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Forbes
11-07-2025
- Business
- Forbes
From Hunch To Hard Science: Why Smart Clean Tech Investing Starts With Good Data
Michele Demers, Founder & CEO of Boundless Impact Research & Analytics. Clean tech investing has matured well beyond the early days of intuition-driven bets. I've seen firsthand that even the most seasoned investors can fall into the trap of relying on optimistic forecasts and surface-level emissions data. Most serious investors recognize that we've moved beyond gut instincts. But while environmental data is more available than ever, much of it is still used to support a good story instead of validating whether that story holds up to scrutiny. To separate promise from proof, venture capitalists should rely on two science-based tools: Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA). These are not nice-to-haves. They are essential if you want to understand whether a technology can actually compete in the real world. Life Cycle Assessment: More Than Just A Carbon Number Life Cycle Assessment measures the environmental footprint of a product or technology across its entire life cycle—from raw material extraction to production and delivery. It's the only method that offers a statistically rigorous, cradle-to-gate analysis of environmental performance. It evaluates emissions, toxicity, resource use and other critical indicators that self-reported data almost always overlooks. Unlike estimation-based reporting, LCA follows internationally accepted ISO standards and is widely used by federal agencies, including the Department of Energy and the Department of Agriculture. Most sophisticated investors and institutions now insist on this level of rigor. They rely on LCA to validate technology claims and reduce exposure to unproven or underperforming products. At Boundless, we work with both investors and innovators to provide third-party validation. Our process includes benchmark comparisons, expert review and comprehensive analysis of multiple environmental indicators. There are times when companies are genuinely surprised by the results—like the biopesticide company we assessed that had great toxicity performance but a poor GHG footprint because no one had thought through the logistics of their supply chain. LCA caught what conventional diligence didn't. Techno-Economic Analysis: The Business Reality Check While LCA clarifies environmental impact, TEA focuses on cost and scalability. In other words, TEA is about whether the economics make sense once you move past the pilot phase. It breaks down capital and operational expenses to a granular level. Investors often have to consider the theoretical performance of new tech. TEA helps move theory to reality. TEA is especially useful in identifying gaps or inefficiencies in production processes, energy usage or sourcing decisions. I can't tell you how many times we've seen a company with an impressive emissions reduction per unit but costs that skyrocket because they're flying materials or integrating parts of their supply chain halfway across the globe. These considerations are easy to overlook, but they can undermine a company's long-term competitiveness. Both assessments serve as reality checks. They protect investors from the reputational, regulatory and financial risks of greenwashing. They provide the level of clarity needed to guide investment decisions in a space filled with novel ideas and technical complexity. Avoiding The 'Valley Of Death' The funding gap between early seed capital and commercialization is commonly called the 'valley of death.' This stress-filled stretch of go/no-go is difficult for new companies to navigate. It used to take six weeks to raise a Series B; now it can take six months or longer. Without strong data to support claims, even promising companies can stall during this critical phase. A credible LCA helps bridge that gap by offering the robust third-party validation investors increasingly require. In my experience, the companies that invest in a serious LCA are the ones that get funding faster because investors trust the numbers. It accelerates trust, unlocks funding and prevents companies from being stranded just short of scale. For startups, investing in a quality LCA often proves less costly than the months lost chasing capital without it. What Investors Should Expect A clear view of a company's environmental footprint also sheds light on its supply chain resilience and technical feasibility. If you're looking at a battery startup, for example, you have to ask: Are they dependent on minerals sourced from regions with geopolitical risks? What happens to their margins if the price of lithium doubles overnight? If you're investing in clean tech, here are five things to look for in a reliable LCA: Quality matters. Weak or self-produced assessments can look fine on a spreadsheet but fall apart when regulators or customers start asking questions. They can mislead investors and backfire on companies when challenged. Evidence-Driven Investing Environmental data should be treated with the same rigor as financial data. Yet too often, it's still used to support narratives rather than drive decisions. If you're an investor, you have to insist on proper third-party LCAs, because shortcuts here can lead to big losses later. Investors need to educate themselves on what makes environmental analysis credible and insist on third-party assessments as a baseline for due diligence. Done right, LCA and TEA can identify risk while simultaneously revealing opportunity. Clean tech is too important—and too complex—to rely on instinct alone. If we want to scale the solutions that can truly move the needle, we have to start with good data, not just good intentions. Forbes Business Council is the foremost growth and networking organization for business owners and leaders. Do I qualify?
