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Bianca Hill is the Head of People & Culture at Decidr, specializing in embedding AI into HR operations to reduce administrative burdens and enhance meaningful work. With extensive experience in leading People and Operations for startups, she focuses on aligning teams, simplifying organizational complexity, and building structures that support sustainable growth while preventing burnout. Learn more about her on her expert page.
Bianca Hill, Head of People and Culture at Decidr — an agentic AI operating system company based in Sydney — joins Amy Springer and Tim Brewer to unpack what's really happening at the intersection of AI and the workforce. Drawing on an unconventional career path from accounting to agency recruitment to HR, Bianca brings a systems-thinking lens to some of the most pressing questions leaders face right now. She challenges the assumption that AI ownership belongs in tech or with the CEO, makes a compelling case for HR leading AI change management, and shares her "pipe dream" of forecasting agentic offsets at board level. This episode cuts through the hype with practical insight on probabilistic vs. deterministic AI, why workflow mapping is the foundation of every successful AI rollout, and what the real future of work looks like when knowledge is no longer your superpower.
[00:00:00] Bianca Hill: People talking about how it is such a probabilistic technology and we're trying to jam it into a environment where you need a deterministic response.
If you're doing an account reconciliation, you need it to be done the same way every time. And a lot of the issues or a lot of the challenges that we've seen with AI over the last 12 months have been because that is not happening.
I have a bit of a pipe dream of being able to continuously and accurately forecast an agentic offset. So the amount of time, or the amount of a role that we can offset using agentic capabilities and then roll that up to board level.
[00:00:39] Tim Brewer: Welcome to the Org Design Podcast. This is Tim Brewer and I've also got Amy Springer joining you today as co-host of the podcast. And we are lucky enough to have Bianca Hill joining us from Decidr. Bianca, welcome to the Org Design Podcast.
[00:00:56] Bianca Hill: Thank you for having me.
[00:00:57] Tim Brewer: One of the things we often start with before we ask a little bit about your background and how you ended up doing what you're doing as the Head of People and Culture. Did you always hope or want to get into human resources? You're telling your mom as a kid, like maybe astronaut, maybe lawyer, HR person, and I know you'll tell us a little bit about your company, but pretty deep in the AI workforce space which we're excited to talk about today. Tell us how you ended up in the place you are today.
[00:01:25] Bianca Hill: Definitely not a lifelong aspiration to end up in HR. I actually had no idea what HR was when I was growing up. I started my career as an accountant in tax and audit. I did that for about a year and I'm a people person by nature and accountants don't typically, exhibit people, people first tendencies. So I left after a year and went to the most people orientated industry that I could find, which was agency recruitment, did that, hated it, and just kind of fell into, into HR, and I've been in it for the last seven or eight years.
[00:01:59] Tim Brewer: Tell us a little bit about the company that you're with at the moment. And what they're up to.
[00:02:03] Bianca Hill: So I work for a company called Decidr. We are based out of Sydney in Australia. We work in the AI space. Our core product is called Decidr OS, which is an agentic operating system. The point of difference in market is Decidr focuses on structured intelligence. So we take the data that exists in all of your systems, restructure that into a format that is very consistent and readable for AI which enables more deterministic and consistent responses. Company's been around for eight years in R&D, but in commercial operation for about two.
[00:02:36] Amy Springer: Your accounting background would make you happy with that mission, pulling in the right data, giving the right signals.
[00:02:44] Bianca Hill: It does make me happy. I definitely think in structure and framework and so working for a business that has a product that thinks in structure and framework is very helpful. And I now tend to use the ethos of Decidr in a lot of things that I approach both inside and outside of work.
[00:02:59] Tim Brewer: We've been talking a lot over the last 12 months about the agentic workforce and how the agentic workforce fits with the human workforce, which I know you look after. How are you thinking about that at Decidr for your own team? Where do you think that's heading in the future? But maybe tell us a little bit about how you're thinking about that challenge today.
