that will touch the whole business across the board, not just isolated pockets of it.  

Why AI pilots fail 

“Your AI strategy serves your business strategy,” she said, “It is not something different”.  The question she asks before any AI project begins is whether it’s tied to a real strategic priority, such as margin pressure, competitive threat, customer experience, etc. If it doesn’t, she is confident there are ten other use cases that would. “Press release versus reality is a real problem when it comes to AI pilots,” she said.  

According to MIT’s NANDA initiative, 95% of generative AI pilots fail to deliver measurable P&L impact. Hannah’s view is that the word “pilot” is itself part of the problem. A pilot, she argued, signals that the conversation has already gone wrong. It usually means someone has been asked to find a few impressive examples rather than to audit where the business is actually losing time or money. The technology often works in controlled conditions, it just never translates to real business returns. 

She is also frank about the cost of generic AI training programmes: roll out something broad and theoretical to an entire business, and it rarely changes how anyone works on Monday morning. 

Treat it like the pandemic 

During the pandemic, Hannah oversaw a rapid digital transformation at Unilever in Australia. Online grocery penetration spiked overnight, and there was not enough time to study the problem. A small team had to upskill the whole commercial and marketing function and embed themselves across every department.  

The pandemic, she argued, is the right frame of reference, not necessarily with the same urgency, but with the same understanding that every aspect of the business and the surrounding environment, as well as expectations from consumers will change. 

“You can’t put transformation in the corner,” she said. “A digital team or a data team cannot sit in a corner. They have to be in every team meeting.” Her team became known internally as “Jack in the box” for constantly appearing in other people’s meetings, making the case for what digital could do. 

She sees AI being handled in exactly the way digital was at first: siloed, outsourced, owned by no one with real accountability. The difference is that there is no burning platform forcing a reaction. The pandemic gave companies no choice but to move. AI is coming more slowly, and that slower pace is, paradoxically, making it harder to treat with the same urgency. 

She pointed to L’Oréal as a blueprint. The company has spent over a decade harmonising its data and building a company-wide skills foundation, alongside a compelling internal vision: becoming a beauty tech business. “We’re not teaching you AI so that we can take your jobs. You’re part of this new vision,” is the message L’Oréal gave its workforce.  

Trapped talent 

One of the strongest themes in the conversation was what Hannah called trapped talent. Before any AI tool enters the picture, she argued, the first question should be: what are the most capable people in this building actually spending their time on? 

According to Asana’s Anatomy of Work Index, which surveyed over 10,000 knowledge workers globally, 60% of working time is spent on what Asana calls ‘work about work’ (chasing updates, searching for information, and attending unnecessary meetings) rather than the skilled work people were hired to do. In her view, AI’s first job in most organisations is not to automate anything external-facing, but to free skilled people from the tasks that are wasting time. 

What shouldn’t be automated 

She was equally clear about where that human superiority applies most. Caring for one another, teaching, genuine human relationships, are not things AI should be doing, however capable the technology becomes. “Let’s use AI behind the scenes to enable human relationships,” she said, “not be the bit that’s actually doing the caring.” 

When the conversation turned to limits, Hannah was direct. “There is only one way to use AI, and that’s safely and securely.” She described the test she applies to anything going into a model: would she be comfortable if it ended up on a billboard in Piccadilly Circus? If not, it shouldn’t be there. 

On the question of what AI should never replace, she didn’t hesitate. Caring for one another. Teaching. Human relationships. “Let’s use AI behind the scenes to enable human relationships,” she said, “not be the bit that’s actually doing the caring.” 

She also pushed back on the idea that established businesses are losing a race to AI-native competitors. Heritage, customer trust, and decades of operational knowledge are not things that can be replicated quickly. “I am not going to let a tech company that has sprung up from nowhere take that experience away,” she said. “Instead, I’m going to use AI to drive change in the industry I know inside out.” 

About the Podcast 

Screw It is a podcast from Ingka Group, the largest IKEA retailer, exploring the “art of assembly” in business, sustainability, and life at home. The series invites global experts and leaders to discuss how we piece together better homes and societies, even when life looks nothing like the manual. From a company that wants people to sit comfortably, but recognises that progress is often uncomfortable, Screw It ditches the corporate script to embrace the “wonderful mess” of building a better future. 

