Betterworks Uses ‘Horizontal Intelligence’ to Connect HR Silos

The moments that help define an employee's trajectory, including performance reviews and manager feedback, are too consequential to get wrong. AI promises to help managers be better prepared for these important conversations by presenting clear insights that draw from the sea of daily work data. But it can only deliver when it is trusted on all sides.

In my recent conversation with Maher Hanafi, senior vice president of engineering at Betterworks, and Solidigm’s Jeniece Wnorowski, we discussed what it takes to turn AI’s potential into a trusted and valued enterprise solution.

Beyond the Static HR Database

Betterworks describes itself as a talent and performance management platform for global enterprise customers, but Maher is quick to distinguish it from traditional HR software. Where legacy tools function as administrative record-keepers by tracking history, storing documents, and managing lists, Betterworks aims to orient its platform around the flow of work.

“We were looking at the data from a performance lens,” Maher explained. “We’re trying to enable anything that helps go beyond just tracking history…to focus more on the flow of work.” For large enterprises with complex organizational structures spanning multiple regions, that means helping individuals, managers, and business units connect their daily efforts to company-wide goals, a capability that only becomes more valuable, and more technically demanding, as AI matures.

AI as Horizontal Intelligence

Maher offered the useful frame of thinking about AI as enabling “horizontal intelligence.” Before AI, Betterworks’ modules — goals, feedback, one-on-one meetings, talent and skills — operated as largely separate domains. Generative AI has made it possible to interconnect those domains in ways that weren’t previously practical.

“With AI today, it’s just way easier to interconnect all of these,” he said. “I think SaaS products and SaaS platforms will be built as more of an interconnected set of layers that will break the silos between different components and features.”

In practical terms, this means a manager preparing for a one-on-one meeting can receive and review AI-generated insights drawn from an employee’s recent goals, feedback, and performance history before a conversation, rather than manually pulling together and examining months’ worth of data.

Responsible AI as a Design Principle

When AI provides insights that can influence such important conversations, it’s paramount that all parties can trust the system’s output. Operating in this environment, Betterworks has emphasized responsible AI guided by two principles in particular: transparency and explainability. Transparency means the system can show users what sources it drew on to generate a response. Explainability means users understand why an AI suggestion is what it is. With this foundation, when managers are giving feedback to employees based on information AI provides, they can make suggestions and have confidence in the underlying insights.

“We are trying to use AI as a way to really get you as a better individual, better member of the organization and contributing to the big picture versus having AI take control,” Maher said. “You should be in the driver’s seat. AI is just there to help you and be a co-pilot, nothing else.”

Advice for Technology Leaders

As the conversation turned to broader lessons, Maher offered practical guidance for engineering and technology leaders navigating AI adoption inside enterprise organizations.

His first recommendation is simply to stay informed without becoming overwhelmed. “AI is moving very fast…. Picking the one out of the haystack is very challenging,” he said. To manage that, he created what he calls an AI Engineering Lab at Betterworks, a structured environment where engineers could explore tools and run experiments, rather than waiting for top-down mandates on which technology to adopt.

He also urged leaders to take the financial dimension seriously. “There was a huge risk of AI taking too much money without achieving ROI,” he said. “Turning into someone who cares more about the financial aspect and looking at costs on a frequent basis…was a huge success.” In his view, senior technology leaders increasingly need to think with some of the rigor of a chief financial officer when it comes to managing AI infrastructure spend.

Finally, he pointed to the value of frameworks. His own AI maturity framework and a flywheel model focused on planning, building, and optimizing AI systems have helped keep the team oriented even as the technology underneath them continues to shift.

The TechArena Take

Maher’s perspective reflects a measured but substantive view of what AI can deliver in enterprise software, one grounded in the realities of compliance-heavy industries and the organizational complexity of global customers. Rather than positioning AI as a transformation layer bolted onto an existing product, Betterworks has committed to rebuilding the platform’s foundations to make intelligence a native capability. For technology decision makers evaluating AI-powered SaaS in regulated environments, the Betterworks story offers a useful model.