Write the AI Policy Before You Write the AI Feature
Episode Summary
Trust is where this conversation puts the difficulty, and Maher Hanafi, VP of engineering at Betterworks, manages it in two directions at once. Internally, the team, the leadership and the board have to be brought along, which means understanding the technology and its risks well enough to explain them and win backing. Externally, in HR technology, customers arrive with their own compliance and their own AI acts to satisfy, and adoption gets navigated close to tenant by tenant. His team spent more time on AI privacy policies, transparency and explanation than on building the AI itself, and shipped features small in scope and big on impact that customers could opt into at their own pace. On agents he holds the same line. Agents will reach further into the product, and the human stays in the driver’s seat rather than having decisions made on their behalf. His advice to executives is to learn enough to take part in the decision.
Key takeaways
He splits trust into two audiences, and that is the frame the whole episode runs on. The internal stakeholders are the team, the executive leadership team and the board, and they have to be on board with the journey into building generative AI features
What earning internal trust actually means is specific. Understand the concepts and how they work, then understand the risks involved in adopting AI and how it will disrupt the business either way, and build a plan and a framework the company can explain to its internal stakeholders
The external half is harder in his category, and he says why plainly. Betterworks sells enterprise performance management software into HR technology, which carries a lot of constraints and compliance, so pushback on AI runs higher, he thinks, than in other spaces or in direct-to-consumer products
The unit of negotiation is smaller than the market, and that is the operational point. Enterprise and global customers bring their own rules, policies and AI acts, so adoption gets navigated close to tenant by tenant or customer by customer
He allocates effort in a way that is unusual to hear stated out loud. His team worked on AI privacy policies and on transparency and explanation of how they build and use AI more than on building the AI itself, because they needed to arrive clear, transparent and responsible
The product tactic he runs alongside the policy work has a memorable shape. Ship early versions small in scope but big on impact, give customers early access, and let them opt in at their own pace, because even the best use case can meet an internal policy that says not now
He does not claim victory on adoption, which makes the rest more credible. Adoption is never 100 percent, because enterprise and global customers have a lot of moving pieces and their own processes for looking at AI
The reassessment his customers are running is bigger than the AI features. Long-standing customers have come back to say that because of this new era of AI they have to reassess the whole solution, not just the AI additions
His answer to that reassessment was to arrive before the conversation did. Building trust ahead of those early conversations meant putting together FAQs and use cases and answering questions in advance, as pre-read material customers could get comfortable with
He marks a change in where the resistance comes from, and it is a useful before and after. In the early days everyone said no because they did not know how it worked; now it is more understood and what is being navigated is internal policy and strategy
The pace of AI is a governance problem as much as a speed problem, and he says why. The frameworks built over years for software as a service and cloud are changing with AI, including role-based access controls, data pipelines and data governance
What customers ask about has moved on, and he is precise about the new question. Assessments still ask about data governance, infrastructure and cloud architecture, he says, and now they also ask what the AI will do, whether it makes decisions on behalf of people, and whether it affects performance, outcomes and scoring
On agents he answers both halves of the question rather than picking one. Internally the company will explore agents that give customers enhanced features, and externally customers will bring their own agents to its public APIs
His constraint on internal agents is stated plainly. Agents should not take decisions on behalf of human beings, they should make a complicated process easier, and the human stays in the driver’s seat with full control of what happens
He gives a reason why agents will be slower to arrive in his category than in others. A company cannot take the most sophisticated technology, bring agentic AI into HR technology and hand it all the access controls it needs, so the approach has to be more strategic and the adoption slower where the controls are heavier
The bring-your-own-agent path runs on plumbing that already exists. Customers have access to public APIs, which he says could in some way serve as tools for their agents, so they can build an internal agent that uses those APIs to create experiences outside the platform
His forecast about interfaces goes furthest of anything he says. Today an API exists for other software to consume and for an engineer to code an integration against, and he expects interfaces designed for agents instead, where an agent is told what tool is available and talks to it directly
