How To Prove ROI From AI

Your use of AI is probably succeeding in countless ways; however, AI has the potential to fail you, and in a big way: by sealing down the fate of your business and career. In fact, you might not even be able to prove that AI is driving you or your stakeholders to profit at all. Failures in the world of AI today can be small or enormous. Take for example IBMs Watson for Oncology. The initiative had to be cancelled after $62 million in spending lead to unsafe treatment recommendations.

According to Venturebeat, an estimated 87% of data science projects never make it to the production stage, and TechRepublic claims that 56% of global CEOs expect it to take 3-5 years to see any real ROI on their AI investment. Long story short: you are not alone in your quest for returns. Nevermind this fact, you can take solace in the reality that you want to be a leader, not a laggard, and you need to be the one who can prove that your use of AI is contributing to ROI via expansion and growth.

Start Back at the Beginning

AI has taken over nearly every facet of business in the 21st century. Every major player in every industry has AI at the root of nearly every project. In retail, Domino's Pizza has used AI to reduce and more accurately predict delivery time from 75% to 95% accuracy.

In Mining, there are companies in Australia using autonomous trucks and drilling technology to cut mining costs, improve worker safety and boost productivity by 20%. They also predict that 77% of jobs in the countrys mining sector will be altered by technological innovations, increasing productivity by up to 23%.

Then in banking, Barclays is using AI to detect and prevent fraud. Barclays is also using similar tech to improve customer experience through chatbots, leveraging the vast amount of data they have accumulated. However, Barclays still faces challenges. They struggle with implementation of faster payment options for their customers.

Challenges That Youll Face

You will have to conquer several hills on your journey to return on AI investments. One challenge has to do with the American A.I initiative. Even though this policy implementation is a good start, we are still behind some of our global competitors when it comes to direct government funding of AI. Therefore, if you dont have the capital to internally implement, monitor and optimize your AI, you will have to seek out funding.

You will also need a well-planned and executed initiative for retraining, reskilling and repurposing your employees. A recent study by McKinsey predicts that in the U.S. up to 33.3% of the 2030 workforce may need to learn new skills and find new work. By now, you have already spent countless hours and substantial revenue on recruiting, hiring, training and building your team and company culture. Don't allow that money to go to waste by allowing your workforce to become irrelevant. Invest more in your people now to save your business in the long run.

In addition, your access to data and your use of it is critical. The AI you implement is only as good as the fuel you give it. And that fuel is data. The Pistoia Alliance released a survey in 2019 that showed 52% of respondents cited insufficient access to data as one of biggest barriers to the adoption of AI.

How can you mirror the success of the previously mentioned players? In order to replicate their triumphs, you must start by asking yourself variations of the following questions:

What are your specific business goals or challenges that youre looking to address with AI solutions?

Buying AI is not buying a one-size-fits-all, off-the-shelf solution for your business. Business leaders must treat AI like any other technology investment: it should have a specific purpose to solve a specific goal. It must be tracked with benchmarks and KPIs. You must then hold yourself and your teams accountable for those numbers.

Is this the right technology to solve your business problem?

Its important that an organization approaches AI from the starting point of: What problem do we need to solve? Rather than, Lets do something with AI. And it should be the right problem for which AI can have a substantial impact. Many companies have not answered basic questions on what business problems can be addressed with AI, which leads to unrealistic expectations.

Do you have internal expertise to maintain AI integration, and a team committed to training and improving the technology across your organization?

How are companies creating a people-focused practice around the operationalization of AI on a company-wide scale? Some have applied to AI teams. Some have virtual teams where two days out of the week, data scientists are embedded with the operations team (this is analogous to DBAs who train non-technical colleagues to understand databases role within company operations). Breaking down organizational silos, and allowing various groups to interact and collaborate, is a critical enabler of an AI project.

How will you measure the success of an AI deployment?

You create your own AI KPIs and maintain a functioning knowledge of how you will measure them prior to deployment. There should be no guesswork or maybes involved in the process. If you want to prove returns, you need concrete benchmarks to get you there.

