Why the Math Around Adaptive AI is Painful

Artificial intelligence (AI) is expensive.

Companies reduce costs while investing in digital transformations to become more agile, lean and profitable, I get the physics! Just don’t look too deep. Artificial intelligence strategy is not designed as a cost saving model.

Adaptive artificial intelligence and machine learning business models combine processes, automation and the promise of responsiveness with absolute speed; Many organizations consider this capability to be a cost-effective, adaptive and rational decision. Well, I think you. indeed

Adaptive AI business strategies work because organizations will make more sense of their data in Databricks and Snowflake sitting in cloud, legacy SAN, LUNS and S3 buckets. If you count the 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. Adaptive AI business platforms require more pre-rationalized data sets to make logical and optimized decisions, let’s consider accessible opportunities.

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Many organizations, including financial institutions, are experiencing volume attacks despite traditional information security measures, experienced SecOps resources, and extensive adaptive controls with MSSPs. etc. Dealing with growing cyber threats requires true self-healing powered by adaptive AI.

The cornerstone of current and future Web 3.0 and blockchain strategies is based on innovative contract capabilities. Smart contract and blockchain capabilities will benefit car leasing, medical record and billing automation, and passport processing. Adaptive AI and machine learning are important in this workflow.

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Many agree that adaptive AI will only be effective if enough data is processed. Organizations deal with data storage, replication and capacity costs before AI can come into play.

In Splunk’s example, this company will charge for as much data as they process and store, as they want! However, many organizations selectively send certain log files to Splunk for a lower cost. Now, in the new world of blockchain and adaptive AI, organizations need to stretch their budgets to support excessive data storage in order for AI to work as planned.

Some organizations consider adaptive AI to replace human capital. AI needs to program its self-healing, optimizing and self-innovating capabilities.

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Until that day, organizations will need qualified data scientists and analytics resources. By adding compute, storage, cybersecurity and development resources, how will adaptive AI be a cost-effective asset for organizations?

As I said at the beginning, wait to see the math. The same discipline will be required to combat cybersecurity attacks with continuous monitoring, threat hunting and incident response, blockchain and adaptive AI. Until the promise of adaptive AI is realized, organizations must consider their cost model for ongoing operation and development costs.

Balancing the costs of compliance, cybersecurity, and risk, is adaptive AI a major threat to an organization’s financial outlook?

That is for another time 🙂

All the best,



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