Artificial intelligence (AI) is expensive.
Companies are 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 based on a cost savings model.
Adaptive artificial intelligence and machine learning combine business models with a promise to process, automate, and respond with speed; many organizations value this ability to make cost-effective, optimized and rationalized decisions. Well, I feel you. Really
AI-adapted business strategies are working as organizations make more sense of their data sitting in the cloud, legacy SANs, LUNS, and S3 buckets within Databanks and Snowflake. If you count the data sitting in the DR, that’s a lot of data. Data mining through AI and ML is old news. Many organizations have yet to realize a solid ROI for this critical investment. With adaptive AI, trading platforms that search for more predictable data to make logical and optimized decisions, assess accessible opportunities.
Many organizations, including financial institutions, are planning volume attacks even with extensive adaptive controls with traditional information security solutions, expert SecOps capabilities, and MSSPs. The need for true self-remediation by adaptive AI has become a necessary use case to deal with growing cyber threats.
The cornerstone of current and future Internet 3.0 and blockchain strategies is based on contracted airline capacity. Smart contracts and closings will enable rental cars, medical record and billing automation, and passport processing. Adaptive AI and machine learning are critical in this work.
Most agree that adaptive AI is only effective if sufficient data is processed. Organizations are dealing with the cost of data storage, replication and capacity before joining AI.
In the example of Splunk, this company will charge for the amount of data processed and the amount, as appropriate! However, many organizations will only send select Splunk log files at a lower cost. Now, in the new world of blockchains and adaptive AI, organizations must scale up their systems to support the massive amounts of data stored in order to complete their AI work.
Some organizations consider adaptive AI to be the replacement of human capital. AI will need to exhibit the ability of self-healing, optimizing, and self-innovation.
Organizations will need skilled scientific and analytical resources until that day. In addition to computing, storage, cybersecurity, and development capabilities, how will adaptive AI be a constant asset for marginal organizations?
As I mentioned at the beginning, expect to see the math. Combating similar cybersecurity with continuous monitoring, threat hunting, and incident response, interference, and adaptive AI will require similar training. Organizations must consider their operating model a constant cost of development until the promise of adaptive AI is realized.
Balancing the cost of compliance, cybersecurity and risk, is adaptive AI a bigger risk to an organization’s financial perspective?
That is for another time
all the best;