Why the Math Around Adaptive AI is Painful

Artificial intelligence (AI) is expensive.

Companies that are cutting costs while investing in digital transformation to become stronger, leaner, and more profitable, I get the physics! Don’t look too deep. Artificial intelligence strategies are not designed to be cost-saving models.

Technology-savvy business models and machine learning combine the promise of processing, automation, responsiveness and speed; Many organizations use this ability to make effective, efficient, and ethical decisions. Okay, I hear you. Absolutely.

Adaptive AI business strategies work because organizations understand their data residing in the cloud, traditional SANs, LUNS, and S3 buckets in Databricks and Snowflake. If you count the data that resides in the DR, there is a lot of data. Data manipulation with AI and ML is old news. Many organizations have yet to realize a solid ROI for this critical investment. With adaptive AI business platforms that require more data sets before being processed to make logical and optimized decisions, we need to consider opportunities.

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Many organizations, including financial institutions, are dealing with major attacks even with extensive adaptive controls and traditional information security solutions, sophisticated SecOps resources, and MSSPs. Etc. The need for true auto-remediation powered by adaptive AI is a use case to address evolving cyber threats.

It is a cornerstone of web 3.0 and the future with blockchain strategies based on innovative contract strength. Smart contracts and blockchain capabilities could benefit car leasing, medical record and billing automation, and passport processing. Adaptive AI and machine learning are essential to this workflow.

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Most agree that adaptive AI can only be effective if there is enough data to process. The processes of paying for data storage, replication and capacity will end before the advent of AI.

In the Splunk model, this company pays for the amount of data they process and store, as needed! However, many organizations only send private log files to Splunk to reduce costs. Now, in the new world of blockchain and adaptive AI, organizations need to increase their budgets to support large amounts of data storage so that AI can work as planned.

Some organizations envision adaptive AI as a replacement for human capital. AI will need to ensure its self-healing, optimization, and innovation.

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Organizations need qualified data scientists and analytical resources to date. Adding to the math, storage, cybersecurity, and development resources, how can AI be adapted as a low-cost asset to organizations?

As I said at the beginning, wait until you look at the math. As combating cyber security attacks and continuous monitoring, threat detection, and incident response, blockchain, and adaptive AI require similar measures. Organizations need to consider their cost model as an operational and development expense until the promise of adaptive AI is realized.

Comparing the cost of compliance, cybersecurity, and risk, does adaptive AI pose a greater risk to an organization’s financial perspective?

For another time 🙂

All the best,



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