About Ingka Group 

With IKEA retail operations in 31 markets, Ingka Group is the largest IKEA retailer and represents 87% of IKEA retail sales. It is a strategic partner to develop and innovate the IKEA business and help define common IKEA strategies. Ingka Group owns and operates IKEA sales channels under franchise agreements with Inter IKEA Systems B.V. It has three business areas: IKEA Retail, Ingka Investments and Ingka Centres. Read more on Ingka.com. 

Screw It – Episode 9 – Full Conversation Transcript 

Dani: Hi Hnnah, what a pleasure having you here. 

Hannah: It’s lovely to see you. Thank you so much for having me. 

Dani: Let’s dive in. I want to hear a little bit about your background — what led you to work in retail, how long you’ve been doing it, and what you’re currently up to. 

Hannah: Can you believe it’s been 20 years? My first job was Asda — a large UK supermarket. I walked in on my first day into a marketing role and felt this energy and excitement that got me hooked straight away. There was something about that place. It was so vibrant. And ever since then I’ve found that in every job. It’s a very democracy of ideas, and I’ve always enjoyed that. 

Hannah: I then went to a health and beauty retailer in the UK — five years there in various marketing roles — and then Sainsbury’s for another five. Really different roles: working on own brands like Taste the Difference, project management. What I found was that as well as the energy, retail is a complex business. To achieve something can be quite challenging, but it’s a real challenge to get stuck into. 

Hannah: I then moved to Australia — not a gap year, I was in my 30s — and crossed over to the supplier side. For the last eight years I’ve been in various supplier organisations, on the FMCG side. I wanted to own the P&L of a brand, and then I moved into sales at Unilever. When you’re in sales, you own that number in a way that’s unlike anything else. And being on the other side — working with retailers rather than being inside one — same energy, same hunger for growth and hunger to serve the customer. 

Hannah:  What brought me to where I am today is the pandemic. Six years ago, at Unilever, overnight our sales shifted. In Australia, grocery penetration online was quite low until then. Suddenly there was a flood of traffic onto Coles and Woolworths online, and we had to respond fast. I moved into an e-commerce role focused on making that relationship with our retailers as successful as possible. Two years of rapid transformation — really focused on driving growth and driving a culture that was genuinely excited by becoming a digital company. 

Hannah: After that I took a career break, which led to a passion project — interviewing women doing remarkable things over 40. But then ChatGPT came out, and I spent the next few months learning about AI and the future of work. Since then, my mission has been to get people and businesses AI-ready. 

Dani: There’s so much to unpack there. I want to start with consumer-facing brands. At IKEA we’ve known the customer for 80 years, but that evolves — especially online and digitally. What has working in consumer-facing businesses taught you about the customer journey? 

Hannah: The customer is king. At Sainsbury’s, everything was about how the customer perceived you — and I think IKEA is probably the same. People have strong opinions about their supermarket. It’s part of their identity. During my retail journey we were always encouraged to work in store, to get out of the ivory tower and into the reality. We could run an amazing marketing campaign, but if you couldn’t find your favourite coffee on the shelf, you’d lost the basket. If the queue was too long, you’d lost that customer. 

Hannah: In a digital world it looks very different — traffic, conversion rates, clicks — but it’s the same person. When you’ve worked in a customer-facing business, you’re always asking: what is that customer actually saying about this experience? In a digital world as well, there’s a human who could take their pound coin somewhere else if you don’t treat them well. 

Dani: Moving around so many different parts of the business — marketing, sales, supply chain, operations — do you think that gives you a different understanding of the customer than if you’d stayed in one role? 

Hannah: Lots of my friends went all the way up in one track and I kept finding myself going sideways instead. It gave me this holistic experience — almost like a graduate scheme, but as a grown-up. You accumulate so much knowledge and empathy for the people around you. I’m a little embarrassed now by how I approached marketing in my early days, because I wasn’t as accountable for the sales number as a buyer was. When I moved into sales and had to go into supermarkets and deliver difficult news — we can’t meet your supply demand, there’s been an issue on the supply chain — I had incredible empathy for everyone in every seat. 

