Special Feature
Part of a ZDNet Special Feature: Managing AI and ML in the Enterprise

The true costs and ROI of implementing AI in the enterprise

Leaders championing AI/ML initiatives need viable use cases and compelling metrics to advance their cause. Here’s how to approach cost justification, identify ROI, and avoid implementation missteps.

What it takes to make AI projects successful

In 2019, web content evaluator MarketMuse revealed that 80% of IT and corporate business leaders wanted to learn more about the cost of implementing existing artificial intelligence (AI) technology; 74% were interested in how much more it would cost over present expenditure levels to implement AI in their enterprises; and 69% wanted more information about how to measure the return on investment (ROI) for a new AI solution.

The picture is decidedly different in 2020.

Special Feature

Special Feature: Managing AI and ML in the Enterprise

This ebook, based on the latest ZDNet / TechRepublic special feature, advises CXOs on how to approach AI and ML initiatives, figure out where the data science team fits in, and what algorithms to buy versus build.

Read More

In 2020, most C-level executives have a working understanding of AI and have set their AI strategies. AI initiatives are being pushed down to the operational levels in their organizations, where middle management in business, IT, and data science are now expected to develop and implement AI for the business.

At the same time, there is still doubt, especially in the minds of implementers, about how well AI users and promoters in the end business understand the business cases where AI can be applied. In 2019, companies addressed this concern by running large numbers of AI pilot projects, with no particular expectations for the AI to be productive on the first project go-arounds.

In 2020, expectations from upper management are different. Now, AI projects are being moved into operations with high expectations for results.

Unfortunately, trepidation still remains that business users don't understand the best ways to put AI into productive use for the business -- and that ROI won't be realized.

Companies can minimize this risk by obtaining help from outside consultants, and AI vendors that have expertise in specific company verticals and business areas. There are also cases where AI vendors have taken the lead by pre-packaging AI/ML use cases toward specific industry verticals. One example is IBM Watson for healthcare, which is now a tried- and pre-packaged solution that hospitals and medical clinics can use to assist in medical diagnoses.

These pre-packaged solutions must still be tailored to company operations, but at least companies aren't starting from scratch on every project, and there is vendor-provided guidance that business users, IT, and data science can follow.

SEE: Third party vendor policy (TechRepublic Premium)

Justifying the investment

In 2019, many companies spent time developing ROI targets for AI projects in the project pilot phase. 

In 2020, many of these companies are tasked with implementing these projects in production to confirm that the original projected ROIs are on target.

Popular areas of AI implementations include: 

  • AI for the use of equipment failure prediction and maintenance cycles/scheduling. This furthers the goal of 24/7 operations without failures.
  • AI for automation underwriting and decision making for loans and policies in banking and insurance; as well as to provide early detection and prediction of fraud.
  • AI to assist in medical diagnosis.
  • AI for security breach and intrusion detection and prevention, and also in data center hardware, software, and environmental maintenance.
  • AI for the prediction of consumer patterns and prefaces for marketing and sales and for engineering product development.

A shift from hiring talent to retaining employees 

As part of operation's AI implementation efforts, companies are also making commitments to retrain internal technical and business staff to work with AI.

There are several reasons for this:

  1. Companies have had a difficult time finding or affording AI talent in the open market.
  2. As AI is embedded on the operational levels of organizations, business processes and how business is conducted are changing. Business users and IT staff members who already understand business and system processes are in the best positions to change them. 
  3. By engaging employees and committing to training, companies decrease the fear of AI that many employees have—such as endangerment of their employment.

The ROI deliverables from these actions are less tangible, but they can be significant. Among them: Investments in company people assets; savings of recruitment fees and high salaries for outside AI; and improved morale and employee retention because employees see their companies investing in them.

These elements should also be baked into ROI formulas as positive returns, but they are frequently missed. 

5 key takeaways

1. Company management now expects operations to begin showing the ROI from AI projects that they projected

Showing this ROI can be done by profiling a given business process benchmark against the same process with AI to show ROI efficiencies such as saved costs or hours, or more accurate diagnoses that can improve patient outcomes.

Companies can also improve ROI by leveraging the AI to improve other business processes. For example, if AI is used to maximize company logistics and transportation routes, it can potentially be applied to manufacturing and distribution to improve operational routings of products.

2. To successfully leverage ROI across systems processes and people and integration of processes, systems, and people are critical

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 an in-house highly customized or legacy system. When this happens, it is usually IT that must hand-code system interfaces.

If many different types of system interfaces are involved, there are also integration tools, and vendors that can simplify the task. 

3. AI projects are iterative and never ending

AI emulates the human mind, but at warp speed. It is only as good as the algorithms and data that are fed to it.

Organizations recognize these key risk factors. For this reason, more companies are investing into data cleaning and preparation. As AI gets placed into production, they are also evaluating whether AI will achieve the ROI projected for it when the AI only needs to achieve a level of 95% accuracy when compared with results that would have been attainable from a company or industry expert, or other authoritative source, without the assistance of AI.

As AI is installed in company operations and strategic forecasting, it remains to be seen whether a 5% chance for error will be sufficient for meeting ROI goals. For now, humans work alongside AI to also provide input and to make most final decisions (e.g., A surgeon or a radiologist interprets an MRI and also what the AI says about it, and then makes a decision regarding patient treatment.).

4. Infrastructure and accounting costs must be accounted for in the ROI

ROI is well on its way when the AI/ML reduces time to diagnosis, saves man-hours, and hopefully, reduces 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, etc. 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.

One way AI ROI risk of failure can be mitigated is by communicating total costs to management and keeping management informed.

Champions of AI projects should also get together with finance and determine long-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 revisions and integration costs -- and even extra manpower that might be needed to run the new technology. The goal is achieving an ROI that remains in the black over time and that builds on its value by continuing to enrich business processes and results.

5. Don't overlook other positive ROIs that AI can deliver

If companies invest in their employees as part of their AI initiatives, they have a better chance of retaining employees and of building the skills and capabilities of their human workforces. These areas should be included as positive returns on investment in ROI formulas, but often aren't.

Also see