Why the Math Around Adaptive AI is Painful

Artificial intelligence (AI) is expensive.

Companies driving costs down while investing in digital transformation to become more agile, lean and profitable, I get the physics! Just don’t look too deep into it yet. Artificial intelligence strategies are not built on a cost savings model.

Adaptive artificial intelligence and machine learning business models combine the promise to process, automate and respond with sheer speed; Many organizations consider this capability a cost-effective, optimized and rationalized decision. Okay, I feel you. Indeed.

Adaptive AI business strategies work because organizations will make more sense of their data sitting in the cloud, sans legacy, LUNs, and S3 buckets inside Databricks and Snowflake. If you count data sitting in DR, that’s a lot of data. Rationalizing data through AI and ML is old news. Many organizations have yet to realize a solid ROI for this critical investment. With adaptive AI business platforms requiring more pre-rationalized data sets to make logical and optimized decisions, let’s consider the accessible opportunities.

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Many organizations, including financial institutions, overcome volume attacks even with extensive adaptive controls with traditional information security solutions, experienced SecOps resources and MSSPs. etc. The need for true auto-remediation powered by adaptive AI is a necessary use case to deal with the growing cyber threats.

A cornerstone of current and future Web 3.0 and blockchain strategies is based on innovative contract capability. Smart contracts and blockchain capability will benefit leasing cars, medical record and billing automation, and passport processing. Adaptive AI and machine learning are critical in this work stream.

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Most agree that adaptive AI will only be effective if enough data is processed. Organizations end up dealing with the costs of data storage, replication and capacity before AI comes into play.

In the Splunk example, the company will charge for the amount of data they will process and store, as they should! However, many organizations selectively only send specific log files to Splunk to lower costs. Now, in the new world of blockchain and adaptive AI, organizations need to increase their budgets to support the redundant data storage to make AI work as planned.

Some organizations are considering adaptive AI as a replacement for human capital. AI will need to program its capabilities for self-healing, optimizing and self-innovation.

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Organizations will need qualified data scientists and analytics resources until that day happens. Adding to the math, storage, cybersecurity and development resources, how will adaptive AI be a cost-marginal asset for organizations?

As I mentioned at the beginning, wait to look at the math. Similar to fighting cybersecurity attacks with continuous monitoring, threat hunting and incident response, blockchain and adaptive AI will require similar disciplines. Organizations should consider their costing model a continuous operation and development expense until the promise of adaptive AI comes true.

Balancing the costs of compliance, cybersecurity and risk, does adaptive AI pose a greater risk to the organization’s financial outlook?

That’s for another time 🙂

All the best,



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