HRTech Interview with Gershon Goren, Founder and CEO of Cangrade

Welcome to HRTech Cube, Gershon. We’re delighted to have you. To start, could you walk us through your professional journey and what led you to founding Cangrade?
My professional journey started in technology and entrepreneurship. Prior to founding Cangrade, I led the engineering group at Webdialogs, a company focused on online meeting and communication solutions that was acquired by IBM. After that acquisition, I served as Chief Software Architect in the Lotus group at IBM, where I worked on what became IBM SmartCloud, a cloud-based collaboration suite.

That experience was formative — it gave me firsthand insight into how large organizations approach talent and collaboration, and also how much opportunity there was to improve how teams are built and supported.

After leaving IBM, I explored a number of ventures, but it was clear to me that hiring and talent decisions were still being made without the kind of rigorous, fair data that other business functions take for granted. Together with collaborators doing early research in AI and machine learning applied to talent data, I saw a chance to bring a better, more objective approach to hiring and talent management. That insight — making hiring decisions more data-driven and equitable — is what led me to found Cangrade in 2014.

You’ve predicted that the AI market will cool down in the near future, potentially triggering economic turmoil. What indicators are you seeing that suggest this slowdown is imminent?
What I’m watching most closely is the jobs market, because it tends to reflect reality faster than headlines or valuations.

Right now, we’re seeing a clear divergence in hiring. While there’s still demand for a narrow set of highly specialized AI roles, overall tech and business hiring has slowed, job postings are down, and many companies are quietly prioritizing cost control over expansion. That tells me organizations are becoming more cautious about where AI is actually delivering ROI versus where it’s still experimental.

At the same time, layoffs and hiring freezes across tech and adjacent industries signal a shift from “growth at all costs” to efficiency and productivity. Historically, that kind of reset often follows periods of intense hype and investment.

But a cooling AI market doesn’t mean AI is going away. It just means companies will be far more selective. The focus will move from chasing shiny tools to using AI where it measurably improves hiring quality and drives performance. That shift is already visible in how organizations are rethinking workforce planning and talent evaluation today.

As the hype cycle begins to ease, how do you think organizations should reassess their current AI investments to avoid overreliance or misallocation of resources?
As the hype eases, organizations need to shift from asking “Is this AI-enabled?” to “Is this delivering measurable outcomes?”

The first step is to reassess AI investments against clear business and people metrics—quality of hire, time to productivity, retention, long-term success predictors, etc.—not novelty or adoption alone. If an AI tool can’t demonstrate impact on those outcomes, it’s a signal that resources may be misallocated.

Second, leaders should be wary of over-automating human judgment. In HR especially, AI should support better decisions, not replace accountability. Systems that lack transparency, explainability, or validation can introduce legal and operational risks that aren’t worth taking.

At Cangrade, this is exactly how we’ve approached AI from day one. Our assessments are built to predict job performance beyond hard skills and experience, and we validate them continuously against real hiring outcomes. That means customers can see clear ROI in areas like quality of hire and retention, while maintaining transparency and compliance.

The goal isn’t to automate hiring, but instead give HR teams better, fairer, evidence-based insights to make more confident hiring decisions.

You’ve also said enterprises will get much more serious about adopting AI as a response to difficult market conditions. What types of AI-driven efficiencies or capabilities do you believe will see the fastest adoption?
When market conditions tighten, enterprises focus on efficiency, predictability, and risk reduction, and that’s where AI adoption accelerates fastest. We’ll see the quickest uptake in AI that helps organizations do more with existing teams—particularly tools that reduce manual work, improve decision consistency, and surface insights that would otherwise require significant time or headcount.

In HR, that includes AI-driven screening and assessment to prioritize the right candidates faster, workforce analytics that identify performance and retention risks earlier, and tools that help standardize decisions across managers and locations. At Cangrade we’ve recently released several new tools to facilitate this for job simulation, reference checking, and resume ranking to name a few.

At the same time, some companies will misread this moment as an opportunity to overinvest in AI as a headcount-reduction strategy. This is a mistake that always backfires. Replacing people without rethinking processes, accountability, and decision quality negatively impacts areas like hiring, compliance, employee (and customer) experience—where context and judgment still matter. Just look at recent headlines about Klarna and others.

The organizations that get this right will use AI to augment human capability, not eliminate it. They’ll invest in tools that help smaller teams make more consistent and strategic decisions, rather than chasing short-term cost cuts that undermine performance, trust, and long-term success.

