Why the Math Around Adaptive AI is Painful

Artificial intelligence (AI) is expensive.

Companies cutting costs while investing in digital transformation to become more agile, cost-effective and profitable, I understand physics! Just don’t look too deep into it yet. AI strategies are not based on a cost savings model.

Adaptive AI and machine learning business models combine the promise of processing, automating and reacting at breakneck speed; many organizations see this option as a cost-effective, optimized and streamlined decision. Okay, I feel you. Really.

Adaptive AI business strategies are working as organizations gain greater understanding of their data in the cloud, legacy SANs, LUNS and S3 segments in Databricks and Snowflake. If you count the data that is in the DR, that’s a lot of data. Streamlining data using AI and ML is old news. Many organizations have yet to realize a solid ROI on this important investment. Considering adaptive AI business platforms that require pre-rationalized datasets to make logical and optimized decisions, let’s consider the options available.

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Many organizations, including financial institutions, receive high-volume attacks even with extensive adaptive controls in place with traditional information security solutions, experienced SecOps resources, and MSSPs. Etc. The need for true automatic remediation powered by adaptive AI is a necessary use case to combat the growing cyber threats.

The cornerstone of current and future web 3.0 and blockchain strategies is based on the innovative contract capability. Smart contracts and blockchain capabilities will benefit car leasing, medical record and billing automation, and passport processing. Adaptive AI and machine learning are critical to this workflow.

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Most agree that adaptive AI will only be effective if enough data is processed. Organizations end up dealing with data storage, replication and capacity costs 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 selectively send only specific log files to Splunk to reduce costs. Now, in the new world of blockchain and adaptive AI, organizations must increase their budgets to support excessive data storage for AI to function as intended.

Some organizations see adaptive AI as a substitute for human capital. AI will need to program its own self-healing, optimization and self-innovation capabilities.

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To this day, organizations will need skilled data scientists and analytics resources. Adding math, storage, cybersecurity, and development resources, how will adaptive AI be the cost-margin for organizations?

As I mentioned at the beginning, wait to see the math. Similar to combating cybersecurity attacks with continuous monitoring, threat hunting, and incident response, blockchain and adaptive AI will require similar disciplines. Organizations should view their costing model as an ongoing operating and development expense until the promise of adaptive AI is realized.

Balancing compliance, cybersecurity and risk costs, is adaptive AI a greater risk to an organization’s financial outlook?

That for another time 🙂

All the best,



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