Why the Math Around Adaptive AI is Painful

Artificial intelligence (AI) is expensive.

Companies that are reducing costs while investing in digital transformations to become more agile, lean, and profitable, I get physics! Don’t look too deep. Artificial intelligence strategies are not built on being a cost-saving model.

Technology consulting and adaptive business processes combine the promise to create, automate, and respond with greater speed; many companies consider this power to be a cost-effective, fixed, and decision-making process. Yes, I mean you. it’s true.

Adaptive AI business plans work as organizations better manage their data residing in the cloud, legacy SANs, LUNS, and S3 buckets within Databricks and Snowflake. If you count the data that resides in DR, there is a lot of data. Processing data through AI and ML is old news. Many companies have yet to see a solid ROI for this significant investment. With adaptive AI businesses that require pre-arranged data to make efficient and effective decisions, let’s consider the opportunities.

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Many organizations, including financial institutions, are getting overloaded with large adaptive capabilities with traditional information security systems, information SecOps resources, and MSSPs. Etc. The need for real-time innovation enabled by adaptive AI is a must-use tool to address the growth of cyber threats.

A cornerstone of current and future web 3.0 and blockchain initiatives is the ability to create contracts. Smart contracts and blockchain capabilities will benefit car rentals, health record and billing automation, and passport processing. Adaptive AI and machine learning are critical to this job stream.

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Most agree that adaptive AI will be more effective if there is enough data. Organizations eliminate the cost of data storage, processing, and capacity before AI begins.

In the Splunk example, this company will pay for the amount of data they create and store, as needed! However, many companies only send specific log files to Splunk to keep costs down. Now, in the new world of blockchain and adaptive AI, companies need to increase their budgets to support the amount of data storage to make AI work as designed. .

Some companies consider adaptive AI as a replacement for human capital. AI needs to design its own self-healing, optimization, and rework capabilities.

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Organizations need the right data scientists and analytics resources to be up to date. Combined with mathematics, management, cybersecurity, and development resources, how can adaptive AI become more valuable to organizations?

As I said at the beginning, wait to see the math. Just as combating cybersecurity attacks with continuous monitoring, threat detection, and incident response, blockchain, and adaptive AI require the same discipline. Organizations need to consider their models in continuous development and evolution until the promise of adaptive AI is realized.

Balancing the cost of implementation, cybersecurity, and risk, is adaptive AI the bigger challenge to an organization’s financial vision?

That’s it for a while 🙂

All the best,



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