What Makes a Climate Company Investable, and Where AI Actually Helps

Climate and sustainability decisions are increasingly tied to broader questions about cost, risk, resilience, growth, and capital allocation. Companies need to understand how climate-related risks can affect supply chains, energy costs, insurance, regulation, and long-term financial performance. At the same time, artificial intelligence is being introduced into sustainability reporting, investment analysis, supplier screening, and risk assessment, creating useful efficiencies as well as new governance concerns.

In a conversation hosted by Net Zero Compare, Bhuva Shakti, an AI adoption and automation advisor and founder of Bhuva’s Impact Global and Wallet Max, discussed what makes a climate company investable, why sustainability data remains difficult to assess, where AI can improve decision-making, and why automation still depends on reliable data and human accountability.

🎥 Watch the Full Conversation: The full Net Zero Compare interview with Bhuva Shakti explores these issues in greater detail, with examples from climate investing, sustainability reporting, AI governance, and supply chain risk. The discussion also examines why technically strong climate solutions can still struggle to attract capital and where companies should be cautious about automating decisions. Watching the full conversation provides additional context around the trade-offs businesses and investors face as sustainability and AI become more closely connected.

Climate Risk Is Also Business Risk

Shakti’s approach to sustainability is shaped by her background in financial services, where she learned that risks rarely stay contained within one department or one part of the economy. An operational problem can become a financial one, while a regulatory issue can affect trust, margins, or access to capital. She applies the same logic to climate and sustainability, arguing that companies should focus less on whether climate belongs in an ESG report and more on how climate-related events affect the economics of the business.

A supplier shutdown caused by flooding, for example, is not only a supplier or environmental issue. Depending on the importance of that supplier, the disruption can affect production, customer service, revenue, and potentially liquidity. Energy, insurance, and water risks can have similar effects, particularly in locations where extreme weather or resource constraints affect a company’s ability to operate. From this perspective, climate risk belongs within the same financial and operational risk framework companies already use for other material business issues.

A Strong Climate Solution Is Not Automatically Investable

Through Wallet Max, Shakti works with sustainable startups, investors, and operators, and one recurring problem she sees is the gap between having an important climate solution and building a company investors are willing to fund. Founders are often highly focused on the environmental or social problem they are trying to solve, but impact investors may already agree that the problem matters. What they still need to know is whether the company can build a durable and scalable business around the solution.

That means investors still ask conventional questions. Who is the customer? Who pays? How much are they willing to pay? How expensive is customer acquisition? How long is the sales cycle? What are the margins? How much capital will the company need before it can sustain itself?

A technically sophisticated solution can still be difficult to finance if deployment is expensive, infrastructure requirements are significant, or customer acquisition takes too long. A simpler solution may be easier to fund if it produces a clear and measurable financial benefit. For Shakti, the environmental case and the business case need to connect.

What Investors Actually Look For

Scientific quality and technical innovation can strengthen an investment case, but they are only part of what investors evaluate. Shakti highlighted market conditions, unit economics, the founding team, execution risk, and measurable impact as some of the factors that determine whether a climate company is genuinely investable.

She gave the example of a battery storage company that had been successful in Texas but struggled to reproduce that success in Colorado. The company assumed that a model that worked in one state could simply be transferred to another, but the infrastructure, market conditions, customers, and regulations were different. For climate companies, commercialization needs to be reassessed whenever the business enters a new market.

Unit economics matter just as much. Large revenue projections may attract attention, but investors still want to know what happens at the level of an individual customer or transaction. If it costs more to acquire and serve a customer than that customer generates in value, scaling will not fix the problem. Investors therefore look closely at customer acquisition costs, margins, payback periods, break-even points, and the amount of capital needed before the company becomes self-sustaining.

The team also matters because investors are backing people who must respond when assumptions fail. Regulations can change, technologies can evolve, competitors can enter the market, and supply chains can become less reliable. Shakti noted that investors often value founders who communicate problems transparently and show they can adapt rather than pretending everything is going according to plan.

Measurable Impact Is Part of Commercial Credibility

For climate and sustainability companies, investors also need evidence that the claimed environmental impact can be measured and repeated as the business scales. Broad statements about reducing emissions or improving sustainability are less useful than results from pilots, customer deployments, or other measurable outcomes.