With some AI companies beginning to claim they’re getting close to achieving AGI, how should business leaders evaluate these claims, and what impact do you see this having on the competitive landscape?
Business leaders should approach AGI claims with a healthy amount of skepticism. Most of what’s being labeled as “AGI” today is still narrow, task-specific intelligence. It can be very powerful in certain domains, but far from the kind of generalized reasoning, judgment, and adaptability that true AGI would require.

For decision-makers, look beyond how impressive the demo looks and ask the hard questions: Does it perform reliably outside controlled conditions? Can it explain its decisions? Can it be audited, governed, and used responsibly in real business environments? If the answer is no, then it’s not AGI, and it’s not ready to be a foundational layer for enterprise use.

These claims will likely widen the gap between hype-driven adopters and disciplined ones. Some organizations will overextend, betting on unproven capabilities, while others will more practically stay focused on applying AI where it delivers real value today. Over time, the advantage will go to companies that treat AI as a tool to improve outcomes—not as a speculative leap toward general intelligence that, in reality, remains a long way off.

From the perspective of talent and hiring, how do you expect shifts in the AI market—cooling, consolidation, or increased enterprise adoption—to reshape workforce planning?
From a talent and hiring perspective, these shifts will push organizations toward much more deliberate workforce planning.

Companies will move away from broad hiring toward fewer, more clearly defined roles tied directly to business outcomes. We’re already seeing this in the narrowing of “AI” job titles into more applied, domain-specific positions, and in greater scrutiny around what skills actually translate into performance on the job.

At the same time, increased enterprise adoption will change how companies hire, not just who they hire. There will be greater emphasis on predicting performance and adaptability, rather than hiring based on pedigree or buzzwords. Organizations will look for people who can work effectively alongside AI, learn quickly, and bring their human qualities and competencies to work. This is where we’ll see soft skills and potential outshine previously lauded hard skills and experience.

Overall, workforce planning will become more evidence-driven. Leaders will need better data to decide where to invest in people, where AI can responsibly augment work, and how to build teams that remain resilient as technology and market conditions evolve.

The companies that win with AI won’t be the ones that adopt it fastest. It will be the ones that apply it most intentionally, with clear ownership, measurable outcomes, responsible practices, and human judgment at the center.

As AI becomes more deeply embedded in decision-making processes across industries, what role do you see ethical oversight and responsible innovation playing in the next phase of market maturity?
As AI becomes more embedded in decision-making, ethical oversight needs to be a core operating requirement. Organizations are already held accountable for how decisions are made—whether systems are fair, explainable, and aligned with real business and human outcomes.

This is especially true in areas like hiring, promotion, and workforce management, where AI directly affects people’s livelihoods and trust in the organization. We’ve already seen how detrimental AI can be via Amazon’s famous gender-biased hiring algorithm, and though we’ve come a long way since 2018, these issues still persist.

Responsible innovation means building governance into AI from the start: clear ownership, continuous validation, bias monitoring, and human accountability at the decision level. Companies that treat ethics as an afterthought will face increasing legal, reputational, and operational risk. Those that embed responsible AI practices early will earn trust, make better decisions, and ultimately gain a competitive advantage as the market matures.

As a founder and CEO, what personal leadership or strategic approach has helped you navigate market uncertainty and rapid technological change?
One approach that’s always served me well is an adaptive mentality. Market conditions, competition, and the economy are changing rapidly and unpredictably. Things happen beyond your control. What I’ve found in my years leading Cangrade is that this game is truly the survival of the most adaptive. If you don’t adapt to consistent change, you don’t survive.

What advice would you give to business leaders who feel pressure to “do something with AI” but don’t yet have a clear strategy or infrastructure to support meaningful adoption?
The last decade has been defined by the “move fast and break things,” mentality but what leaders really need to do is focus more on intentionality.

Feeling pressure to “do something with AI” is understandable, but moving too quickly without a clear problem, data foundation, or ownership just leads to wasted spend and unnecessary risk. Instead of starting with the technology, leaders should start by identifying specific problems or workflows where better information would materially improve outcomes.

From there, invest in pilots that are measurable, governed, and designed to integrate with existing processes, not standalone experiments. Make sure there’s clarity around accountability, data quality, and success metrics before expanding further.

AI delivers real value when it’s applied with intention. Organizations that take the time to build the right strategy and infrastructure will move slower at first, but much faster, and far more strategically in the long run.

Before we wrap up, do you have any final thoughts you’d like to share about the future of AI, the risks and opportunities ahead, or how companies can prepare responsibly for what’s coming next?
AI is a powerful tool. It’s changing the fabric of work and society. We should be smart and vigilant about how we adopt it and how we use it strategically. But we also shouldn’t forget that for us humans, people will still be the key factor for all our successes and failures.