Asked whether customers want to appraise agents as employees, he is careful to separate today from tomorrow. He is not aware of any conversations going that direction yet, and he can see a future where agents are part of the pool of resources alongside human resources
The future he does describe is specific about design, and he marks it as a future rather than a capability he has. He sees a future in which APIs are designed for agents, and experiences for something like an agent business analyst, which needs access to data under specific controls. He says they do not have that yet as far as he knows
His first piece of advice to executives is about their own preparation, and he is careful about how much he is asking. Executives should go as deep as their job and domain require into what AI is, what it can do and what the risks are, rather than delegating the work to others and to third-party contractors
His analogy for the executive learning curve is deliberately unglamorous. It is the same way people learned about the cloud coming from on-premise, and about the web coming from desktop applications, so he does not think he is asking too much
The structure he recommends is cross-disciplinary and, he says, cannot be owned by engineering alone. He describes a council with a framework that builds trust in the adoption, iterating on something like a flywheel so the organization learns, gains confidence and matures
His advice about what not to do first is aimed straight at agentic hype. Do not follow the most recent trends and start building an agentic flow on day one; the basic features have become affordable, easy and cost-efficient, and are still impactful, he says, for end users
The test he offers is a single question: what is the number one thing that will have a needle-mover impact for customers. Start there, he says, and pursue the sophisticated solutions once the framework and the confidence exist
His final word puts a boundary on the whole subject. He thinks responsible AI will get more attention as AI acts are written and revised, and what he asks of it is that it keeps the human at the center and gives people more power rather than replacing them
About Maher Hanafi
Maher Hanafi is VP of engineering at Betterworks, which builds enterprise performance management software as a service. He works on its generative AI features in HR technology, a category where customers arrive with heavy compliance requirements of their own. His argument on this episode is that trust is one of the pillars of adoption, and that it has to be earned in two directions at once: with the executives and board who fund the work, and with enterprise customers who are reassessing the whole solution they already buy, not just the AI added to it. What his team did about it was to put more work into AI privacy and transparency policies than into the features themselves, and to ship small, opt-in capabilities customers could adopt at their own pace.
In this episode
| 00:58 | Welcome, and who Maher Hanafi is |
| 01:30 | The opening question: building trust in the era of AI |
| 01:43 | Why trust is key to adoption in a fast-moving field |
| 02:05 | Hype, noise, and the two sets of stakeholders |
| 02:26 | Everyone around the business on board with the journey |
| 03:16 | The other stakeholder: customers in HR technology |
| 03:37 | Compliance, AI acts, and negotiating tenant by tenant |
| 04:05 | Understanding a customer’s constraints and expectations |
| 04:23 | Policies, transparency and explanation, more than the AI itself |
| 05:07 | Small in scope, big on impact, and opting in at their own pace |
| 05:38 | Why trust is a pillar of adoption, not a nice to have |
| 06:02 | Are customers still reluctant? |
| 06:18 | Adoption is never 100 percent |
| 06:25 | Customers reassessing the whole solution, not just the AI |
| 07:12 | FAQs and use cases as pre-read material |
| 07:41 | From nobody understands it to navigating internal policy |
| 07:58 | The pace of change as its own problem |
| 08:18 | Why an enterprise should be concerned about a vendor moving fast |
| 08:46 | The frameworks that are changing: access controls, pipelines, governance |
| 09:26 | What the assessments ask about now |
| 10:16 | Getting familiar, and staying inside the boundaries of the system |
| 10:50 | Agents: yours, your customers’, or both? |
| 11:29 | Both scenarios, starting with agents inside the product |
| 11:50 | No decisions on behalf of people, and the human in the driver’s seat |
| 12:18 | Why agents take longer in a category with heavy controls |
| 13:11 | Bring your own agent, through the public API |
| 13:37 | Hit the API, get the data, do what you want with it |
| 14:11 | Interfaces designed for agents rather than for engineers |
| 14:30 | Tell the agent what tool is available and let it talk |
| 15:16 | Would customers want to appraise an agent employee? |
| 15:55 | Not a conversation he is aware of yet |
| 16:40 | Designing for the agent business analyst |
| 17:11 | Guidance for executives |
| 17:53 | Be educated, to the depth your job requires |
| 18:17 | Joining the cross-disciplinary conversation |
| 18:59 | The same learning curve as cloud, and as the web |
| 19:21 | The council, the framework and the flywheel |
| 20:01 | Do not start with an agentic flow on day one |
| 20:49 | Focus on impact, and the needle-mover question |
| 21:06 | What listeners should take away |
| 21:24 | Responsible AI, AI acts, and the human at the center |
| 22:10 | Close |
In Maher’s words
“trust is key to the AI adoption in this very fast-paced AI evolution”
Maher Hanafi (01:43)
“we have been working on our AI privacy policies, on our, the way we use AI in our transparency and explanation of building AI and using AI more than building the AI itself”
