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Artificial intelligence (AI) is expensive.
Companies driving costs down 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 costing savings model.
Adaptive artificial intelligence and machine learning business models combine the promise to process, automation, and respond with sheer velocity; 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 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.
Many organizations, including financial institutions, are getting 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 needed 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.
Most agree that adaptive AI will only be effective if sufficient data is processed. Organizations finish dealing with the cost of data storage, replication, and capacity before AI comes into play.
In the Splunk example, this company will charge for the amount of the data they will process and store, as they should! Yet, 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 excessive data storage to make AI work as planned.
Some organizations consider adaptive AI as a replacement for human capital. AI will need to program its self-healing, optimizing, and self-innovation capabilities.
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 to organizations?
As I mentioned in 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 constant operation and development expense until the promise of adaptive AI comes true.
Balancing the cost of compliance, cybersecurity, and risk, is adaptive AI a greater risk to the organization’s financial outlook?
That is for another time 🙂
All the best,
John
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