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 and planning, owners often oscillate between the anxiety of leaving money on the table and the fear of selling before they’ve fully captured the AI arbitrage opportunity. Conversely, investors are increasingly wary of technical debt inheritance and of paying a premium for an AI company or 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, as the stakes are increasing and changing, as many AI and LLMs have performance gaps. According to IBM, 61% of AI Leaders effectively manage their data to support AI initiatives, compared to only 11% of AI Learners. According to observations, many AI leaders are identifying this valuation erosion before the Letter of Intent (LOI) is signed, as it may 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 looking for companies that can be integrated with 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 long before a transaction or exit occurs, allowing for 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 provides the confidence needed to navigate the market and determine whether a business should consider an exit, a merger, or an investor relationship. From an investor standpoint, an acquisition with a promising business valuation is seen as a beacon for the future 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 the proper valuation multiplier lies in 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 covered have shown, Retrieval Augmented Generation (RAG) involves hooking 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, businesses present past successes to validate future achievements. 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 merger and acquisition 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 whether a system can 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 will generally search for business software, cybersecurity, and compliance initially before considering 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 the metric for valuing small businesses in which the owner is actively involved in daily operations and receives 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 prove in a valuation that its earnings are normalized and that it can function independently of the owner’s daily involvement. Normalization is the practice of aligning performance with normal operating conditions by stripping away accounting noise. Also, it demonstrates a stabilized 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 requiring a separate appraisal for software beyond book value. In turn, the Net Asset Value (NAV) is calculated by subtracting total GPU adjusted liabilities from total adjusted assets, ensuring that 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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