[00:03:22] Bianca Hill: Yeah, absolutely. It's, it's definitely a challenge and I think we are at. A bit of a turning point in, you know, what work kind of, kind of looks like. And I think the future of work is gonna drastically evolve over the next few years. AI has meant that knowledge is a lot more accessible for everyone.
It's a lot easier to use ChatGPT or a different LLM to find knowledge and structure that in a way that makes sense. And so my personal ethos is that the future of work is around what you do with that knowledge and that becomes your superpower. So here at Decidr, when we look at roles, we focus a lot on archetypes and what type of person we are looking for and how they use the knowledge from their discipline. And so if someone is a builder, they are used to building processes, they think in structure, they're used to building frameworks or sustainable, scalable things. And theoretically that can be applied across different areas with the knowledge that we can gather from AI.
[00:04:23] Tim Brewer: Last year, and the year before, the media was full of doom and gloom about what AI was gonna mean for people in an organization. And for a lot of the organizations we would meet with earlier than the middle of last year, it was a bit of spray and pray approach to like deploying AI, or literally the wild West, everyone was going at it. CEOs were like, I just want AI, I have no idea how that's gonna be applied. What are you seeing with peers in your industries? What challenges does this kind of inflection point cause in organizations with aspirations to leverage AI and the way they deliver value.
[00:05:01] Bianca Hill: It's an interesting question. I, I have also seen the change over the last, you know, six to 12 months in the way that people approach AI. I think, you know, when it first came out in the first wave, everyone thought it was this kind of amazing thing that just knew all the answers to everything, and it was always correct and, you know, chat GPT is always your best friend and kind of pumping you up. But now the conversations that I'm hearing are people talking about how it is such a probabilistic technology and we're trying to jam it into a environment where you need a deterministic response. And so what does that look like?
And I feel fortunate to work for Decidr who. Has been exploring that concept for the last few years, and I'm seeing people talk about intelligence a lot more and the knowledge that you're giving AI and not just prompting. And I think, yeah, the transition that we're in is, you know, AI is a thing, it's here, but it's more about can we get ROI from that and identifying where it's best deployed? I think that AI is really helpful at the moment, at the task level. And so people tend to use it to support their work, whether or not it's gathering knowledge, whether or not it's executing on repeatable workflows. But we are not yet at the stage where it's a, you know, full kind of person or a full kind of role.
[00:06:23] Amy Springer: You are HR in an agentic AI company, you must feel a bit of pressure to make sure your own work is keeping up to speed, staying front of industry. Are you happy to share? I mean, how has the way you approach HR changed the way you come alongside other leaders in the company? What's changed for you?
[00:06:45] Bianca Hill: Yeah, you know, going back 12 months when I first joined Decidr, the reason I was so excited to join was because the business and the platform just aligned with my personal ethos when it comes to work. I think in systems and I think in framework, and I am obsessed with automation. I don't like doing admin and I don't wanna do the same admin task twice.
So historically I've always tried to find different ways of automating what I do, whether it be through conditional logic or systems. And so I think joining Decidr has definitely enhanced that. Both because I've learned so much around the technology, but I also have a lot of freedom to experiment with these things. And with that has come a lot of learning about what AI is, how to use it. And so I have built out a bunch of core projects to assist with different different programs and different aspects of the, the people life cycle. And I use those a lot. I talk about them a lot within the within the HR kind of space in Sydney. It's definitely kind of evolved the way that I work. Not so much in an admin sense, but it does what I'm not good at kind of thing. And an example is I built a Claude project to support with a performance process. We don't have HRIS. That means our performance reviews are in Google Docs. We had, I think 50 or 60 employees at the time. And so I built a core project that analyzed all of the reviews and then aggregated the data and outlined the systemic themes across the organization that was affecting performance. And sure, I could have read 50 Google Docs, but I wouldn't have done a good job of it, and I wouldn't have ended up with the same result. And this took me like six minutes.
