Why the Math Around Adaptive AI is Painful

Artificial intelligence (AI) is expensive.

Companies driving lower costs while investing in digital transformation to become faster, leaner, and more profitable, I get the physics! Don’t look too deeply into it yet. Artificial intelligence techniques are not built on being a cost-saving model.

Adaptive artificial intelligence and machine learning business models include the promise of high-speed processing, automation, and response; many organizations consider this capability to be the most cost-effective, organized, and rational decision. Okay, I hear you. Indeed.

Active AI business strategies are effective because organizations will make more sense of their data sitting in the cloud, precious SANs, LUNS, and S3 buckets within Databricks and Snowflake. If you count the data sitting on the DR, that’s a lot of data. Data modeling with AI and ML is old news. Many organizations have not received a solid ROI for this significant investment. With dynamic AI business platforms that require pre-filtered data sets to make informed and optimized decisions, let’s consider the possibilities that are achievable.

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Many organizations, including financial institutions, are experiencing large-scale attacks and even comprehensive dynamic controls with traditional information security solutions, experienced SecOps services, and MSSPs. Etc. The need for true auto-remediation powered by adaptive AI is a necessary condition to deal with growing cyber threats.

The foundation of the current and future web 3.0 and blockchain technologies is based on the ability of the new contract. Smart contracts and blockchain power will benefit rental cars, medical record and payment automation, and passport processing. Adaptive AI and machine learning are essential for this job stream.

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Many agree that adaptive AI will only work if enough data is processed. Organizations end up having to deal with the costs of data storage, replication, and energy before AI takes off.

In the Splunk example, this company will charge for the amount of data it will process and store, as it should! However, many organizations choose to only send certain log files to Splunk to reduce costs. Now, in the new world of blockchain and dynamic AI, organizations need to increase budgets to support data storage to make AI work as planned.

Some organizations are looking at adaptive AI as a replacement for human capital. AI will need to develop its own self-sustaining, creative, and creative abilities.

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Organizations will need trained data scientists and analytics resources until that day happens. In addition to computing, storage, cybersecurity, and development services, how will flexible AI become a cost asset for organizations?

As I said earlier, wait to see the statistics. Such as combating cybersecurity attacks with continuous monitoring, threat hunting, and incident response, blockchain, and adaptive AI will require similar disciplines. Organizations should view their cost model as an ongoing cost of operations and development until the promise of dynamic AI is realized.

Weighing the costs of compliance, cybersecurity, and risk, does adaptive AI pose a significant threat to an organization’s financial outlook?

That is for another time 🙂

All the best,



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