Now what? Youve worked diligently and answered all of the questions. Youre ready for implementation, but how do you execute? There are multiple factors and things to keep in the back of your mind in order to ensure your deployment is a success.

Growth and Expansion is Greater Than Savings

While AI has the potential to cut expenses, the primary focus should be on growth and expansion in order to maximize outcomes. This includes innovation in products and services, efficiency for productivity and gaining market share. AI is optimized when it is adopted at every level of technology, from value chains to pricing, and when understanding the AI-related preferences of customers. Your best bet is to stay focused on growth by innovating new products and fine-tuning your business model. To better capitalize on the technological benefits of AI, stay on the offense.

Investment is Needed in Both Human Resources and Technology: You cannot fully benefit from AI in technology if your employees arent prepared. Consider that 69% of enterprises are facing a moderate, major or extreme skills gap when it comes to AI. Management and staff must be educated and trained in cross-functional teams in all processes and operations. Finding the right people for new jobsand the recruitment of new employees for the requisite technical job categoriesis essential.

ROI For Business as a Whole: Consider the potential ROI for the entire business. If there is a bottleneck operation in the automation process, you need to increase throughput, not just in one area, but throughout the entire organization. With business process automation platforms growing by 63% in 2019 you may be tempted to just dive in and throw caution to the wind. But, there is an effective playbook out there. Look at, for example, companies like Bosch, which is saving around $500,000 a year by automating some management operations regarding its thousands-strong network of suppliers. Find a similar story in your industry and study their playbook.

Continue to Cultivate and Develop AI: The business world is trending in the correct direction when it comes to workplace culture and AI. According to this publication, Forbes, 65% of workers are optimistic...about having robot co-workers...and 64% of workers would trust a robot more than their manager. Take steps to ensure that you have an abundant AI environment for success, an increase in knowledgeable talent/workers and a heightened corporate awareness of general AI knowledge and related benefits. Make certain these are fully embedded in your organization from top to bottom.

Effectively measuring ROI on AI is a universal challenge

Everyone faces the challenge of creating their standards, KPIs and goals for their AI. There have been several successful methods deployed. Here are a few to guide you on your path:

Determine What It Will Cost Versus What It Will Save: Focus on use-case goals around savings instead of potential revenue growth. This includes reduced employee hours, reduced headcount and less time on processes. How much you invest in AI should be based on these saving forecasts and not revenue uplift. This calculation determines how much you should be willing to invest and the break-even point for AI deployment. If the deployment is not successful, the organization will have risked only what it expected to save, rather than risking what it expected to add in revenue.

Focus on Soft Dollar Benefits: on top of cost savings and additional revenue, companies must also calculate soft dollar benefits, such as fewer errors, reduced turnover, faster access to information and service, etc. AI will improve employee productivity, customer satisfaction, and it will reveal areas where a company may have been unknowingly struggling to achieve maximum value.

Know When the Break-Even Point Will Be: The break-even point is when the cost savings of an AI project equals the investment. Once an organization has calculated what it hopes to save by implementing AI, only then should it begin to consider how much to invest. Many organizations struggle with predicting the break-even point for AI deployments. By allowing cost-savings to dictate the initial AI investment, companies can begin to estimate when the break-even point should be reached.

New Product or Service = New Revenue Streams: this point is about maximizing ROI. Once an organization has become fluent in AI deployment, its an ideal time to determine ways in which AI can help deliver new products or services to customers. Businesses that attempt this level of deployment should invest with both product development and AI piloting principles in mind. New products and services require additional investments beyond the AI technology itself (including marketing, sales, product management, etc.). As a result of these additional investments, organizations should not use the previous equations for determining ROI.

Stay committed to digitalization and automation as if your business and career depends on it, because it does. Stay committed to maximizing efficiency and return on your investment, and most importantly, being able to demonstrate ROI. If you answer the questions above, and execute the four steps of implementation outlined in conjunction with a well worn path to success, you will set yourself and your business up for triumph, and cast yourself vastly ahead of any competition. Refuse to take these facts into consideration, and you will be left in the dustbin of history.

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4 Ways That You Can Prove ROI From AI - Forbes