A recent analysis of web topic popularity by web content evaluator MarketMuse revealed that 80 percent of IT and corporate business leaders want to learn more about the cost of implementing existing AI technology in an enterprise; 74 percent are interested in how much more it would cost over present expenditure levels to implement AI in their enterprises; and 69 percent want more information about how to measure return on investment (ROI) for a new AI solution.
Concerns about AI and machine learning (ML) costs and payoffs correlated with data I recently evaluated for Tech Pro Research. That research showed that a majority of organizations don't have a clear understanding of how AI/ML is going to help their businesses. Unsure of results, 64 percent of the survey respondents reported using pilot projects to test AI/ML concepts before proceeding into full implementations.
The takeaways are clear. In fact, they are all too familiar whenever companies deal with emerging technologies. Just as cloud technology presented its share of uncertainties when it was first being deployed, so AI and ML are generating similar heartburn as companies cautiously move forward.
The causes for this anxiety are easy to understand. Many organizational influencers still don't know enough about AI/ML and how these newer technologies can pay off for their businesses. They are uncertain when they step into strategic meetings and budget discussions. They are asking themselves, "How far can I push for these promising new technologies when I lack empirical, first-hand knowledge about their pros and cons -- and about the investment paybacks that management will surely ask me for?"
In short, AI champions, whether they come from IT or the end business, want affirmation in two principal areas:
- How can I present an impactive business case for AI/ML?
- How can I ensure that there will be an acceptable return on investment and understanding of ongoing costs for any recommendation I might make?
SEE: Free machine learning courses from Google, Amazon, and Microsoft: What do they offer? (Tech Pro Research)
Developing the business case
According to the TechRepublic research, 53 percent of companies interviewed reported that they don't have a clear understanding of how AI or ML could benefit their businesses.
This is a red flag area, where vendors and industry consultants with experience both in AI/ML and in specific industry verticals can help.
Consultants can work alongside corporate IT and business managers, helping them identify sound business use cases where AI and ML can be put to work and pay off.
AI and ML vendors can help by prepackaging AI/ML uses cases that are purposed toward specific industry verticals. One example is IBM Watson for healthcare, which is now a 'tried' and prepackaged solution that hospitals and medical clinics can use to assist in medical diagnoses.
Even with prepackaged and tried solutions, however, it is currently company best practice to trial these systems with a preliminary pilot project that can 1) show that the solution will deliver what the company thinks it will and 2) show promise that it will deliver a return on money and effort investments.
An AI/ML pilot project is important as a technology proof of concept that could justify increased spending. It is equally important as a vehicle that can build confidence and experience with AI in both IT and the end business.
SEE: The impact of machine learning on IT and your career (free TechRepublic PDF)
Justifying the investment
Once business use cases are identified and trialed, the task of identifying an ROI and funding the costs of a broader implementation of AI/ML begins.
A common method IT departments employ for calculating ROI for an IT project is assessing how much time and money a system improvement will subtract from a business process. For example, if you're investing in virtual servers to replace physical servers in the data center, as most companies did 10 years ago, it's relatively straightforward to calculate your upfront costs in new virtualization software and equipment and then compare this against the floorspace, energy, and physical server investments you're saving.
With AI and ML, determining an ROI isn't that simple.
Most commonly, AI and ML can be used to achieve manpower savings because they can automate portions of operational and decision-making processes -- but they seldom automate or economize all parts of an end-to-end business workflow.
Why is this important?
Because promoters of AI and ML will be expected to provide an ROI that their companies will see on the bottom line. This means that the entire business workflow, not just part of it, must deliver tangible bottom-line value.
For instance, if you automate packaging on an assembly line, reducing time and waste, but all of your other end-to-end processes are unaffected and continue to throttle the workflow, the ROI visibility of your AI/ML insertion and its ROI delivery will be lost.