Forbes
24-06-2025
- Business
- Forbes
Making Recruiters AI-Powered, Not AI-Replaced
Paraform product Paraform Silicon Valley has a new obsession: eliminating humans from recruiting. Venture capitalists have poured billions into AI-powered resume scanners, chatbots that conduct interviews, and algorithms that promise to find perfect candidates without human intervention. The pitch is seductive: Why pay expensive recruiters when artificial intelligence can do it faster and cheaper? The integration of artificial intelligence into recruitment processes represents a fundamental shift in how organizations identify and evaluate talent, yet it raises profound questions about the nature of human potential itself. While AI-powered systems can process thousands of resumes in minutes and identify patterns invisible to human recruiters, they simultaneously risk codifying historical biases and reducing complex human capabilities to algorithmic scores. Major corporations have documented substantial gains from algorithmic hiring - e.g. Unilever reduced its recruitment timeline by 75% while processing nearly two million applications annually, saving over £1 million and 50,000 candidate hours through AI-driven assessments. Yet this efficiency revolution carries an uncomfortable irony: these same systems may systematically exclude unconventional candidates who don't conform to algorithmic patterns, potentially filtering out the very innovators and disruptors that drive organizational breakthroughs. The paradox becomes stark when considering that the most valuable employees are often those who defy easy categorization: the college dropout who built a billion-dollar company, the career changer who brought fresh perspective, or the candidate whose resume gaps hide periods of crucial personal growth. As AI recruitment tools become ubiquitous, organizations face a critical choice between operational efficiency and the messy, unpredictable reality of human potential - a decision that may ultimately determine whether they optimize for today's needs or tomorrow's breakthroughs. Make no mistake, the AI recruiting startup ecosystem is reaching a watershed moment. Companies like Mercor have had a meteoric rise from dormroom idea to $2 billion valuation in just 18 months—a trajectory that encapsulates both the immense promise and inherent contradictions of algorithmic hiring solutions. Meanwhile, players like Borderless AI are using agentic workflows and AI as an early AI-powered company in the HR and Employer of Record space. On the flipside, Eightfold AI is focusing on what happens after hiring: boosting productivity and managing talent using AI. But the ultimate test won't be whether they can attract venture capital or process millions of resumes—it's whether they can solve the human problem of matching the right person to the right role without perpetuating the very inefficiencies and biases they claim to eliminate, all while navigating an increasingly skeptical regulatory environment that questions whether algorithmic hiring truly serves workers or simply serves the algorithm. Time-to-Hire overview: Traditional vs. AI powered Vs Skill ... More based. Source: Recruiter Powered Marketplace Instead Of an Automation Rush When it comes to that age-old challenge, an intriguing counter-trend is emerging in the shadows of this automation rush. While most companies chase the promise of fully automated hiring, a growing number of companies are discovering that their most critical talent decisions actually require more human expertise, not less. And they're willing to pay for it. Enter Paraform , a startup betting big on that shift. The company just raised a $20 million Series A to scale its recruiter-powered hiring marketplace. Led by Felicis Ventures, the round also included participation from A* and strategic angel investors like the co-founders of Canva, Instacart, YouTube, and xAI. Rather than using AI to replace human recruiters, Paraform says that its AI makes recruiters faster, sharper, and far more profitable. The result is a new labor model -- one where elite recruiters act more like sports agents than back-office support, and companies get better results on the hires that matter most. Paraform's customers offer a glimpse into how companies are adapting to meet high-stakes hiring needs. Apriora , a Y Combinator-backed company developing AI agents, used Paraform to hire four engineers in one month, reporting a fivefold reduction in time to hire and a 90 percent cut in recruiting overhead. Carma , a fleet management platform, turned to Paraform to find its founding engineer after struggling with traditional agencies. In both cases, the technology helped widen the funnel and accelerate the process, but the final decision came down to human judgment. Paraform CEO John Kim Paraform "AI can't evaluate soft skills, predict team dynamics, or assess whether a candidate will meaningfully contribute to long-term success," said Paraform's CEO John Kim. "For early and growth-stage companies hiring for critical roles, the difference between a good hire and a bad one can shape the entire future of the business." The Rise of Enhanced Human Expertise The AI recruitment market is now growing toward a projected $1.12 billion by 2030 , with 87% of companies now employing AI-driven hiring tools. Jason Rumney, who runs Intelletec Group, a Paraform partner that specializes in executive placements, said they aren't competing against AI — they're collaborating with it. "The best platforms use AI to handle the busywork so we can focus on what actually drives results — building relationships and closing critical hires,' he said. At recruiting agency Continuity Partners, founder John Keenan placed four candidates in a month, generating $82,000 in billings. By automating manual tasks like candidate submissions using Paraform's AI features and giving his team instant access to a steady stream of open roles, the platform has helped him cut 20 hours a week of operational overhead. 