Hannah: Moving around helps you work far better with other people. You can always advise someone to put themselves in someone else’s shoes. It’s much easier having actually been in those shoes. And for anyone sitting in sales or account management right now — after inflation, supply chain challenges, geopolitical pressures — I take my hat off to you. It has been a very difficult few years. 

Dani: That connects to something important. At IKEA we have a programme where everyone who starts has to spend time in store, because it keeps you grounded in what people are actually buying and how. When you work with companies on AI adoption, do you work cross-functionally from the start? 

Hannah: Cross-functional is imperative. If I’m brought in by a general manager or CEO, my first AI kickoff is always a cross-functional session. Who’s in that room? Scientists, supply chain people, business development, marketing. And one of the biggest things that comes out of it — before we even look at AI — is that there’s information sharing that should already be happening that isn’t. I’ve been amazed at the rich insights that emerge just from getting those people in the same room. 

Hannah: I feel really proud when I leave those sessions because avenues of conversation open up that simply wouldn’t have otherwise. I believe the future of work is much more collaborative, much more agile — and we can get to better answers faster together. 

Dani: You mentioned the pandemic and transformation. Do you think the pandemic prepared us for the wave of AI change we’re now in? 

Hannah: You would think it would, and that’s what I think a lot of people are missing. What I saw — and this is my personal journey — is that during the pandemic I had a small team. There was no way we could have achieved the transformation with three people sitting in a corner. The strategy I used was to prioritise by category, then upskill the main people: commercial and marketing teams, because they had the budget and they were talking to retailers. Everything was going to need to shift. 

Hannah: What we achieved over those two years was an elevation of skills across the whole function. I put on my leaving post on LinkedIn: a digital team or a data team cannot sit in the corner. They have to be in every team meeting. My team got called Jack in the box, because we would constantly pop up in different meetings saying — this is what’s happening with digital, this is what you can do. We had to change the mindset. 

Hannah: What I think is so interesting is that approach isn’t necessarily being taken with AI. In my mind, it’s the same thing. But what I see — and why I think a lot of AI pilots and investment is getting completely wasted — is that it’s being treated as an AI team problem. They sit in a corner. Consultants go in and talk to the leadership team. Treat it like the pandemic. The pandemic impacted your whole business. AI will impact your whole business. 

Dani: And we had no choice during the pandemic. AI is coming in more slowly, which almost makes it harder to treat with the same urgency. 

Hannah: Exactly. And actually I’ll clarify one thing. I use IKEA all the time as a best-in-class example, because what I needed to do with a lot of companies was lift their digital acumen — but interestingly, some of the digital teams needed their commercial acumen lifted too. IKEA turned into an omnichannel business very quickly. Kudos. Businesses look genuinely different today than they did then, and that happened fast. But we’ve moved on so quickly to the next problem that we forget what an incredible adaptation that was. 

Hannah: What I’m saying is not that the whole business should be doing AI straight away — actually the opposite. What I’m talking about is that AI is being outsourced. I understand why, because it seems like it’s technology, it seems like it’s data. But it’s a business problem. Your AI strategy serves your business strategy. It is not something different. 

Hannah: The reason companies have gone around in circles — not through lack of effort — is this hot potato. The CTO gets it. Whereas actually, as a leadership team: what does this mean to the business? What does it mean to our performance, our competitors? Treat it like a pandemic conversation. Don’t put it in the corner and press release it. That is not the right business approach. 

Dani: That brings me to AI pilots, because from an MIT study only around 5% of AI pilots scale to measurable impact. Working with so many companies, why do AI projects fail? 

Hannah: The central issue is the word pilot. Why are you just focusing on a pilot? That indicates to me that someone has been asked to find a couple of examples that look impressive. A pilot might take a few months, a consultancy comes in, they present it back — and then you look at whether it’s actually solving a real business problem. Is it on the matrix? High value, low complexity? Has the cost of maintaining and updating the model been mapped out? 

Hannah: I take a very different approach. I’m embarrassed by that 95% failure rate — not for me personally, but because we can do better. Business leaders are brilliant people. But if they haven’t bought into the idea of AI embedded in the business, they’ll just say: take that little pilot, run with it, let’s see. It’s a way to manage risk. And AI has turned up very quickly, and suddenly board members are asking what you’re doing. So naturally you go: let’s run a pilot. I’m not critiquing that. But we need to learn from it. 