If you're piloting AI/ML for a single process in an entire chain of end-to-end business processes, ensure that the AI/ML you're using can also be leveraged for value to these other business processes so you can make a total impact on the business without bottlenecks. And as part of this effort, if you are first documenting an ROI gain for a single business process, be sure to structure that ROI around that single business process only, so company expectations are properly set.
Understanding (and factoring in) the costs for a true ROI
At its simplest, an ROI formula benchmarks a current process against a revised process that uses AI and/ or ML. So, if you're using AI/ML for purposes of medical diagnosis, suddenly you have compute power and predictive algorithms that enable the digestion of thousands of pages of medical data in seconds, resulting in a rapid diagnosis of a patient's medical condition that a medical specialist then reviews and assesses. The desired outcomes you measure for are speed to diagnosis, reduction in man-hours, and improved accuracy of results. If these business metrics are achieved, ROI is well on its way because the AI/ML have reduced time to diagnosis, saved man-hours, and hopefully have reduced margins for error.
Unfortunately, this initial ROI doesn't factor in the cost of obtaining more compute power, storage and so on, to support the new solution. Nor does it include time for restructuring business processes, revising surrounding systems, integrating these disparate systems with the new AI platform, training IT and end business users, consumption of energy and data center costs, initial implementation costs, licensing, etcetera. These setup and ongoing support costs must also be factored into the ROI equation to ensure that you are still achieving positive ROI results over time.
Achieving an initially attractive ROI in a pilot project by reducing time of operations or improving revenue potential is not a strong enough ROI result to move forward with. The champion of an AI/ML project should get together with finance and determine longer-term ROI projections over a period of several years. These long-term projections should take into account every corporate asset that is required to run the AI/ML, such as new equipment/software, cloud costs, energy and data center costs, training costs, system and business process revision and integration costs -- and even extra manpower that might be needed to run the new technology. The goal should be achieving an ROI that remains in the black over time and that builds on its value by continuing to enrich business processes and results.
SEE: How to differentiate between AI, machine learning, and deep learning (TechRepublic)
Avoiding the ROI sand traps
Another key to producing a credible ROI formula that can operate over the long term for AI/ML is to recognize the potential cost sand traps that can threaten your ROI. Here are several typical ones:
AI/ML systems don't operate in a vacuum. Vendors know this, and many will tell you that their systems have a complete set of APIs that interoperate with all systems. This works until the AI must work with a highly customized or legacy system you have in-house. When this happens, it is usually IT that must hand-code system interfaces.
This costs time and money, and both can destroy your ROI.
AI gone wrong
Because AI depends upon computers emulating the human mind, and because ML is a subset of AI that strives to continue learning from repetitive pattern recognition in the same way that the human mind learns, computers -- like the human mind -- can misinterpret.
One case in point: Symrise, a major global fragrance company in Germany, used AI to produce new perfumes for Brazil's Millennial market. These perfumes boosted revenues and global reach. But Symrise executive Anton Daub said it took almost two years to get to this point. Those two years were spent in intensive training of the AI system by Symrise's perfumers and included costly IT upgrades to connect the company's disparate data to the AI.
Because AI systems need to be continually recalibrated and trained, there will always be a 'venture' element in any AI project -- because the human mind (and emulating it) can be unpredictable. This uncertainty must be planned for in any AI ROI formula. One step AI promoters can take is to educate upper management of the risks so that these risks can be planned for and managed. A second step is to factor risk into the ROI formula by adding a 20 percent cushion to your AI project cost projections as a margin for the unknown and the unexpected.
New system and business processes
Introducing AI in your company is going to impact systems and business processes. Minimally, systems that need to communicate and exchange information with your AI must be integrated with the AI. System processes will by necessity be modified. As the AI is rolled out to your business operations, processes that formerly were performed by humans will be undertaken by the AI -- causing the displaced humans to enter into new and/or revised job roles. This business process revision will need be planned for, trained for, and accounted for in your ROI formula.
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