Maher Hanafi (04:23)
“Early versions that are small in scope, but big on impact”
Maher Hanafi (05:07)
“The adoption is never 100%”
Maher Hanafi (06:18)
“all these kind of frameworks we built for years when it comes to SaaS and cloud are changing with AI”
Maher Hanafi (08:46)
“we want to ensure that the human is still in the driver’s seat and taking full control of what’s happening”
Maher Hanafi (11:50)
“I think in the future, we’ll be building interfaces that are for agents”
Maher Hanafi (14:11)
“my biggest advice is not go directly following the most recent trends in AI and start to build an agentic flow day one”
Maher Hanafi (20:01)
Resources
Betterworks: The enterprise performance management software company where he is VP of engineering
Ideas and terms discussed
Two sets of stakeholders: The split the conversation is organized around. Trust has to be earned inside the company, with the team, the executives and the board, and outside it with customers, and the work is different in each direction
Policies over features: His account of where the effort went: on AI privacy policies, transparency and explanation of how AI is built and used, more than on building the AI itself
Small in scope, big on impact: His shape for an early release. Enough of it to matter, offered as early access, so customers can opt in at their own pace rather than having it turned on for them
Tenant by tenant: How adoption gets negotiated in enterprise software as a service, because global customers arrive with a lot of rules, policies and AI acts of their own
The frameworks that are changing: His argument for why the pace of AI is a governance problem. Role-based access controls, data pipelines and data governance were built up over years for cloud software, and he says AI is changing all of them
The human in the driver’s seat: His design rule for agents inside the product. Agents handle processes more complicated than summarization or text generation, and he does not want them taking decisions on behalf of people
Bring your own agent: The other half of the agent answer. Customers already have access to public APIs, which he says could in some way serve as tools their own agents use to build experiences outside the vendor’s platform
Interfaces designed for agents: His forecast for what sits alongside the API written for engineers: an interface an agent is pointed at and talks to directly, which he expects to change how business software is built
Agent employees: Christina Ellwood’s framing, offered as a question about whether performance management will need to appraise agents. He is not aware of any such request yet, and describes the design it would require
The council and the flywheel: His structure for making adoption stick. A council, as he puts it, that cannot be owned by engineering alone, with a framework it iterates on, something like a flywheel, so the organization builds confidence and maturity as it goes
Responsible AI: The boundary he puts on all of it, and the note he ends on. Keeping the human at the center of the loop, and giving people more power rather than replacing them, as AI acts are written and revised
Related AI Realized episodes and events
Threat Intelligence Is a Board Question, Not an IT One: Staffan Truvé on why threat intelligence belongs in the boardroom, which is the same argument for executive fluency that Maher Hanafi makes about AI itself.
AI Governance as Code: From PDF Policies to Pipelines: Ken Johnston and Bob Rapp on making governance testable rather than declarative, which is the engineering answer to the policy work Maher Hanafi describes.
Extend Data Governance Into Models, Then Into Agents: Kevin Petrie on trust, data quality and human oversight, extending the governance a company already has rather than building a separate one for AI.
Frequently Asked Questions
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Trust with enterprise customers is built by putting more work into the policy and the explanation than into the feature. Maher Hanafi of Betterworks describes spending more time on AI privacy policies and on transparency about how the company builds and uses AI than on building the AI itself, so that customers meet a clear position on how their data is used. The second half is the rollout: early versions small in scope but big on impact, offered as early access, which customers opt into at their own pace rather than having it turned on for them.
Transcript 04:05 to 05:38
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HR technology adopts AI more slowly because the category carries a lot of constraints and a lot of compliance. Maher Hanafi of Betterworks says pushback runs higher, he thinks, than in other spaces or in direct-to-consumer products, and that enterprise and global customers arrive with their own rules, policies and AI acts, so adoption is negotiated close to tenant by tenant.
Transcript 03:16 to 04:05
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Executives should go as deep into AI as their own job and domain require, which Maher Hanafi of Betterworks frames as enough to take part in the decision rather than delegate it. That means understanding what the technology is, what it is capable of and what the risks are, so an executive can contribute to a cross-disciplinary conversation about the vision, the roadmap and the implementation. His comparison is deliberately ordinary: the same learning people did moving from on-premise to cloud, and from desktop applications to the web.
