At the start of the 21st century, one of the greatest predictions in the tech-world had been surrounding the ‘Hype’ of machine learning. However, as of yet, it has yet to catch ‘fire,’ and many IT leaders and tech experts are confused about why machine learning isn’t living up to the hype it generated. An obsessive approach towards tasks has resulted in marginal returns on technological investments, which is why now it’s time that everyone started rethinking their approach to machine learning.
When Chief Information Officers (CIOs) think about their businesses and where they can deploy machine learning, the process generally begins with creating an inventory of tasks. This is where department leaders and CIOs must identify repeatable and routine processes that most humans can pass off to computers, where the IT teams and operations must set up targeted programs to ensure that those tasks are more efficient.
According to most senior CIOs at the biggest corporations, it is a piece-meal approach that has been adopted as a standard procedure in most organizations. That has resulted in leading CIOs down a path of marginal returns and limited innovation, surprisingly. Legendary CIO Paul Strassman has served as CIO at the Pentagon and was NASA’s CIO from 2001 to 2003.
He claimed on record that software should only be viewed as a storage place for knowledge and experience in any business, which is named as ‘knowledge capital’ by him. According to him, a new forklift shouldn’t get the same value as software.
A new forklift will work better and faster, but it doesn’t improve or learn with every use because it doesn’t learn the workflow of a business or how it’s work fits into the workflow of other machines. Therefore, an even better and faster forklift is bought to replace this new forklift. All the work put into the previous forklift is scrapped because the machine couldn’t retain knowledge.
Most businesses use enterprise technology in the same manner, by using it and replacing it with better technologies, instead of using it as a storage for knowledge capital that will get smarter and smarter. This is true for machine learning because it’s a tool that helps make tasks faster and efficient. Still, it’s not being used as a storage for knowledge capital but only for that task, instead of how that task and others fit together and can fit better together.
CIOs that are planning to revolutionize their organization through machine learning must start adopting the same thinking patterns as Strassman if they aim to truly capitalize on the potential of machine learning.
CIOs should be pushing to empower machines to learn more, learn better, and get ahead of tasks. That will require rethinking how machines analyze and process data. Companies shouldn’t only think of themselves as a collection of tasks but should start viewing their operations as being brought to life through data streams that will empower their workflows made of those tasks. These tasks are simply the muscles of the corporation, and data is going to be the blood flow of the nervous system.
Their focus should turn on how they must turn that data into useful information and unique insights horizontally across organizations, as that is how they will gain a competitive edge and expand their returns on investment when it comes to machine learning. Deploying smarter systems for how data will be interpreted and processed by machines will improve efficiency and accuracy because the goal is to move from one benefit to the other benefits.
Slow Pace of Adoption
Most CIOs are fighting a losing battle to convince stubborn business leaders about the best way to deploy machine-based intelligence in their organizations. The words ‘machine learning’ are thrown easily by tech marketers, but it’s difficult to back those words with high-quality and sustained results. Business leaders want to see real results and won’t be swayed by the hype in the 21st century.
A CFA Institute survey conducted recently revealed that in the financial world, about 10% of investment professionals were using machine learning. The rest relied on traditional desktop data tools and spreadsheets. Across other industries, about 50% of big corporations have adopted artificial intelligence strategies, and nearly 80% of corporations that have rolled out machine learning or artificial intelligence projects have reported stalled progress.
These reports clearly show that CIOs are going to continue struggling to modernize their businesses and show meaningful returns on investment if the effort remains task-oriented. If the social and economic systems that are in the framework remain subject to completion of tasks and place greater value on labor and its ability to complete those tasks, then AI and ML solutions will continue to be used and implemented as they are today. They will only be seen as cost-reducing enablers, and substitutes for humans instead of being viewed as partners of humans.
The big question should be: How are entire organizations going to take advantage of smarter data systems that are going to pervade across workflows? Especially if the people aren’t spending their time sorting and collecting data. So, what else can they do to add more value to their organizations?
That’s something that all CIOs and business leaders must work together to find the right solution. As of yet, the biggest hurdle to machine learning is the slow adoption of the technology by businesses and not trusting its complete potential. The future for machine learning and AI remains as bright as it ever was, but there is a hindrance from everyone to have complete faith in its capability and its ability to provide meaningful returns on investment.
by Bobby J Davidson
We love our company and we love what we do. Check out the ‘Why Percento‘ page to learn more: Love of Technology and Business! As the President of Percento Technologies International, I provide day-to-day leadership to the company’s senior management and I am personally involved in the strategy, business development and sales activities of the firm.
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