Hannah: I use a pyramid approach. Leaders don’t need to be tech visionaries, but they do need a foundational level of AI understanding — enough so that when they’re at the watercooler they know what they’re talking about. They can challenge whoever they’re talking to: that hallucinated, that gave me a different answer, what about the accuracy? When you’ve got an AI-powered leadership team, the conversation shifts. 

Hannah: Then it’s about building an AI-literate culture across the whole business — and training everyone else. One of my first contracts when I came back from my career break, I did an AI webinar for a whole business. Afterwards I started getting messages from people around the company saying: I’ve been playing around with AI, I’m really interested, can I get a coffee? And what I didn’t tell them at the time was that I was essentially hiring them to be part of an AI squad. Those were the change makers. People with initiative, interest, and engagement. 

Hannah: We identified areas where there were challenges in their roles. Within the next 30 days, we tried different tools and techniques, then got back together to see what worked. That has since become my AI Changemakers programme — structured experimentation, documented, reported back to leadership. Not using consumer tools in an enterprise environment. And we all get to learn from what didn’t work, because you’ve potentially steered your company away from an expensive mistake. 

Dani: Do you think companies are sometimes picking the wrong use cases? Is the siloed approach part of why? 

Hannah: Absolutely. If you talk to people in their roles, they are your use cases. When you’re looking at headcount, look at what those people are working on that they know is a waste of time. There was a study by Asana — I believe — that found around 60% of the work we do is the work of work. Nobody in a business can really disagree with that. 

Hannah: What I find immediately beneficial is working with a cross-functional group, going through how they spend their time, where they add the most value. What often happens in those AI workshops is people begin to connect the dots naturally: that tool would really help me with my data analysis. 

Hannah: I recently had a conversation with someone who said they were building a chatbot. My alarm bells went off straight away. Is that actually your biggest problem? Or has someone come in and presented a chatbot and you’ve said yes? What you really want is people to identify the problems internally and then match them to an AI solution. Otherwise, people who understand the tech are trying to think of where to apply it to a business. 

Hannah: The whole AI industry is so excited by the technology. Can we get excited about the problems it should be solving? 

Dani: I completely agree. Looking at it centrally is so important. A small team in tech might see a problem in one area and solve it, and get budget for it — but should they? There might be something so much bigger. 

Hannah: Exactly. In the grocery or FMCG sector, buyers and account managers are drowning in data. Excel spreadsheets every single day. These people are incredibly smart, full of ideas that would make their business money. But they’re buried in admin. When I work with those teams, I’m looking at reporting structures. Copilot in Excel with the right approach is a genuine game-changer — but you have to understand what it’s replacing first. At the moment you’re spending this much time every day on reports. You’re running the same report every Monday. With the right prompts and the right augmentation — I’m cautious of the word automation — you can get across that data far quicker and use your talent to go after the big hitters. 

Dani: If you were advising a leader on what to ask before approving an AI pilot, what would you tell them? 

Hannah: I would ask for an audit first. Where are the business’s biggest problems and biggest opportunities? Then: how does this pilot answer our strategic priorities? At the moment, if the pressure is on margin, how does this help with that? If a competitor is gaining on you, how does this help? If the answer is clear, great. If it doesn’t ladder up to the big problem, I bet there are ten other use cases that would. 

Hannah: The conversation also shouldn’t be three people from a project team presenting back to leadership. It should be the leadership team in a room full of ideas, having ranked them by complexity and value. What’s going to be easier but highest impact? What’s going to take longer with uncertain return? I’d focus on quick wins first — you can do meaningful things in 30 days. 

Dani: How do you take from experimentation to scaling? 

Hannah: 30-60-90 days can do a lot. Take a marketing example. In 30 days, you can look across the numbers, realise the team is pulling data from multiple sources, identify a major opportunity to build commercial acumen. You can embed a new reporting process. That starts in one pocket of the team. Translate it to the whole marketing team, to every bit of budget, every agency relationship — your return should be so much greater. That’s what scaling means. It starts with a real use case where someone has demonstrated ROI in the first 30 days. 