'As the market increases, the way I always think about it is like this: If I place one candidate a week, I make $20,000 off that placement,' said Keenan. '20 times five is 100, so I would make a million dollars a year if I placed one candidate a week." It's a trend that reflects broader shifts in the workplace: a recent Gallup study found that 93% of Fortune 500 CHROs are adopting AI to boost efficiency, with nearly half of AI users reporting productivity gains. For recruiters using tools like Paraform, that efficiency translates into real business outcomes – from reduced overhead to higher placement volume (and earnings). The Future of Professional Services The transformation underway in recruiting may offer an early glimpse into how AI could reshape professional services at large. Instead of replacing human workers outright, its increasingly being deployed alongside them—streamlining repetitive tasks while leaving space for judgment, creativity, and relationship-building. This model could extend to consulting, creative services, legal work, and other knowledge-intensive fields. A recent report from McKinsey lends weight to this idea, noting that workers who adopt AI tools are not only more productive, but also more focused on the kinds of high-leverage work – like strategic decision-making and problem-solving – that define expertise in complex domains. "Speed and talent are everything in today's world," notes Peter Deng, a partner at Felicis Ventures who led Paraform's Series A round. 'With every search, the Paraform network compounds in intelligence - improving candidate matching, interview efficiency, and recruiter-role fit.' The companies that can identify and close the best candidates fastest gain compounding advantages in rapidly evolving markets - and this is equally true today when some companies are apparently hiring AI researchers - not to build products, but to prevent their competition from hiring them. And while prominent VC funds and investors pump capital into AI powered HR solutions and openly debate whether or not deeply human functions like HR should be outsourced, the future may not be a matter of human Versus machine - but rather, one where the most powerful solutions are human-powered and machine-enhanced.
Associated Press
31-03-2025
- Business
- Associated Press
Factor Releases Benchmark Study on GenAI Adoption in Legal Departments
Benchmark data highlights what's holding legal teams back—and what the legal AI leaders are doing differently. 'Legal departments are often forced to make high-stakes technology selections like Venture Capitalists, betting millions on platforms that may become obsolete within months.' — David Mainiero, AI Enablement & Legal Transformation, Factor NEW YORK, NEW YORK, UNITED STATES, March 31, 2025 / / -- Factor, the market leader in Integrated Law, today released its 'GenAI in Legal Benchmarking Report 2025,' revealing a significant disconnect between AI access and effective utilization in corporate legal departments. The study of more than 120 in-house legal teams provides insights into the current state of GenAI adoption, highlighting both implementation challenges and proven strategies for success. Key findings include: Access vs. Utilization Gap: While 61.2% of legal departments provide AI access to most or all team members, 33.7% of legal professionals report they are not confident using enterprise AI tools and need more support. Pilot Purgatory: 29.6% of legal departments restrict AI access to small pilot groups rather than deploying it widely, significantly constraining potential impact. Leadership Minority: Only 12.1% of legal teams report 'leading the way' in GenAI adoption, with the majority finding themselves at average (35.4%) or behind the curve (26.3%). Build vs. Buy Approaches: 47.5% of legal teams have built an internal AI interface/chatbot, while 40.4% have purchased specialized legal-focused AI tools. David Mainiero, VP, AI Enablement & Legal Transformation at Factor, says: 'Legal departments are often forced to make high-stakes technology selections like Venture Capitalists, betting millions on platforms that may become obsolete within months or even just pivot away from the initial use case.' The report also reveals that members of Factor's Sense Collective are outpacing the market in AI adoption, with 100% providing broad AI access (vs. 46.2% market average) and 81.8% building internal AI interfaces (vs. 35.8% market average). The full report, available for download here, includes detailed analysis of current adoption patterns, benchmarking data, and recommended best practices for legal departments navigating the AI transformation journey. Join Factor's David Mainiero and our industry panel, featuring LegalTech & AI Consultant, Peter Duffy and GSK's Kelly Clay, for our upcoming webinar " GenAI in Legal: New Data on What Actually Works" on Wednesday, April 9th at 11am EDT. Factor is the market leader in AI-Integrated Law, working with corporate legal departments to integrate intelligent capabilities across legal and transactional functions such as contracting. With 10+ years of experience in enabling complex legal work at scale, Factor brings agile, practical solutions to address the many dimensions that must be solved for modernizing legal operations. From re-skilling legal teams to advancing high-impact applications of AI, Factor helps Legal and contracting functions achieve new levels of efficiency and business value. Factor is not a law firm and does not provide legal advice. For more information, go to or LinkedIn. Get Factor Legal Disclaimer:
