Artificial Intelligence Return On Investment

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The Roi Of Artificial Intelligence For Development

Delivering high-value insights and strategy using AI: return on AI investments

Doing the math to prove the value

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Artificial Intelligence is fuelling a revolution in the software development world. This eBook is aimed at those who are considering implementing AI into their developer stack.

Artificial Intelligence can bring deadlines back on track, make significant changes to legacy code and decrease time to market for new or iterated code, freeing up valuable coder time to develop new features, instead of writing tests.

Read the eBook to understand how leveraging AI can help improve your return on investment not just in the cost of the technology, but also as an enhancement to your developer productivity and the agility of your development cycle.

Get Better Insights Via Data Analysis

The second commonly used AI project is leveraging AI algorithms to detect data patterns and interpret what it means. You can use this machine learning app to:

Predict the buying habit of a customer

Identify real-time credit and insurance claims fraud

Analyse safety and quality problems in automobiles and manufactured goods

Provide your insurers with accurate actuarial modelling

Automate digital ads as per personal targets

Cognitive insights provided by machine learning have a different level of proficiency. These models can help you predict or categorise things.

Technologies such as deep learning can mimic the human brain when you want to recognise patterns, speech, and images. While curating data is known to be labour-intensive all throughout history, machine learning can help you understand the most suitable matches when the data belongs to the same person or company but in different formats. All these can lead to an increase in ROI invariably.

Challenges Calculating The Roi Of Ai

Its easier to understand why cost-benefit analyses are more complicated for AI/ML projects if we start at the beginning with the classic return on investment formula:

ROI: / investment

Calculating ROI is one way that companies measure the value of their invested capital. The formula is powerful because it can be used across a wide range of industries and projects, including those involving digital operations.

With AI/ML, calculating ROI requires more thought.

AI/ML projects are experimental by nature, which makes it nearly impossible to plug clean gains and investment numbers into the formula above.

You have to define gains carefully, as there are many ways you can interpret this metric. For instance, gains could represent:

  • Increased productivity
  • More business per customer

The list goes on and on.

So, you have to make sure you define your gains with as much specificity as possible in terms of your unique business model. Otherwise, you wont be able to quantify or qualify success.

The investment variable can be even harder to nail down. Analysts tend to think solely in terms of the number of dollars spent, but there are other factors to consider, such as:

To put things simply, back-of-the-envelope math wont cut it for AI/ML projects. Your cost-benefit analysis should break down both the gains and investment variables into smaller, measurable units that are relevant to your business.

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Define Needs Business Value And Set Up The Goals

Every business has its own characteristics and needs. Thats why the implementation of machine learning should be tailor-made. Find your pain points and define a business case for AI at your organization. You should know what the goal of this implementation is and what your KPIs will be. Also, a method of measurement should be precise.

What Do We Mean By Roi For Ai

Return On Artificial Intelligence: The Challenge And The ...

Most people probably think they know what AI is and does, but its a term that encompasses many technologies, processes and functions, so its difficult to pin down. Its definitely not a one-size-fits all field. That can make it challenging to determine a return on investment.

In its simplest form, ROI is a financial ratio of an investments gain or loss relative to its cost. In other words, when you invest in AI, the benefits of your investment should outweigh the costs.

Normally, the cost is incurred in the present or the near future, while the benefits accrue at some nonspecific point in the future. However, the uncertain timing around benefit accrual is greater than the uncertainty around the expenditures timing. So, be sure your ROI calculation accounts for both the time value of the money invested and the uncertainty of the benefits.

That is a standard textbook definition of ROI in financial terms, known as hard ROI.

Soft ROI looks at a broader set of benefits, including employee satisfaction and retention, skills acquisition, brand enhancement and a higher valuation of the company.

In the case of AI, the hard ROI you achieve can come from a number of different sources:

In addition to these hard returns, AI can provide a number of soft returns. They include:

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Measuring Roi For Ai Efforts

Calculating the Return on Investment on data science, machine learning, or AI projects is often critical to secure resources however, these calculations can be notoriously challenging to figure out.

Data ROI calculations are especially challenging since data efforts empower so many aspects of an organization to operate more effectively and efficiently. Its often difficult to isolate the contribution of data alone to improvements, especially larger business outcomes . While data teams cant take sole credit for organizational wins like this, finding concrete wins is often the easiest way to calculate ROI.

McKinsey estimates that analytics will potentially unlock $9.5 trillion to $15.4 trillion in value annually, with AI activating about 40 percent of that

By 2024 50% Of Ai Investments Will Be Quantified And Linked To Specific Key Performance Indicators To Measure Return On Investment

The first barrier is skills. Business and IT leaders acknowledge that AI will change the skills needed to accomplish AI jobs. Fifty-six percent of respondents said that acquiring new skills will be required to do both existing and newly created jobs, according to a Gartner Research Circle survey. Today, AI can evaluate X-rays like human radiologists. As this technology advances beyond research settings, radiologists will shift their focus to consulting with other physicians on diagnosis and treatment, treating diseases, performing image-guided medical interventions and discussing procedures and results with patients.

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Accounting For Unexpected Value

One final, but important, point is that AI can deliver unexpected, additional insights. In other words, AI can deliver valuable results in addition to achieving the primary objective of the project. When that happens, those insights should be included in the assessment of the projects value. Planned or not, they are part of the projects outputs. For this reason, you should be clear with your stakeholders that the exploratory nature of AI means you may be left with, or led to, additional or alternative results that may be no less valuable than your primary targets.

In Five To Seven Years Ai Will Be A Critical Game

Investing With Artificial Intelligence

-Abhinav Singhal, Chief Strategy Officer, Asia Pacific, thyssenkrupp

ESI ThoughtLab conducted a comprehensive benchmarking survey of executives at 1,200 companies across 12 industries and 15 countries. It was carried out over the phone in March and April of 2020.

Respondents had superior or excellent knowledge of the use of AI within their organizations. A full 85% were C-level executives, and the rest reported directly into the C-suite.

The survey examined AI investments, plans, practices, and performance results at a wide array of firms. It included quantitative questions to allow ESI ThoughtLab economists to develop a robust AI maturity framework, analyze performance results, benchmark practices, and measure the ROI on AI investments.

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Computer Vision In Construction World

CV is extremely useful to help our construction industry enter the digital world. It provides high accuracy verification tools which automatically monitor the construction sites and increase our productivity. It is believed that the investment in CV will deliver exponential returns despite the high investment cost.

Safety Measures Detection / Construction Safety Management

You must know around 10% of ignition and warehouse industries.

On average, over 228K will be spent on one fall accident, including replacement, project delay, compensation cost. And over 341K will cost on each electric accident.

This leads to the presence of smart safety applications in order to help us identify objects in a 3D environment. Workers who haven’t worn proper safety measures like helmets and high visibility jackets will be detected. viAct is one of the AI companies aiming at reducing the injury rate in construction areas by using computer vision.

The followings are the potential return on investment on CV

  • Reduce accidents thus increase safety

  • Reduce 80% compensation cost for injured workers

  • Intricately precise and faster inspections monitoring by machines than human

  • Reduce the time for process

  • Reduce expense of man-hours

  • Lower operation cost

  • Reduce human workloads, thus can free up human capital for other tasks to increase the throughput

Activity Tracking

Help Machinery Counting and Material Tracking

The Roi Of Complying With Data Privacy Regulations

A much more difficult risk reduction-related ROI to measure is how much more or less compliant a company is with regulations before and after adopting AI software. Data privacy laws like GDPR and CCPA require that large enterprises remove customer data from their systems upon customer request. But companies may not be able to access all of their customer data.

In order to fulfill data privacy requests from customers, they may need to digitize some of the data they have stored in paper documents, such as past loan files.

A document digitization software may help with this, and an enterprise search and discovery software may allow companies to then search for customer data that exists in multiple digital locations like an intranet or the cloud. Companies may not be able to gauge the ROI of these software beyond being able to access their data when they werent before.

They could measure the time it takes employees to provide documents to auditors or fulfill customer requests to have their data deleted before adopting the software and then compare it to the time it takes employees to provide documents and delete data after adopting the software.

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Assessing The Future Value Of Ai

When we think what AI can do to the business, it mostly comes from multiple well-marketed examples, coming from well-defined areas. Like, the best chess player getting beaten by Artificial Intelligence. But problems like this, have a definitive endpoint, like in chess. However, most problems in Fortune 500 companies do not have a definite outcome. Problem statements that these companies deal with are, for example, will the next product launched, be successful? But whats the definition of success here? Similarly, another example can be, how to improve customer experience in banking or similar sectors?

Most of these problems are vague and complex, and the outcomes defined in multiple ways. So, the challenge comes in quantifying the outcome on investment in something where the outcome itself is vague. To answer this question, the most important thing to ask is whether we truly understand the business problem that we want to solve. In other words, is the business question well framed?

Run Proof Of Concept It Will Help To Collect More Information For Future Roi Measurement

Study warns of compliance costs for regulating Artificial ...

According to Wikipedia, “Proof of concept or proof of principle is a realization of a certain method or idea in order to demonstrate its feasibility or a demonstration in principle with the aim of verifying that some concept or theory has practical potential. A proof of concept is usually small and may or may not be complete.”

POCs are timed-boxed , with clear KPIs for measuring your results. This exercise keeps costs low and provides rapid insights into what results can be expected before investing significant resources into the project.

Proof of concept can demonstrate a project’s ROI potential just as well as it proves its technical capability.

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The Challenges With Measuring Roi

There are two major roadblocks to quantifying business value from advanced analytics:

  • Unclear business outcomes: The outcomes from analytics initiatives are often fuzzy for example, improved operations efficiency, better employee morale, or higher brand value. Can you measure them objectively? Any close estimates that you arrive at could appear questionable.
  • Difficulty in attributing results: When you notice measurable improvements in outcomes, can you be sure that it was your data-driven decisions that moved the needle? For example, Disney+ launched in November 2019. It acquired 95 million subscribers, beating its four-year goal in 14 months. Was this due to a brilliant marketing strategy, or was it just a pandemic windfall?

Thankfully, there are ways to measure the outcomes of analytics initiatives and perform attribution scientifically.

Ai Investment To Increase But Challenges Remain Around Delivering Roi

Two-thirds of senior executives across industries and nearly nine out of ten leaders from the worlds largest enterprises believe AI is vitally important for the future of their businesses and will be increasing AI investment in the post-pandemic era.

However, significant challenges remain on delivering ROI from AI investment.

An ESI ThoughtLab study of 1,200 organisations has revealed that companies are generating on average an ROI of only 1.3%, while 40% of AI projects are not yet profitable.

The reason for this, according to the research, is that AI initiatives require time, expertise and scale to deliver on their promise of high returns.

With the pandemic speeding up the need for quick data-driven decision-making, companies should act now to develop the skills, platforms, and processes that can enable them to achieve the full strategic, operational, and financial benefits from AI.

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A Busy Market For Private Equity

Having raised an exceptional sum of capital totalling USD 3 trillion over the last five years,3 private equity managers are struggling to find suitably priced investments, according to panelists speaking at Super Return in Berlin. The glut of capital is prompting those firms, which are actually making investments, to acquire companies at more than 10 times or even 13 times EBITDA .

Buy-outs at such high multiples are not sustainable over the long term and some private equity managers are responding by looking to make more investments into new or niche technology companies. Despite the potential of AI, very few private equity and venture capital managers have invested in companies developing such technology.

Summary: Return On Investment For Machine Learning

HPE CEO Sees Return on $4B Investment in Two Years

Machine learning deals with probabilities, which means theres always a chance for mistakes. This inherent uncertainty causes many decision makers feel uncomfortable with implementing machine

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Eljas Linna

Machine learning deals with probabilities, which means theres always a chance for mistakes. This inherent uncertainty causes many decision makers feel uncomfortable with implementing machine learning and traps them in an endless chase for the magical 100% accuracy. The fear of mistakes nearly always pops up when Im working with companies taking their first steps towards intelligent automation, and I get asked What if the algorithm makes a wrong prediction?

If this issue is not addressed, the company will very likely spend a hefty amount of resources and years of development time on machine learning without ever getting returns for their investment. In this article, Ill show you the simple equation I use to relieve these concerns and get decision makers more comfortable with the uncertainty.

Read the complete article at:towardsdatascience.com

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The Roi Of Chatbots For Customer Service

If a company can measure what percentage of new messages from customers can be handled by the conversational interface, then it may be able to determine how much fewer call center resources have been required to handle customers concerns since implementing a chatbot.

Another example of measurable efficiency ROI is proper support ticket routing. When a new call or text comes in, a company could adopt a machine learning system that directs the call or text to the right person. The right person to handle a refund is different than the right person to handle a product inquiry. The latter person might be in sales, for example.

The ROI of a conversational interface differs depending on the specific use-case. When a company adopts a conversational interface, it wont be measuring its ROI, but the ROI of the discreet use-case in which it is being applied.

Return On Artificial Intelligence: The Challenge And The Opportunity

Moving up the charts with AI

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There is increasing awareness that the greatest problems with artificial intelligence are not primarily technical, but rather how to achieve value from the technology. This was a growing problem even in the booming economy of the last several years, but a much more important issue in the current pandemic-driven recessionary economic climate.

Older AI technologies like natural language processing, and newer ones like deep learning, work well for the most part and are capable of providing considerable value to organizations that implement them. The challenges are with large-scale implementation and deployment of AI, which are necessary to achieve value. There is substantial evidence of this in surveys.

In an MIT Sloan Management Review/BCG survey, seven out of 10 companies surveyed report minimal or no impact from AI so far. Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 report business gains from AI in the past three years. This number improves to 3 out of 5 when we include companies that have made significant investments in AI. Even so, this means 40% of organizations making significant investments in AI do not report business gains from AI.

Deloitte 2018 State of Enterprise AI surveyThe top 3 challenges with AI were implementation issues, integrating AI into the companys roles and functions, and data issuesall factors involved in large-scale deployment.

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