5 Hidden Realities of Valuing A Business in The AI Era

For business owners and investors in 2026, the valuation process has moved beyond simple accounting. It is currently undergoing a structural revaluation. When developing an exit strategy, owners often face the anxiety of leaving money on the table. They also fear selling before they’ve fully captured the AI arbitrage opportunity. Conversely, investors are increasingly wary of technical debt inheritance. They are also cautious of paying a premium for an AI company. This caution extends to a ‘wrapper start-up‘ built on overhyped tech that lacks a sustainable competitive moat.

READ MORE: 2026 Valuing An AI SaaS Overview

For businesses on the fence for an exit, a valuation is a financial mirror that reveals operational health. Due to advancements in AI, the components that a valuation reflects have changed. The definition of assets has shifted toward technical infrastructure and proprietary data. To stay on course with AI, many enterprise leaders are taking it seriously. The stakes are rising, and performance gaps persist across many AI and LLM models. According to IBM, 61% of AI Leaders effectively manage their data to support AI initiatives, compared to only 11% of AI Learners. Many AI leaders are proactively identifying valuation erosion. They do so before the Letter of Intent (LOI) is signed. This can be the difference between a high-multiple exit and a failed deal.

Many businesses are on the cusp of the AI frontier as they consider an exit. Additionally, investors are seeking companies that can integrate new AI technologies. My overall analysis of the M&A market is this: When valuing a business, it’s important to identify blind spots where new tech and software can transform the industry. When valuing, addressing technology will increase valuation and open the door to mergers, investment deals, and high-value acquisitions.

2. Why a Business Valuation is The Best Roadmap For Intangible Assets Value

There is a common misconception among mid-market owners that a business valuation is a reactive post-mortem necessary for a sale. In my practice, I view it as a proactive roadmap for an exit, merger, or acquisition. As with most business evaluations, a business valuation is a formal appraisal that identifies value drivers and operational risks well before a transaction or exit, enabling Normalizing Adjustments.

When performing a business valuation, I aim to determine the business’s true economic value. In the AI era, knowing a company’s intangible asset value is crucial. It provides the confidence needed to navigate the market. This helps determine whether to pursue an exit, a merger, or an investor relationship. From an investor standpoint, a promising business valuation during an acquisition is seen positively. It acts as a future beacon in a pre-deal valuation.

3. Proprietary Data is the New AI Value Multiplier

Recently, in the M&A markets, off-the-shelf AI models and wrapper start-ups have become commodities. As I covered in a recent AI LLM researchtitled “Artificial or Just Artful? Do LLMs Bend the Rules in Programming?”, by Oussama Ben Sghaier, Kevin Delcourt, and Houari Sahraoui, dives deep into how LLMs perform using proprietary data. The research indicates that the proper valuation multiplier depends on how a company leverages its unique data. As LLM experts point out, every company has its own language and proprietary data.

5 Hidden Realities of Valuing A Business in The AI Era. Infographic

For investors seeking an acquisition, several data sources impact enterprise value in the M&A market. One data source sought is the Prompting data within an enterprise data report. The prompting data that passes through instructions is gold in valuations. For clarity, promoting data-driven training applies to both low- and high-volume LLMs. Also, prompting data helps businesses avoid generic AI templates for training language models. As research papers I’ve reviewed have shown, Retrieval-Augmented Generation (RAG) involves connecting the model to a private database. Also, RAG reduces AI LLM hallucinations and increases language model accuracy without permanently altering the model.

With prompt engineering data, businesses can fine-tune AI language models and adjust their parameters to create specialized agentic AI agents that operate independently on the business’s infrastructure and constitute significant Intellectual Property (IP).

4. Technical Due Diligence In The AI Ecosystem

In most business valuations and financial audits, companies present past successes to support future projections. As with technical due diligence (TDD), it protects the business’s future backbone and proprietary data, which is attractive to investors. Recent data indicates that 62% of deals fail to meet financial targets due to poor technical due diligence. In an exit, merger, or acquisition, TDD serves as an early warning system for software health, identifying whether a company has a reliable software engine or spaghetti-code liability.

In the M&A space, investors look for proprietary data, technical debt, and code maintainability. They also look for wrapper start-ups that will drain the budget post-acquisition. In turn, software infrastructure and the system’s ability to handle a 10x load are essential, as many investors look to grow immediately after the sale.

Technical due diligence also involves security and compliance with all industry regulatory requirements. Investors typically will valuate a business’s software, cybersecurity, and compliance before determining whether an investment is warranted. In M&A for software, many investors conduct a technical evaluation team to assess the human element and determine whether the IP is trapped in the heads of a few engineers.

5. SDE, EBITDA, GPU-Adjusted EBITDA Multiples Reality Check

In all business valuations, grounded math is required by investors’ expectations. Typically, in the acquisition market, there are three primary metrics: SDE (Seller’s Discretionary Earnings) for smaller, owner-operated businesses and EBITDA and GPU-Adjusted EBITDA for institutional deals. SDE is a metric used to value small businesses in which the owner is actively receiving a salary. In comparison to EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization), which is used for mid-market and institutional transactions to analyze operating performance.

For a company to move from a 3x SDE multiple to an institutional EBITDA multiple, it must demonstrate in its valuation that its earnings are normalized and that it can operate independently of the owner’s daily involvement. Normalization is the practice of aligning performance with normal operating conditions by stripping away accounting noise. It also presents a stable figure that represents what a buyer can realistically expect to earn under standard conditions.

The GPU-Adjusted EBITDA valuation process involves Identifiable Assets attached to the GPU. These assets are then revalued to their current fair market value. Often, a separate appraisal is required for software beyond book value. In turn, the Net Asset Value (NAV) is calculated by subtracting total GPU-adjusted liabilities from total adjusted assets. This ensures the company’s digital software is valued at its current market value. GPU-Adjusted EBITDA also establishes a valuation floor that can account for the high costs of assets such as GPUs. This method can yield a higher valuation.

Conclusion

In the AI era, a business valuation is not a transaction event necessary for an exit; it’s also an ongoing KPI. It measures the strength of a business’s intangible processes that allow an investor outperform the market. For companies that look toward an exit or an acquisition, value is baked into fine-tuned business models, software, data architecture, and team processes. The latter is what earns the premium multiple in business valuations.

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