Why the Math Around Adaptive AI is Painful

Artificial intelligence (AI) is expensive.

Companies are lowering costs while investing in digital transformations 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 being a cost saving 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. Really

Adaptive AI business strategies work because organizations will make more sense of their data sitting in the cloud, legacy SANs, LUNS and S3 buckets in Databricks and Snowflake. If you count data sitting in DR, that’s a lot of data. Rationalizing data with AI and ML is old news. Many organizations have yet to realize a solid ROI for this critical investment. With adaptive AI trading 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, receive volume attacks even with comprehensive adaptive controls with traditional information security solutions, experienced SecOps resources and MSSPs. Etc. The need for true auto-healing powered by adaptive AI is a required use case to address the growing cyber threats.

A cornerstone of current and future network 3.0 and blockchain strategies is based on innovative contract capability. Smart contracts and blockchain capability will benefit car rental, medical record and billing automation and passport processing. Adaptive AI and machine learning are critical in 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 the cost of data storage, replication and capacity before AI takes off.

In the Splunk example, this company will pay for the amount of data they will process and store, as they should! However, many organizations selectively only send specific logs to Splunk to keep costs down. Now, in the new world of blockchain and adaptive AI, organizations must increase their budgets to support the excess data storage for AI to work as planned.

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

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Organizations will need skilled 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-margin asset for organizations?

As I mentioned in the beginning, expect 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 cost model a constant operation and development expense until the promise of adaptive AI is realized.

Balancing the cost of compliance, cybersecurity and risk, is adaptive AI a greater risk to the organization’s financial outlook?

That’s for another time 🙂

all the best,



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