Transcript 17:53 to 19:21
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Agents should not take decisions on behalf of people, and Maher Hanafi of Betterworks is explicit that he does not want them doing it. Agents get access to more of the product and handle processes more complicated than summarization or text generation, while the human stays in the driver’s seat with full control of what happens. He expects this to make agent adoption slower in categories like his, because a company cannot bring agentic AI into HR technology and hand it every access control it asks for.
Transcript 11:29 to 13:11
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An API designed for agents would be an interface built to be used by an agent directly, rather than by an engineer writing an integration against it. Maher Hanafi of Betterworks contrasts it with how cloud software works today, where APIs exist for other software to consume and somebody has to code against them. His version is closer to a tool description: the agent is told what is available, talks to the interface and gets what it needs. He expects this to change how software as a service is built for business customers.
Transcript 13:37 to 15:16
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Not today, as far as Maher Hanafi of Betterworks is aware: he says he knows of no customer conversations asking to manage an agent’s performance the way an employee’s is managed, and that Betterworks has no such use cases. He does describe the shape it would take: agents joining the pool of resources alongside human resources, and products designed for something like an agent business analyst that needs access to specific data under specific controls. What he points at is the category rather than the idea, because human resources technology carries, in his words, way more guardrails and safeguards.
Transcript 15:16 to 17:11
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Start with the basic capabilities that are already affordable and impactful rather than with an agentic flow, which is the advice Maher Hanafi of Betterworks gives most directly. His reason is that two years on, the ordinary features have become affordable, easy and cost-efficient while still being impactful, and the test he offers is a single question: what is the number one thing that will have a needle-mover impact for customers. The sophisticated solutions come after the framework, the confidence and the maturity exist.
Transcript 20:01 to 21:06
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Responsible AI means keeping the human at the center of the loop and giving people more power rather than replacing them. Maher Hanafi of Betterworks expects the subject to get more attention as AI acts are written, revisited and redesigned, and as everyone’s understanding of the technology keeps evolving. He ties it to building AI for the best of humanity.
Transcript 21:24 to 22:10
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[00:58] Christina Ellwood: Welcome to AI Realized podcast for enterprise executives leading AI deployments. From addressing security, data, and operations challenges to managing the organizational and management changes, AI deployment presents the opportunity to redesign our organizations from the inside out. I’m Christina Ellwood, your host for today’s episode. We’re talking today with Maher Hanafi, the VP of engineering for Betterworks. Welcome, Maher.
[01:26] Maher Hanafi: Hi, Christina. Thank you so much for having me on your podcast.
[01:30] Christina Ellwood: Maher, trust is the core for all business It’s so fundamental. In this era of AI, how are you building trust with your team and with your customers?
[01:43] Maher Hanafi: That’s a good first question. Yeah, I do-- I think trust is key to the AI adoption in this very fast-paced AI evolution, right? Like, we live in an era where every day you wake up, you go to the platforms you follow to see what new updates are in AI, and there is, like, an, a bunch of news and a lot of things happening. So there is a lot of hype, there is a lot of noise as well, and it’s very important to make sure that you have two stakeholders you need to manage when it comes to trust, and they really get, get there with your vision. The first one is your, um, internal stakeholders, I would say your team, your executive leadership team, your board of investors. Like, all the people around your business, they need to be on board with the journey to get into building generative AI features and, uh, the adoption of AI. And I think to build trust, you need to really get to a level where you can earn that trust by first understanding these concepts and how they work, and second, by understanding the risks that are involved in adopting AI and how this will either disrupt positively... I mean, disrupt in either way, but positively or negatively your business and your company. So to be able to have a good, deep understanding of this, come up with a good plan, adopt really or build, come up with your own frameworks that you can really explain to your internal stakeholders to get them to build more trust into the future of adopting AI, uh, internally, but also in the product. And then the other stakeholders are your customers. In, in my case, again, I work for Betterworks. We build a SaaS enterprise performance management software, so our customers are a little bit more tricky to navigate. They are more... They have-- We, we work in the HR technology space, which is, uh, it has a lot of constraints and a lot of compliance that is involved. So I would say the pushbacks on AI is higher than maybe other spaces or direct-to-consumer products, where you need to really navigate all these things, and you navigate them close to tenant by tenant or customer by customer. Because if you are enterprise companies and globally-- global companies, you have a lot of these rules and policies and a lot of these AI acts out there that you need to navigate. So again, to build the trust with your customers, you need to understand their environment, you need to understand their constraints and compliances, and you need to understand their own expectation of AI and navigate all of that. And I think in the early days of gen AI, there was a lot of questions