[00:08:29] Amy Springer: Yeah. Cool. So that's like your own delivery. What about thinking about the structure, roles, who's doing, what you finding that you guys are good at? Thinking through, okay, what should I be doing? Is there a more structured approach across the company about facilitating the team to think through those as well? Do you take a structured approach to that?
[00:08:52] Bianca Hill: Not super structured and I think, you know, the more we embed our own platform within our operations, that will become more and more structured. But, an AI company, we have all of the AI tools. And so everyone has access to create their own GPT and create their own Claude projects. And there is a wealth of knowledge amongst the team.
So it is always encouraged that we're exploring how we can use those type types of tools to enhance our own work. In addition we're also always looking at other tools that we can bring in and add to our tech stack that are AI powered that can support what we do. At the moment we're exploring a AI powered BDR instead of hiring.
Human BDRs 'cause it obviously scales a lot better and a lot more cost efficient. But we're always looking at how we can Yeah. Leverage different tools and and innovative ways of looking at AI.
[00:09:42] Amy Springer: If you had unlimited time in your day, you're, you're at a scaling company, so much to do, but if you had a couple of days that you could set aside that was that bigger picture of, all right, what are we here to do and who is doing what and where could we be delegating? what would that dream project look like? Like, you know, what's that thinking you haven't really been able to sit down and do that you wish you could do?
[00:10:07] Bianca Hill: It definitely falls in the org design remit. I have a bit of a pipe dream of being able to continuously and accurately forecast an agentic offset. So the amount of time, or the amount of a role that we can offset using agentic capabilities and then roll that up to board level. And then an additional layer on that also would obviously be using Decidr OS agentic apps, it's hard. It's really hard. It's definitely a point of conversation when I'm going through recruitment processes with our leaders and continually talking to them about how some of the responsibilities we could offset by, by different AI powered products, but being able to forecast that on a continuous basis and see how it kind of ebbs and flows and use that to, I guess adoption across the organization is something that I would just love to, to do and I've tried. know, I think four or five times now, but every time I do, I get a first version and then it kind of dies because you have to continuously manage it.
[00:11:16] Amy Springer: So that's your accounting skillset coming in there?
[00:11:20] Bianca Hill: Love a good spreadsheet.
[00:11:22] Amy Springer: Yeah.
[00:11:23] Tim Brewer: Something we've seen a little bit in the media and definitely when we talk to leaders with the doom and gloom stories that exist around AI, some organizations are reporting that as they deploy AI, they end up getting a pretty visceral response from their team rejecting wanting that help and pushing back. And I kind of explained that as the human response of feeling threat and when someone's threatened, that the responses can be pretty chaotic and visceral. When you're talking to others in the industry, I know you speak a little bit at conferences what are you seeing out there and what things can leaders do to try and avoid having their organization revolt, seeing AI as a threat rather than something that gives them a superpower?
[00:12:14] Bianca Hill: Yeah, I think there's two two kind of layers to this. The first layer is. going back to what the future of work looks like and I think historically it's been your knowledge and your experience has been your superpower. And I think we're going through a, a reeducation process with teams and helping them redefine what their superpower is other than the knowledge and experience that they have.
And so I think, that process helps a lot in terms of, moving along the, the process and understanding that AI is not a threat. If you don't value your kind of knowledge as much as you did previously and you value the way you do it, it becomes less important. I think the second piece, which is things that leaders can do to help their teams with understanding and the change management aspect of it is really understanding your workflows. And I think, you know, people often reject what they don't understand. And so if you are able to really understand the workflow in terms of what is, but also the tacit knowledge that lives in people's heads that affect that workflow, you can map it out. You can then identify the certain tasks that AI is going to automate, and you can have that conversation. An educated conversation with your team and say, "Hey, this is the workflow. This is where we're going to embed AI, this is what it's gonna do for you and this is what it's gonna enable you to do". And I think that then becomes a much more powerful conversation than just saying, "Hey, we've got this tool. We're gonna use it now. Go and, you know, figure it out or make sure you use it". It's really a change management piece.