If a company cannot show how its impact was measured, whether the results can be verified, or how those results translate across different customers and markets, the investment case remains weaker. Measurable impact is therefore part of commercial credibility, not a separate layer added after the financial case has been made.

Sustainability Data Still Has a Reliability Problem

One of the biggest challenges in sustainable finance is the quality and consistency of the underlying data. Sustainability reports may resemble financial reports, but the information behind them often comes from a much wider range of sources, including direct measurements, supplier questionnaires, estimates, industry averages, third-party databases, modeled results, and self-reported information.

That makes the origin of the number just as important as the number itself. Shakti described a simple principle from her financial career: instead of asking only what the number is, ask where it came from. Companies and investors need to understand whether a sustainability metric was measured directly, reported by a supplier, estimated, modeled, or produced using assumptions that materially affect the result.

She suggested a practical classification system: measured, supplier-reported, estimated, modeled, or unknown. Estimated or modeled data is not automatically unusable, especially in areas such as Scope 3 where direct measurement may be unrealistic. The issue is whether users understand the level of confidence they can reasonably place in the information before using it for public claims, capital allocation, or other consequential decisions.

Where AI Can Actually Help

Sustainable finance and climate-related investment often require teams to process large amounts of financial, regulatory, technical, and operational information. Investors may need to review financial statements, climate disclosures, technical reports, regulatory documents, market research, supplier information, and sustainability claims across many companies. This is where Shakti sees one of the strongest practical uses for AI.

AI can organize information into a consistent structure, compare companies, identify missing data, flag anomalies, summarize technical documents, and run different scenarios. It can also prepare an initial profile of a company that includes its business model, climate claims, missing evidence, and questions requiring deeper diligence. That allows analysts to spend more time on the companies that genuinely deserve closer review instead of starting every assessment from scratch.

Shakti also sees value in using AI to challenge assumptions. Investors can ask a system to identify weaknesses in an investment thesis, surface overlooked risks, or test whether a conclusion depends too heavily on one assumption. In this role, AI improves the efficiency and structure of analysis without replacing the judgment of the people making the final decision.

The Risk of False Precision

The same capabilities that make AI useful also create a risk when the underlying information is incomplete or unreliable. Shakti described this as false precision. AI systems can produce polished dashboards, summaries, and models that look authoritative even when much of the underlying data is estimated or weak.

She used the example of a company that has directly measured emissions data for only 30% of its suppliers while estimating the remaining 70%. An AI system may still create a detailed dashboard covering 100% of the supply chain, but the appearance of completeness does not change the fact that most of the underlying information is estimated.

This is especially relevant to Scope 3 emissions, where companies often work with thousands of suppliers across different geographies and reporting standards. In many cases, estimates are unavoidable. The practical response is to distinguish measured data from estimated or modeled results and make the quality of the underlying information visible to the people using it.

AI Governance Should Match the Risk

For Shakti, the central principle of AI governance is accountability. If an AI-supported decision has material consequences, someone inside the organization should remain responsible for the outcome. A company cannot simply point to the algorithm and treat responsibility as outsourced.

The level of governance should depend on the type of decision being automated. Using AI to draft a social media post creates very different risks from using it to support a major investment decision, terminate a supplier, assess credit, make employment decisions, or support a public sustainability claim. Lower-risk processes can often be automated with limited oversight, while higher-risk decisions require clearer controls, defined responsibility, and the ability for people to review or override the system.

For those higher-risk applications, companies should decide who owns the outcome, who reviews the AI-generated analysis, what happens if the system is wrong, and whether there is a fallback process. AI can automate analysis, monitoring, screening, and administrative work, but accountability remains with the organization.

Where Sustainability Teams Can Start Using AI

For sustainability and ESG teams, Shakti recommended starting with tasks that require substantial manual effort but carry relatively low decision risk. Reporting is one example because teams often spend significant time gathering information, checking consistency, comparing results across years, and identifying missing data. AI can reduce some of that administrative burden and allow professionals to spend more time interpreting the results.

Regulatory monitoring is another practical use case. Companies operating across multiple markets need to follow changes in sustainability and climate-related rules, and AI can help monitor jurisdictions, organize updates, and flag changes that may affect the business. Supplier data is also suitable for partial automation because AI can extract and categorize information, identify missing fields, detect duplicate or unusual entries, and help manage initial follow-up requests.