that didn’t even make sense coming from customers, right? Like, where will you use my data to train and how this works and all of that. And as a business that is adopting AI and building AI for our customers, we needed to get A little bit ahead of the curve. And one of the good ways to build trust is we have, and I said this many times, we have been working on our AI privacy policies, on our, the way we use AI in our transparency and explanation of building AI and using AI more than building the AI itself, because we needed to come up as very clear, transparent, and responsible, and aware of all the kind of compliance and, and limitations of the space to get to our customers the best way they can adopt this. And then the other way to really get the trust from our customers was to go and build Early versions that are small in scope, but big on impact, and get them to experiment with that. Give them a- early access to these experiences, and then let them opt in on their own pace. Because even if you come up with the best use case for them, they might have their own internal policies and securities happening that adoption of AI is not for now, and it’s maybe for later. But you need to get them all the reasons for them to opt in and adopt, go into this, and, and hop on the journey with you on AI. So I do agree with you. Trust is a big word in the GenAI and AI, especially in B2B and SaaS, that, uh, sometimes we forget about, and we just build AI because we’re excited about th- its impact, and we forget that adoption is a completely different game, and trust is definitely one of these pillars.
[06:02] Christina Ellwood: Do you still have some customers that are reluctant to use the AI capabilities, or are they pretty much over the initial hump and are willing to use it, but maybe have some constraints around how many of the features they use?
[06:18] Maher Hanafi: The adoption is never 100%, and it’s because, again, we target enterprise customers, global customers. A lot of moving pieces. And even if they are ready to adopt AI, they might have internal processes to look into AI differently. By the way, a lot of our customers, when we, we have them for many years, they came to us and say, “Because of this new era of AI, we have to revisit and do an assessment of the whole solution in general, not just the AI additions.” And this is just to say that they are revisiting their processes. They are building new guidelines around AI adoption for vendors and solutions and SaaS products. So it’s really a, a kind of a push and a pull, and we’re trying to navigate this. So that’s why building trust ahead of even these early conversations was very important. To try to put together a lot of FAQs and use cases and answer questions ahead of time was key for us to navigate these conversations and not be in a place where, you know, every once in a while we have the big customers coming and say, “Oh, AI is not for me, not for now. I have no idea what it is.” So we give them that pre-read material for them to get a little bit more comfortable, and as they figure out their internal policies about AI, adoption or opting in will become easier over time. So definitely we are It moved on from the early days where everyone was saying no because they don’t know how this works, and now it’s becoming a little bit more clear and understood, and we’re just navigating policies and internal kind of strategies, internal strategies of adopting AI.
[07:58] Christina Ellwood: Well, you also mentioned that the technology is changing very rapidly. So that must make it also difficult because as the technology’s moving quickly and you’re having to juggle adopting the new technology while avoiding the pitfalls, but also maintaining the trust. So that seems like that would be a specific challenge as well.
[08:18] Maher Hanafi: Yeah, exactly. If you are an enterprise company and watching all this pace AI is moving with, you definitely should, and you should be concerned about adopting AI from a vendor and, and B2B SaaS product because you don’t know exactly Again, unless you have the right reasons and the right kind of material and, and, and explanations and transparency about what is being done, you, you will be concerned about what is being changing. And all these kind of frameworks we built for years when it comes to SaaS and cloud are changing with AI. All these boundaries and guidelines we had before when it comes to role-based access controls, data pipelines, data, uh, uh, I would say data governance, where the data flows are and how things go, all these are changing with AI. So definitely there is a lot of skepticism when it comes to, oh, it’s gonna, it’s gonna change and break all these gu- you know, boundaries we put for years and we became more familiar with. And even when you look at these assessments coming from these customers, the questions are always about data governance, infrastructure, and cloud solution ar- architecture. But now you see a lot of AI, like what data is flowing, what-- we talked about PIIs before, but now it’s beyond just PIIs, it’s also what interactions my end user customers will have with AI and what role the AI will play in the product. Will it make decisions on behalf of people? Will it impact performance, outcomes, and scoring and all of that? And again, uh, it’s-- these companies have the right to obviously check for these, and from our end, we are proactively working towards making sure that there is a good guarantee for them, there is a good trust relationship we’re building t- for them to really see through our transparency policy and AI privacy policy to really understand where AI, what AI, what role AI is having and what impact it’s having on the product. So definitely, I think over time, continuing on building more trust, but also everyone getting a little bit more familiar with AI and moving away from this fast pace of technology innovation, but just focus on the core implementations of AI and how it’s gonna change our products. And with all of this being restricted with our compliance and domain we’re in, HR tech is different from chatbots and image generation, all of that, so we really need to focus and still build tools and AI-enhanced features within the boundaries of our system and how it was built.