[00:13:47] Amy Springer: And when you talk about workflows, where does that fit in the hierarchy of work for you? Like when you are work at Decidr, sort of the hierarchy of breakdown of work?
[00:13:59] Bianca Hill: Workflows are very important to us. It's what our, our platform is is built on. I think if you go back to our mission at the group level, it is to organize the world's tasks. And so understanding what the workflows are both written down and, and tacit is critical. And I think that's where we always try.
To start we did actually just complete an acquisition of a company based out of the US called Sugar Work. And their their product is knowledge capture. So they go into businesses and they conduct interviews to understand the tacit knowledge and then remap workflows. And so we Sugarworked our own business. It was honestly amazing. The findings were insane. The kind of things they were able to identify. And then what that meant for us when we were able to see what the impact of those things were, and then some of the kind of strategies we could put in place to, to counteract was huge.
So, I think from role design org design to looking at how, how teams work cross-functionally, understanding the workflow and and handoff point is critical.
[00:15:02] Amy Springer: So you see workflows as tying together roles, or you might have a role responsible for a workflow? A bit of both.
[00:15:10] Bianca Hill: So we see workflows as a collection of tasks, a workflow might be identifying a prospect or it could be reviewing a CV. We tend tend to break everything down into quite an atomic level and then we would roll that up into a responsibility and then roll that up into a role and then roll that up into a a position, if you will. And so. I think we always try and start at the most atomic level and then try and roll it up into, into something bigger or something that is more easily understood, both internally and externally, but the workflow is definitely the most important part. And if we haven't thought about that, then our founder Paul will definitely ask the hard hitting questions around what the workflow is, what tasks are involved, and how we're tracking success.
[00:15:59] Amy Springer: you're talking about the ag agentic opportunity and, and talking with the your leaders. I imagine having those workflows clearly documented facilitates that conversation too, rather than a chunk of the role, ambiguous
[00:16:12] Bianca Hill: Yes.
[00:16:12] Amy Springer: specific workflows.
[00:16:14] Bianca Hill: And it's obviously a lot easier to determine ROI because you can break down the role into percentages and then a workflow is a percentage of, of the role. It's definitely not the easiest way. Understanding workflows is really, really difficult. And it's challenging except I think it's the most impactful way 'cause it enables you to have great conversation. It enables you to understand ROI. It enables you to effectively manage the change management process of implementing that AI.
[00:16:45] Tim Brewer: for those people that are listening along and are, you know, using AI for their homework or tasks at work. You know, with ChatGPT or Claude, and they heard you talk about probabilistic versus deterministic. Can you help the audience understand what you meant? About the difference between the two and why deterministic is much harder to achieve.
[00:17:11] Bianca Hill: Yes, yes. Businesses need a deterministic response. If you're doing an account reconciliation, you need it to be done the same way every time. And a lot of the issues or a lot of the challenges that we've seen with AI over the last 12 months have been because that is not happening. AI is built of off text data samples. And you know, you know, when you jump into chat GPT, you write a prompt, you'll get a different response every time. That's a probabilistic response. It's using probability to essentially predict the words that you would like to see next.
The more information that is ingested, the probability ratio of that changes whereas a business. The more accounts that are ingested, that doesn't change the fact for needing an accurate reconciliation. And so when you're trying to jam this thing that is ever evolving and ever changing into a process that you need the same response every time you're going to have issues.
[00:18:09] Tim Brewer: Yeah. Thank you. That was a great explanation. Thank goodness you did accounting.
[00:18:15] Bianca Hill: I always go back to accounting.
[00:18:17] Tim Brewer: Just debits and credits. Debits and credits. Got to balance to zero. They're the big things I remember. A question about some of your discussions in industry. We are seeing. People consider changing how they resource their executive team to deploy AI. And so I know we've seen this argument. Does AI and the agentic workforce fit in the HR org or function? Does it fit in the technology function? Is it a new function? Is it in the operations function? What are you seeing as the big discussions out there? And are you seeing anyone take early bets on where that should land? What are the pluses and negatives that you've seen with it being in those different places of ownership inside an organization?
