Artificial intelligence (AI) is expensive.
Companies cutting 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 to be a cost-saving model.
Adaptive artificial intelligence and machine learning business models combine the promise of processing, automation and response at a high speed; many organizations consider this capability to be a cost effective, optimized and rationalized decision. Okay, I feel you. Really.
Adaptive AI business strategies work as organizations make more sense of their data in the cloud, legacy SANs, LUNS, and S3 buckets within Databricks and Snowflake. If you count data sitting in DR, that’s a lot of data. Streamlining 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 available opportunities.
Many organizations, including financial institutions, receive volume attacks even with extensive adaptive controls using traditional information security solutions, experienced SecOps resources, and MSSPs. 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 based on innovative contract capability. Smart contracts and blockchain capability 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 enough data is processed. Organizations end up having to deal with the costs of data storage, replication and capacity before AI comes into play.
In the Splunk example, this company will pay for the amount of data they process and store, as they should! However, many organizations selectively send only specific log files to Splunk to reduce costs. Now, in the new world of blockchain and adaptive AI, organizations must increase their budgets to support the excessive data storage needed to make AI work as planned.
Some organizations are considering adaptive AI as a replacement for human capital. AI must program its self-healing, optimization and self-innovation capabilities.
Organizations need skilled data scientists and analytics resources until that day happens. Add in 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 see the math. Similar to combating cybersecurity attacks with continuous monitoring, threat hunting, and incident response, blockchain, and adaptive AI require similar disciplines. Organizations should consider their cost model as constant operational and development costs 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,