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Business valuations rely on extensive analysis. When there’s an error in judgment, the consequences are extensive. Given that artificial intelligence (AI) makes a big difference in data-heavy, time-consuming and error-prone processes, business valuations are an ideal use case. In particular, cloud-native AI can drive considerable value in these transactions.

Refining business valuation processes is an increasingly pressing concern for many organizations. As many as 60% of CEOs plan to make at least one acquisition in the next three years. Similarly, 70% of businesses want to use mergers and acquisitions (M&As) to accelerate their tech adoption. Cloud-native AI can improve these processes in several ways.

Automating Asset Discovery

The most straightforward application of AI in business valuation is automating the discovery process. Organizations must have a good understanding of all an M&A target’s assets to determine a fair price. AI helps by discovering and valuing a company’s assets in less time and with greater accuracy.

Formal valuations take three to five weeks on average, which is more than enough time for market conditions to change. That doesn’t include hiccups from errors or settling valuation gaps, either. Even after that time, businesses may overlook some less obvious assets, leading them to an inaccurate price.

AI can analyze the value of a company’s assets much faster, ensuring deals close before conditions change too dramatically. Machine learning models are also better at spotting subtleties in large data sets than humans, so they’re less likely to miss some assets.

Making More Comprehensive Valuations

AI can also provide fairer valuations by quantifying assets based on a wider range of factors. Some parts of a business are easy to value, but others pose difficulties, at least for humans. Valuing them through AI offers a more comprehensive approach.

This automation is particularly useful for intangible assets. The most common way to value intangibles is the income approach, but this only works when the asset produces cash flow. Because AI can analyze more factors at once, it can combine valuation methods to provide the most reliable estimate of an intangible asset’s real value.

Cloud-native AI takes these benefits further. Real-time cloud data streamlines automated discovery and makes it easier to access a broader range of information. With more data, AI models can make more informed decisions about a business’s value, both with its tangible and intangible assets.

Predicting Future Market Trends

Another advantage of AI in business valuation is that it can provide insight into the future. M&A deals typically factor in projected earnings, but predictions like this are prone to inaccuracy when done manually. Predictive analytics is more reliable.

Machine learning models have predicted stock market trends with 93% accuracy, even in emerging economies with less historical data. Applying the same technology to valuations of corporations with more available data could offer significant future insights. Businesses can use these predictions to quantify risks, project deal success or determine a more accurate long-term value.

This high accuracy is even more impressive considering how these models get more reliable over time. As the cloud hosts more data and more businesses employ this technology, it will be able to project further into the future and do so more accurately.

Reducing Costs

Many of these benefits are achievable with on-premises AI-powered software. However, cloud-native AI tools have extra advantages. One of the most significant benefits is their lower costs.

Hybrid cloud solutions as a whole tend to have lower ongoing costs than alternatives because they remove expenses associated with upkeep and maintenance. Security costs are often lower, too. Consequently, cloud-native AI makes predictive business valuations more accessible, even for smaller companies.

M&As are already expensive processes. Reducing the costs of faster, more accurate valuations ensures they don’t become needlessly costly. Organizations can then achieve a positive return on investment faster and have more wiggle room for post-merger adjustment. In that way, cloud-native AI improves M&A success rates.

Improving Scalability

Cloud-native AI solutions are more scalable. A cloud setup means storing or processing more data is as simple as paying for more. There’s no need for expensive, time-consuming hardware upgrades and testing.

This scalability is important for business valuations considering current M&A projections and data growth. Global data volumes will surpass 181 zettabytes by 2025, more than double what they amounted to in 2021. That means AI valuations will become more accurate, but businesses must process much more information for an informed decision, requiring scalability.

Global M&A activity will also likely increase over the next few years. Capturing AI’s valuation potential will become increasingly important amid that trend, raising the need for scalability. Running these models in the cloud provides that flexibility.

Cloud-Native AI is a Game-Changing Valuation Tool

Business valuations have all the hallmarks of a process ripe for AI disruption. As more developers come out with cloud-native AI valuation tools, this technology could thoroughly disrupt M&A activity.

Combining the power of AI and the cloud makes valuations faster, more accurate and more helpful than ever before. Businesses that capitalize on this potential early could see significant growth because of it.