Hannah: No one is scaling AI successfully yet. And I actually think that might be a good thing. I don’t think the security is in place. I don’t think the AI-powered workforce is there yet. If you’re being cautious for those reasons, that’s right. Get those two things done first. But if I were drawing a blueprint for success, it would be L’Oréal. Data excellence, building skills, leadership ownership, and getting everyone on board with a compelling vision. 

Dani: Let’s talk about the human side of AI adoption. Change management has come to the forefront in companies looking to scale AI. The technology works. The people are capable. But it’s still failing. Why? 

Hannah: We’re underestimating humans in a big way. The fact that we’ve been talking about AI more than humans is crazy. What we should be asking first is: where is all this amazing talent? In our company, we have done phenomenal things. Companies adapted rapidly during the pandemic — and that happened because there are brilliant people with invaluable knowledge and capabilities. 

Hannah: Look at the talent you have. How can you unlock it? There is trapped talent in every organisation. What are they doing? They’re tied behind their laptops, buried in admin, navigating internal politics. I’ve found in mapping out marketing processes that sometimes one person — or a small group — is slowing things down, not from bad intent but because the process routes through them. That’s a people issue, not an AI issue. 

Hannah: Start by asking: what talent do we have? How do we unlock it? Where can we help these people get to what they’re really good at — building brands, solving real problems? It becomes much less of a cultural change problem when you’ve done that. 

Dani: Tell us about resistance. How does it show up? 

Hannah: During the pandemic, I moved everything to digital and expected everyone to say: yes, let’s go. Some people did not react that way. Sometimes you don’t know why. Some of it is genuine fear or uncertainty. Some of it is: I’ve set my marketing plan and my budget, I don’t want to change it, I don’t like how this is being communicated to me. 

Hannah: Practically, there are two approaches. You find the people who are genuinely excited and you work with them first. In my case during the digital transformation, we’d create new digital assets with those early adopters, and then other people in the business could see it come to life. The ice cream team had done amazing activations. Suddenly another team wanted in. That’s a very practical model — and I’d apply the same to AI. 

Hannah: The wrong message is: learn AI or you’ll be left behind. The right message is: let’s see how great we can be. Give teams a specific opportunity to contribute something meaningful. And here’s the thing — the resistors are also useful. I’ve been brought into sessions where people are very anti-AI, and I think that’s fine, and actually good. But I want them working on the AI policy. Working on what responsible use means. Working on security frameworks. Their concerns often represent exactly what your consumers might feel if you overuse AI. That could probably be quite useful. 

Dani: How do you build confidence around AI without creating fear? 

Hannah: Leave the question open. What does AI mean for us — in our careers, in our organisation, in our lives? Don’t tell people they need to become AI experts. In every team meeting, create space for: how’s everyone’s experiments going? Share what you’ve found. Sometimes it works brilliantly. Sometimes it’s not accurate. Both are important to know. That kind of open conversation — even once a month — keeps it real rather than theoretical. 

Hannah: One of the things I love about tools like Microsoft Copilot is that it works with what you already have. If you’ve got a team that’s all working on one customer, you’ve got years of meeting notes, research, prior conversations. You can ask Copilot to synthesise that and tell you: every time we’ve met with this senior leader, they’ve raised this concern. That’s likely to come up again. Prepare for it. That kind of practical, immediate application changes how people feel about the technology. 

Dani: You mentioned using AI responsibly. How do you ensure that — both internally and facing consumers? 

Hannah: There is only one way to use AI, and that’s safely and securely. I go against the grain on social media, which is absolutely full of irresponsible messaging telling everyone to upload everything about themselves to a model for better answers. Don’t do that. 

Hannah: The test I apply: would I be comfortable if this ended up on a billboard in Piccadilly Circus? If someone left it on a table in a coffee shop? If your company knew you’d put that data into a consumer AI model — would you be happy? With enterprise security, that’s a different conversation. But the thinking should always be: is this safe? Should this data go here? 

Hannah: A lot of consumer models — especially the free ones — involve a human reviewer looking at the data you’ve submitted. That feeds into the next round of training. That’s why I don’t love the advice to share all your business ideas with an AI and let it give you feedback. It might also be telling someone else. 