[00:19:05] Bianca Hill: I've had this conversation a lot and I've realized that I have a a slightly, I have a different answer to a lot of people, the answer that I've seen the most. When, when talking to people is it should live with tech or it should live with the CEO. I think that it lives with HR. AI is a change management process. You're going through an investigation to figure out where it's best used. You're then going to remove the way that humans are doing something, then you're embedding a new technology and you need to make sure that that's working.
So I see HR as owning that, that process. We've always owned change management. I think that when I've seen IT or a systems function own it in the past they, it's not been embedded correctly or it's not been embedded within the organization. And so after two weeks, four weeks, six weeks, usage falls over. I also think that when picking an AI tool that a lot of people particularly tech functions, they will look at the capability of the tool and they won't think about the user behavior when implementing it. And so I saw this, you know, with our own team at, at Decidr we ended up with a bunch of point solutions that used AI and they had great capability, but our team had to leave the CRM to go and use it. It was integrated, but to actually perform the action they had to go to that platform. And there was so much drop off in using the product because everyone's busy they don't wanna leave the CRM if it's not critical. And so I think that partnership with IT, obviously from a data and security kind of perspective, but also the people side, understanding user behavior and the change management aspect is what will make AI successful.
[00:20:58] Tim Brewer: The fear that some people have that this will take away all IC (Individual Contributor) jobs in time. We internally discuss that slightly differently, particularly as we've hit this last inflection point over the last 12 months. We are seeing it be much more likely that instead of IC's going away, IC's are just gonna change their role. And so you'll have people managers, and then you'll have agent managers 'cause agents still need observability and performance monitoring and correction where they're not being deterministic enough. What are you seeing in your discussions about that? Do you have a controversial view in that arena as well?
[00:21:41] Bianca Hill: Roles and the landscape of what we define as a standard role is going to change anyway. And I think we will see that with the shift of, you know, the ease of access of knowledge. So I think it's gonna look different. But I don't think that it's gonna fully replace roles. Something that we, we say a lot here at Decidr is that AI replaces tasks and not jobs. And so when you are looking at the atomic level and you're looking at workflows and you're looking at how AI is influencing those tasks, there is still people that are going to be around that and performing other responsibilities in conjunction.
I definitely do tend to agree with the way that, you guys have Functionly talk about it and say that you know, you'll have agentic managers and people who are managing their AI teams to achieve outcomes.
Every time I've spoken to people about, you know, the future of work and and org design, everyone has a different response. And I love, I love having the conversation because everyone has, has such a different response. I'm excited to see what happens over the next kind of six to 12 months and see kind of where people take it. And if people are looking at pairing of agents with humans, or if we're looking at the role level and an agentic offset percentage I've kind of heard it all in, in different contexts and I'm just kind of keen to see where we head with that.
[00:23:02] Amy Springer: I'm really excited to keep chatting with you 'cause I think you know, when you did accounting and you realized it wasn't for you and you did recruiting and you realized it wasn't for you. But there's such foundational skills for where HR, intelligence resourcing, all of those things are going in the future.
So I am excited to see how your continues to consolidate, and we'll touch base again with another chat later on when you've had some more insights that you're happy to share with us.
[00:23:31] Bianca Hill: Amazing. I'm looking forward to it.
[00:23:34] Amy Springer: Thank you for joining us on the org design podcast. Tim. We'll see you on the next conversation.
Functionly empowers organizations to navigate the complexities of integrating AI into their workflows by providing a structured approach to role design and responsibility mapping. By facilitating clear visibility into workflows and task responsibilities, Functionly allows teams to effectively leverage AI tools, enhancing productivity while alleviating the anxiety often associated with technological change. This ensures that teams can focus on high-value tasks, enabling a smooth transition to an agentic workforce where AI complements human capabilities, ultimately driving efficiency and innovation within the organization.