[10:50] Christina Ellwood: Well, at this point, agents are a really hot topic as we, uh, enter into 2025, and that’s a yet another level of complexity in the, um, use case for your, uh, customers and the in-integrations in your product. How do you see agents unfolding, uh, and is it core to the work that you’ll be doing, or do you see your customers maybe developing their own agents that need to complement what you are offering, or will you expect you will be offering agents Uh, and that will be bounded within the product. What does that look like?
[11:29] Maher Hanafi: That’s a very interesting topic because I think both scenarios here will be in play. The first one is internally as a SaaS solution, we will be exploring more agents in a way that will empower our customers and give them enhanced features, but still keep the control in the human hands. We don’t want these agents, again, to be taking decisions on behalf of the human beings. We want them to make easy process that is a little bit more complicated than the regular, what the regular LLM is capable of through, again, summarization, text generation, all of that. So agents will have access to more aspects of the product, but at the end of the day, we want to ensure that the human is still in the driver’s seat and taking full control of what’s happening. And I think that’s the challenge, and I think that’s why agents will take time to get to that level of maturity. And I think you and I, we talked before about the AI maturity framework that we have been looking at and using and leveraging as much as we can because you cannot just go ahead and use the most sophisticated technology following the fast pace of innovation, bringing agentic AI into an HR technology space and just give it all the access controls it needs. That’s not gonna happen. So we really need to be more strategic in our approach, and that’s what I think will take, will make a agent AI, agentic AI adoption a little bit slower in environments like this one where we have way more controls. The other thing, the other side of the coin that you described, which is our customers bring in their own agents. And again, as a SaaS product, we have API access, we have-- we give our customers access to public APIs. So that could be, in some way, tools for the agents to use. So they can build their own internal agent that will leverage our public API in a way that will create enhanced experiences on their end. And we have a lot of customers leveraging our A-A-API, the classic way of SaaS product, cloud solutions. You hit the API, you get the data, you process the data, and you do whatever you want with it. And I think with agentic, it’s gonna make all that flow easier, more kind of ex- it, it will help our customers explore more enhanced experiences on their end outside of our platform, but through our API. And I think that leads me to think about also where I see the future leading the way to building software on the cloud that is, or interfaces like an API, that is more designed for agents. I think our current understanding of cloud and software and distributed systems are mainly about exposing API for other software to consume, for an engineer to go and code an, an integration through that interface, which is the API. I think in the future, we’ll be building interfaces that are for agents. Like, this is the a- agent tool that is available. If you can bring your own agent, go and talk to this interface, you can get whatever you want. So I think that’s where the more mature, sophisticated solution will look like in the future when it comes to B2B and, and, and API exposure for SaaS products. But I think both, and going back to your initial question, I think both internally we’re exploring agents, but more, um, focused on bringing the best responsible use case for agents, and externally, I think exploring agents, bring your own agent solution to leverage our API that might be in the future designed for agents. I think that would be big game as well, and I think that’s gonna change the way SaaS products would work in, in the future.
[15:16] Christina Ellwood: Are your customers asking you to provide features in your performance management software that allow them to manage their, quote, “agent employees”? Are you finding that to be a case like, “Oh, I’ve created this employee we call Pierre, and Pierre is doing our resume filtering and...” or whatever their, you know, performance, uh, assessments or something. So Pierre is actually an agent but is acting in a role and, uh, as an employee, and they wanna manage the performance of Pierre. Are you being asked to, to provide features for that purpose?