Dani: Is it okay to lag behind? With everything you’ve said about getting foundations right — is it okay to be cautious and move more slowly? 

Hannah: Absolutely. You’re saving yourself a legal headache. And who are you lagging behind exactly? What feels like a race between Anthropic and OpenAI is a totally different race from what an established retailer or FMCG company is running. Those tech companies haven’t been around for 80 years. They haven’t got the heritage. Don’t underestimate the trust and security that heritage gives you. 

Hannah: Act with pace internally — making sure everyone understands AI, everyone is literate, the security thinking is in place. Then focus on where the growth is, where the opportunity is, and what the risk of getting it wrong looks like. A massive data leak could cost you your customer trust overnight. I want to shop with a company that’s being cautious. I want to know my data is safe. 

Hannah: There is a tech race on. Take it with some discernment around who is racing whom and what it means to your industry. I have been in my industry for 20 years. I am not going to let a tech company that has sprung up from nowhere take that experience away. I’m going to use AI to drive change in the industry I know inside out. 

Dani: From our lens as a retailer, the goal is to reach customers the best way possible and bring our products to life — without sacrificing the human connection. Chatbots, automation — we’re automating what’s repetitive, but designer advice, genuine conversation — we want that to remain human. Is there a model out there that gets that balance right? 

Hannah: Octopus Energy. In a sector that was ripe for disruption and had a poor record on customer service, Greg Jackson went in and made customer service the USP. They use AI to help customers get the most for their money — optimising energy usage, telling you when to run the dishwasher. Bills come down. But you can always speak to a human. Their AI platform, Kraken, empowers their humans. It’s a superpower, not a replacement. 

Hannah: Stretch your ambition. Caution shouldn’t mean low ambition. Have the ambition to be the best in the market, and then use AI to see how you can lift your game. 

Dani: What should never be automated in customer experience? 

Hannah:  I don’t love the word automation. I prefer augmentation. What should never be augmented away? Caring for one another. Teaching. Bringing up the next generation. Caring for the elderly. AI should be optimising in the background — making sure the right money goes into the education system, the care system — but it shouldn’t be the part that’s actually doing the caring. Let’s use AI behind the scenes to enable human relationships, not replace them. 

Dani: Where can AI damage trust? How does a company like IKEA maintain that trust while using AI? 

Hannah: IKEA already has the right instincts here — data, ethics, trust. Share that publicly. People want to find companies that are saying: we have an AI policy, we’re moving deliberately. As soon as I see a press release racing to announce an AI capability, I lose a little trust. It looks like it’s a one-off, and if it’s a race, they’re not being considerate enough. 

Hannah: Think about the experiments you’re running. The IKEA GPT for finding a floor lamp, for example — that felt progressive but with the consumer at the centre of it. Those experiments signal that you’re moving, but you’re putting the customer’s data and experience first. AI should support them. Be proud of the considered approach. That’s what people want to hear more of. 

Dani: Last question — if you walked into an IKEA store, what would you think could be augmented with AI in a way that would genuinely help customers? 

Hannah: Storytelling. If you could have a chatbot that felt like an assistant — helping you imagine how a range would look in your home, what goes with what, live in the store. Some people don’t always want to speak to a person, and that’s fine. A QR code, a quick recommendation engine. 

Hannah: And I always overspend at IKEA when I get to the checkout. Checkout shock is real. If there was a running budget calculator on the IKEA app — something that tots up as you go around the store and keeps you within what you wanted to spend — I think that would be genuinely useful. And it would help reduce waste, which IKEA wouldn’t encourage anyway. 

Dani: I love that. We do have IKEA Kreativ, the AR view you can use, but it’d be great to see more of that in store as you’re actually shopping. 

Hannah: Exactly. You might not want to talk to someone. A diversified approach to how you reach people in that moment. The technology exists — it’s about putting it where it’s most useful. 

Dani: Thank you so much for being here. 

Hannah: Thank you. I just want to say — IKEA reskilled 8,500 customer service team members into interior designers. I wish every business was thinking like that. That is what the world needs. Change makers. People who use AI to ask: what do we actually want to achieve? Sustainability. People. A great organisation. And then use AI plus humans to get there. It’s been amazing to be here. 

Dani: Thank you, Hannah.