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For business owners and investors in 2026, the valuation process has moved beyond just price and is currently changing. When preparing an exit strategy, owners often face the anxiety of leaving money on the table. While also being concerned about selling before they’ve fully captured the AI arbitrage opportunity. Also, investors are growing increasingly wary of inheriting technical debt and acquiring a wrapper start-up. They are also concerned with software built on over hyped tech that lacks a sustainable competitive moat.
1. The Valuation Blind Spot
For businesses on the fence about an exit, a valuation is a financial mirror that reveals the business’s operational health. Due to advancements in AI, the components that a valuation reflects have changed. The definition of promising assets has shifted toward its technical infrastructure and proprietary data. To stay on course with AI, many enterprise leaders are taking AI implementation seriously before an exit. As the stakes in valuations rise, performance gaps persist across AI and LLM models. According to IBM, 61% of AI Leaders effectively manage their data to support AI initiatives. That’s compared to only 11% of AI Learners. While many owners are proactively identifying valuation erosion, many businesses are on the cusp of the AI frontier.
READ MORE: 2026 Valuing An AI SaaS Overview
2. Why an Exit Plan is for Asset Value
There is a common misconception among mid-market owners that an exit plan is not necessary for a sale. In my practice, I view an exit plan as a roadmap for an exit, merger, or acquisition. As with most business evaluations, an exit plan is a formal strategy. An exit plan that identifies value drivers, operational risks, and provides value for an exit or merger.
While preparing an exit plan, it’s important to determine the business’s true economic value. As well as how it can be an asset to another owner or company. In the acquisition market, knowing a company’s asset value provides the confidence needed to navigate the market. Having a strategy for exiting and even merging helps buyers close on the deal sooner. Rather than contemplating whether to pursue an exit or a merger. From an investor standpoint, a business’s exit plan is 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 research report titled 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 LLM AI and SaaS can be trained using a business acquired data. In today’s market for software, the proper valuation multiplier depends on how a company leverages its proprietary data. As LLM experts point out, every company has its own language and proprietary data.
In recent AI SaaS acquisitions, several proprietary data sources have impacted business value in the M&A market. One data source sought is prompting data within an enterprise data report. The prompting data that passes through is gold in valuations as it can train AI systems and establish new guardrails. Also, prompting data helps acquiring businesses avoid generic AI templates for training language models. As research papers I’ve reviewed have shown, Retrieval-Augmented Generation (RAG) involves connecting the language model to a private database. With proprietary data, businesses can fine-tune AI language models and adjust their parameters. They can also create specialized agentic AI agents that operate independently on the business’s infrastructure.
READ MORE: Google’s New Antigravity Now Plugs Directly Into Your Enterprise Data
4. Technical Evaluations In The AI Ecosystem
In most technical evaluations, companies present past successes to support future projections. For an acquire of a SaaS, technical due diligence protects the business’s future backbone and their investment. Recent data indicates that 62% of deals fail to close due to a poor technical evaluation. In most deals, technical due diligence is done to identify whether a company has a reliable software engine.
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, an AI SaaS software infrastructure and its security are what technical due diligence reports are built on. A SaaS with security and compliance within all industry regulatory requirements presents a valuable asset to a future buyer. Investors typically will evaluate a business’s software, cybersecurity, and compliance before determining whether an investment is warranted. In M&A for software, many investors will also conduct a technical evaluation to determine whether IP is trapped in the heads of a few engineers.
5. Asset Multiples
In all business valuations, grounded math is required by investors’ expectations. Typically, in the acquisition market, a SaaS must demonstrate in its valuation that its earnings are normalized. Also, the business must operate independently without 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 valuation process of asset multiples involves identifying additional assets and the value of proprietary software. In a SaaS valuation, assets should be revalued to their current fair market value. Often, a separate appraisal is required for software beyond book value. In turn, a net asset value should be calculated for total adjusted assets. Realizing additional assets ensures the company’s digital software establishes a valuation floor.
Conclusion
In the AI era, a business valuation is a strategic plan that’s necessary for an exit. It’s also an ongoing KPI. As a valuation measures the strength of a business’s internal processes. This allows an investor to forecast how they will perform within the market. For buyers, value is baked into fine-tuned business models, software, data architecture, and security processes. When valuing a business, it’s important to identify blind spots where new tech and software can transform the industry. Also, evaluating the technology will open the door to mergers, investment deals, and high-value acquisitions.
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