[15:55] Maher Hanafi: Uh, to be honest, I’m not aware of any of these kind of conversations going this direction yet. I think, and again, going back to what I was about t- uh, what I was talking about, I think there’s a future where that is definitely a use case, where you can have these agents being part of your pool of resources available, uh, human resources, but also AI resources. I think agents will be, will become that. And today, we don’t have any such use cases. Again, the adoption of this space is slower than other spaces. There’s a lot that is involved. We’re talking about human resources technology that takes more, that has a way more guardrails and safeguards to ensure data governance and no leaks and all of that is, is all in the right place. But I see a future where you, you’re designing APIs for agents. You’re designing experiences for maybe agent employees that have-- like you have an agent business analyst, someone who needs to get access to data in some way with some specific controls and access controls. So you need to design your product for that use case in the future. We don’t have that yet as, as far as I know, but I, I see a future where this becomes more frequent in this space, honestly.
[17:11] Christina Ellwood: I definitely can see that too. Um, and I think it’s interesting that people are anthropomorphizing these agents even at this very early stage. But, um, wh-when you think about the guidance that you want to provide to executives that are listening to our podcast, uh, what, what comes top of mind for you as we enter into 2025 and they’re thinking about deploying AI in their enterprise or providing guidance to their organization about how and when and where to use this technology? What guidance do you have for your peer executives?
[17:53] Maher Hanafi: Yeah, I think the number one thing that comes to mind is to be educated about, to a certain degree of level of details, I would say. Y- y- depending on what you do and what’s your job and what’s your domain of expertise, you need to go as deep as you can to a certain degree to understand what AI is all about, or GenAI in this case, what it’s capable of, what are the risks. So you can be part of a bigger cross-disciplinary conversation, a council, let’s say, that is capable of making decisions internally to leading the vision. You definitely will have AI champions and visionaries and stuff like that, but everyone should be involved from a high level, executive level, to impact the vision, the roadmap, the implementation. So I think for executive leaders, they need to be a little bit more aware of what is this technology, what it’s capable of, what are the risks with it, versus just delegating this work to others and involve third-party contractors that will come and learn into this. And I think, again, I, I think it’s reasonable. I’m not asking too much here. I think there is a reasonable learning path for any executive at this stage. Again, the same way we learned about the cloud moving from on-premise, the same way we learned about web coming from application, desktop-based applications. So I think it’s, it’s the evolution and it’s this era where we need to get a little bit more acquainted with this topic. The other thing i- is, again, that cross-disciplinary involvement. It cannot be just a task or a, a, an owned by engineering or by product engineering. I think everyone at different degrees should be part of this council, should have a framework internally that will build more trust with the adoption And with the success of this adoption, and that can really iterate u-using, uh, again, I, I talked about this before, a flywheel kind of framework that will help you iterate quickly, learn, and then build more confidence, more maturity, and enhance your adoption. Uh, my biggest advice is not go directly following the most recent trends in AI and start to build an agentic flow day one, versus going and exploring the basic features that are out there that now, two years later, honestly became really affordable, easy, cost-efficient, but still very impactful to maybe your end users. So I think my advice is that, is like really have a plan in mind, have people in your executive leadership team be more educated and learn more about the capabilities. Have obviously other experts in your team go deeper and explore, but focus on having impact than having, uh, breadth of execution and go and bring the latest, greatest technologies from AI that we see today coming up. Focus on impact. What is the number one thing that will have a needle-mover experience impact to your customers? And start there. And then again, as you build more maturity and confidence in your framework and your implementation, go pursue these more sophisticated solutions.
[21:06] Christina Ellwood: Those are great ex-- uh, uh, uh, suggestions, uh, for furthering the journey of, uh, the executives as they anticipate adopting AI in their organizations. Would you, um... Is there anything you would like our, uh, listeners to take away, uh, from our conversation today that you haven’t had a chance to mention yet?
[21:24] Maher Hanafi: Yeah, I think, uh, again, highlighting the importance of, uh, building responsible AI. I think that’s gonna be a topic that over time will get more attention as we see more, I would say, AI acts being built and revisited and redesigned. I think everyone’s understanding of AI is evolving over time, and I think all these things will change and evolve. So being able to really make sure we are building responsible AI that is for the best of humanity, that is keeping the human at the center of the loop, that is really giving more power to the human being than just replacing them is key as well. And I think that’s the, uh, final thoughts I would like to share with the audience.
[22:10] Christina Ellwood: Well, thank you so much, Maher Hanafi, vice president of engineering at Betterworks. We really appreciate you talking with us today on AI Realized.
[22:20] Maher Hanafi: Thank you so much, Christina. It’s been a pleasure. Thank you.