Why the Math Around Adaptive AI is Painful

Artificial intelligence (AI) is expensive.

Companies driving costs down while investing in digital transformations to be more agile, leaner and more profitable, I get the physics! Don’t look too deep into it yet. Artificial intelligence strategies are not built on being a cost saving model.

Adaptive artificial intelligence and machine learning business models combine the promise of processing, automating, and responding with great velocity; Many organizations consider this to be a cost-effective, optimized and streamlined decision. Okay, I think you. Seriously.

Adaptive AI business strategies work because organizations will make more sense of their data sitting in the cloud, legacy SANs, LUNS, and S3 buckets inside Databricks and Snowflake. If you count data sitting in DR, that’s a lot of data. Streamlining data through AI and ML is an old story. Many organizations have yet to achieve a strong ROI for this critical investment. With adaptive AI business platforms that require more pre-reasoned data sets to make logical and optimized decisions, let’s consider the accessible opportunities.

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Many organizations, including financial institutions, are receiving volume attacks even with extensive adaptive controls with traditional information security solutions, experienced SecOps resources, and MSSPs. Etc. It is an essential use case to deal with the growing cyber threats, that true automatic optimization driven by adaptive AI is required.

A cornerstone of current and future web 3.0 and blockchain strategies is based on innovative contract capabilities. Smart contracts and blockchain capabilities will benefit leasing cars, medical records and billing automation, and passport processing. Adaptive AI and machine learning are key to this workflow.

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Most agree that adaptive AI will only be effective if sufficient data is processed. Organizations end up dealing with the cost of data storage, replication and capacity before implementing AI.

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

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

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Organizations will need qualified data scientists and analytical 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. Like combating cybersecurity attacks with continuous monitoring, threat hunting, and incident response, blockchain will require similar disciplines, and adaptive AI. Organizations should think of their cost model as an ongoing cost of operation and development until the promise of adaptive AI materializes.

Balancing the cost of compliance, cyber security and risk, is adaptive AI more of a risk to the organization’s financial outlook?

That’s it for another time 🙂

Good luck,



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