The Risks and Rewards of AI

The Risks and Rewards of AI

Many in the business world have been abuzz about AI technology. Some have hailed it as a magic bullet. Others disparage it as a menace to society. 

As with most hotly-contested topics, the reality about AI is somewhere in between those two extremes. For businesses who know how to harness the technology properly, there can be great rewards to adopting AI. But there are certainly pitfalls along the way, and if you’re not careful about how you implement AI, you might find yourself facing cybersecurity breaches or regulatory headaches.

A recent survey out of McKinsey perfectly illustrates the importance of managing this balancing act. While some businesses have been able to use AI to generate great results, many flounder in their rollout and adoption of the technology.

Let’s take a closer look at the risks and rewards of AI: what it can do for your business and how your own approach to the technology might be holding you back from realizing its full potential.

How AI Can Change Business for the Better

There’s no doubt about it: When organizations apply AI technology properly, they see positive results. In their survey, McKinsey found that 63 percent of leaders who had adopted AI technology reported an increase in revenues, while 44 percent said it also helped them reduce costs.

And the great thing about AI is that it can add value in a variety of applications. Businesses can use it for everything from marketing and sales to supply-chain management to HR. The McKinsey survey found that businesses reported benefits across these various applications of the technologies.

For example, marketing and sales teams can use AI to predict the likelihood of conversion, to analyze customer service data, and to build dynamic pricing models. In supply-chain management, AI can help forecast demand for products so that you don’t have extra items clogging up your warehouse or collecting dust on store shelves. And in manufacturing, AI can be used in conjunction with robotics technology to build and assemble products.

Not All AI Adoption Is Created Equal

The McKinsey study also found that, while any company can see a benefit from embracing AI technology anywhere within its organization, those that adopted AI in certain business functions linked to greater value creation saw the best results.

No matter what industry you’re in, incorporating AI into your marketing and sales efforts and supply-chain and manufacturing often has the greatest positive impact overall. By contrast, finance, business strategy, and HR departments see a much smaller positive impact when using the technology.

In their survey, McKinsey noted that among the respondents they deemed to be “high performers” when it came to their AI usage, 80 percent had adopted AI in marketing and sales. By comparison, only 25 percent of companies falling outside the high-performance bounds had applied AI to their sales and marketing efforts.

What Businesses Must Consider When Approaching AI

In addition to applying AI to those high-value business functions, there are seven other things that can cause an AI implementation to either sail or sink.

1. You Must Align AI With Your Broader Strategy

AI can’t be implemented in a vacuum. You must link your AI goals to your broader corporate strategy. Why are you using this AI technology, anyway? Without a connection between the tech and the big picture of where you’re hoping to take your company, you won’t be able to get the most out of an AI program.

2. You Must Invest in Talent and Training

New technology can be complicated and confusing. Your existing team is used to doing things the old way. They might be hesitant to try a new approach. Plus, with cutting-edge technology like AI, your existing team likely doesn’t have the knowledge or skill set to make the tech work to its fullest advantage.

To get your AI program off the ground, you need to bring new AI experts on board. They don’t need to replace your existing team. But you do need people who know the technology inside and out to supplement your current staff.

Not only that, but new technology is always changing. It pays to start a training program that gets your current team up to speed on AI. It can remain in place and help everyone stay abreast of future shifts in the AI landscape.

3. You Must Encourage Cross-Functional Collaboration

Your AI use needs to be integrated fully into day-to-day business. It’s not enough to align your AI goals with broader strategy; your team must take that message to heart and work to realize it every day.

This starts with making sure that teams are collaborating across functions. You can’t have business leadership sequestered in one room, with the AI computer scientists in another space altogether. Instead, these teams should come together regularly. That’s how they ensure their efforts are aligned and supporting the common goals.

4. You Must Create a Strategy and Data Governance Framework for Using Data

Once you’ve collected all of your data, do you know what you’re going to do with it? Having a strong strategy and process for data governance is important. If you don’t have rules around what you do with the AI technology, it’s possible for someone to make a misstep unilaterally and do something that either jeopardizes your growth potential or your regulatory compliance.

5. You Must Build a Repeatable Process

AI technology only works if you’re regularly feeding it clean data. To make sure that’s happening, you need a repeatable set of steps around your AI implementation and usage. This is about making sure you have clearly defined rules.How regularly do you update your models? How will your team use the data? And how do you share the learnings with your entire organization?

6. You Must Put the Data to Work and Measure Results

Many companies adopt AI technology but then are slow to apply that data to their work. Using AI data six months or a year after it’s been collected and analyzed is pointless! Instead, structure your team in a way that empowers them to take immediate action on AI learnings.

A key component in putting the data to work is a framework for measuring results. Creating a dashboard with the right KPIs that you update and share regularly ensures your data is serving you as effectively as possible.

7. You Must Take the Risks of AI Seriously

The McKinsey survey found that, while some executives are aware of the risks of using AI—from cybersecurity breaches to regulatory compliance gaffes to accidental incidences of bias—very few do anything to reduce these risks. If you’re not careful about how you use AI, you open your organization and your customers up to potential risks.

And the results of a mistake when it comes to AI can be painful for your business. (Think fines from regulators, a drop in sales, and a lasting distrust from customers who have been burned by your negligence.)

AI adoption is on the rise, and it’s easy to see why. Businesses in any industry can benefit from applying the technology to their work. But a successful AI implementation strategy goes well beyond hiring one AI expert, purchasing the technology, and diving into the deep end. There is a lot of planning, strategizing, and culture-shifting that need to happen within your organization to get the most out of AI.

We’ve found that proper use of AI leads to higher growth rate. Where do you stand? Get a free, instantaneous personalized report by taking our Growth Mindset Assessment (it only takes 2 minutes).

About the Sterling Woods Group, LLC

The Sterling Woods Group’s mission is to help clients make sense of their data to predictably grow sales. We apply data science to help you optimize your sales funnel, improve your marketing ROI, launch new products successfully, and enter new markets profitably.

We use a hypothesis-driven, data-supported methodology to discover insights that no one else is paying attention to. Then, we help you assemble the right sales strategies, marketing plans, technologies, and resources to seize this opportunity.

About the Author

Rob Ristagno, founder and CEO of the Sterling Woods Group, previously served as a senior executive at several digital media and e-commerce businesses, including as COO of America’s Test Kitchen. Starting his career at McKinsey, his focus has always been on embracing digital technology and data science to spur strategic growth.

Rob is the author of A Member is Worth a Thousand Visitors and is a regular keynote speaker at conferences around the world. He has been featured on ABC, NBC, CBS, Fox, and Digiday.

He holds degrees from the Harvard Business School and Dartmouth College and has taught at both Harvard and Boston College.

Rob lives outside Boston, MA with his wife, Kate; daughter, Leni; and black lab, Royce.

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