Shakti was more cautious about immediately automating areas such as risk management, change management, business continuity, and disaster recovery. These decisions depend heavily on context and judgment and can directly affect whether a business continues to operate during a serious disruption. AI may support these processes, but full automation should not be the starting point.

AI Can Help Prioritize Scope 3 and Supply Chain Risk

Supply chains are one of the clearest areas where AI can reduce the scale of manual work. A company with thousands of suppliers cannot realistically perform an equally detailed review of each one every month. AI can help analyze supplier location, emissions data, certification status, climate exposure, regulatory incidents, geopolitical risk, and exposure to water or energy constraints, then identify which suppliers deserve closer attention.

The objective should not be to let AI classify suppliers automatically as good or bad. Instead, the technology can help prioritize where human review is most valuable. If the analysis identifies a small group of suppliers that account for a large share of emissions or operate in regions exposed to water stress, the business can focus its limited resources on those relationships first.

This can also change the nature of the sustainability question. A supplier in a water-stressed region may initially appear to present a reporting challenge, but the more important issue may be whether that supplier will still be able to meet production requirements next year. In that situation, sustainability information becomes directly relevant to operational resilience and supply chain risk.

Looking Beyond Short-Term ROI

Sustainability investments can be difficult to assess if companies rely only on immediate financial returns. Some projects require substantial upfront capital and may take years before the full benefit becomes visible. Shakti argued that companies should evaluate both direct and indirect financial returns, including revenue, lower operating costs, reduced exposure to energy price volatility, better customer retention, lower regulatory risk, and greater resilience during disruption.

For long-term investments, another useful question is the cost of doing nothing. If the company does not invest, what risks or future costs remain? If it does invest, how much of that exposure can realistically be reduced?

Shakti suggested that executives look at three areas beyond a simple short-term ROI calculation. The first is the market and whether its physical, regulatory, and economic conditions support the investment over its expected lifetime. The second is execution and economics, including whether the company can maintain commitment to a long-term project even when leadership changes. The third is risk, meaning whether the project can continue if conditions deteriorate and whether alternative plans are available.

AI Does Not Solve Greenwashing

One area Shakti believes businesses may overestimate is AI’s ability to prevent greenwashing. AI can analyze information, identify inconsistencies, and strengthen due diligence, but it cannot create integrity or accountability where those are missing. If a company provides misleading claims or weak data, AI may simply process and present that information more efficiently.

That is the same false-precision problem seen elsewhere in AI-supported sustainability analysis. A polished output does not make the underlying claim more credible. Verification still depends on reliable evidence, appropriate controls, and people who are responsible for the final conclusion.

Sustainability and Financial Returns Are Becoming Harder to Separate

A recurring theme throughout the discussion was that sustainability should not be treated as an isolated corporate objective. Climate events can affect supply chains, operating costs, insurance, access to energy and water, and returns on investment. As companies gain access to better data, modeling, and risk-management tools, investors may also expect management teams to show that foreseeable climate-related risks were considered as part of normal business planning.

Shakti argued that the discussion is becoming less about choosing between sustainability and shareholder returns. The more useful question is how environmental and operational risks affect risk-adjusted financial performance over time. Climate technology and sustainability investments are easier to assess when organizations evaluate them as business decisions involving cost, resilience, market conditions, and long-term risk rather than treating them only as environmental initiatives.

Conclusion

The conversation points to a practical way of thinking about both climate investment and AI adoption. Climate companies still need the same commercial foundations investors expect from any business: a credible market, workable economics, a capable team, measurable results, and a realistic path to scale. Sustainability data also needs to be judged by its source and quality, particularly when estimates or modeled results are being used in place of direct measurement.

AI can make research, reporting, regulatory monitoring, supplier analysis, and due diligence more efficient, but its usefulness depends on the quality of the information it processes and the controls around how its outputs are used. The strongest use cases are those where AI reduces administrative work, helps teams manage large volumes of information, and directs human attention toward the issues that deserve deeper review. For decisions with material financial, regulatory, operational, or reputational consequences, human judgment and accountability still need to remain in place.