The digital age is quickly becoming the age of artificial intelligence (AI). Why? Because so much data is being generated, only AI systems can analyze all that data in a timely fashion and businesses are beginning to reap rewards from the insights they are gleaning. Pundits have both lauded and decried AI’s pervasiveness. Just how pervasive AI has become in the business world is a matter of conjecture. A couple of years ago, Alison DeNisco Rayome (@AlisonDeNisco) reported, “Though the promise of artificial intelligence always appears to be on the horizon, many companies are already realizing its benefits, according to a new report from Narrative Science and the National Business Research Institute. Some 61% of businesses said they implemented AI in 2017 — up from just 38% in 2016, demonstrating that AI has finally hit the enterprise in a meaningful way.”[1] Earlier this  year, a Gartner study indicated AI implementation is much lower than 61%. Chris Howard, a research vice president at Gartner, states, “Four years ago, AI implementation was rare, only 10 percent of survey respondents reported that their enterprises had deployed AI or would do so shortly. For 2019, that number has leapt to 37 percent — a 270 percent increase in four years.”[2] The discrepancy between the two figures might be explained by the fact many different technologies fall under the AI umbrella. Whether the percentage of businesses implementing AI is 37 percent or 61 percent, the trend is clear and upward. Howard notes, “If you are a CIO and your organization doesn’t use AI, chances are high that your competitors do and this should be a concern.”

Getting started with AI projects

A study published by MIT Sloan Management Review and the Boston Consulting Group, concluded, “The gap between [AI] ambition and execution is large at most companies.”[3] Company aspirations are indeed high. The study reports, “Three-quarters of executives believe AI will enable their companies to move into new businesses. Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage. … Expectations for AI run high across industries, company sizes, and geography. While most executives have not yet seen substantial effects from AI, they clearly expect to in the next five years.” Like the studies cited above, however, they note implementation remains low. One of the reasons AI implementation may be lagging is fear — or, at least, grave concern. No executive wants to fail; but, according to a study conducted by Nimdzi Insights and commissioned and published by Pactera Technologies, “85% of artificial intelligence projects fail, and 77% of top tech leaders surveyed say that barriers to entry are pushback from senior management, failure to impress CIOs, and not seeing the value in the project. Management often doesn’t want to make the investment.”[4] As Howard noted, however, AI laggards could be at risk of failing. Fred Saayman, a Huawei business unit executive at Pinnacle, observes, “As the world becomes increasingly digital, AI and machine learning are becoming critical due to the sheer volume of data that is being passed through platforms today. All this data needs to be filtered and analyzed to understand, manage and service customers. AI can support human resources in this process, taking over the more mundane tasks, and allowing skill sets to be allocated to more strategic areas.”[5]

Like any investment, a business case needs to be made for artificial intelligence. An enterprise needs to understand how AI can further its business and what goals they want to accomplish. Saayman notes, “Evaluation is crucial here — don’t jump into unnecessary solution deployments unless you have a big picture of the entire business and what you hope to achieve. AI is not suitable for all environments, so just because solutions are available, it doesn’t mean that they are essential or that they meet a particular need within your business.” He adds, “As with every new big project, you will need executive buy-in, and someone to champion the initiative from the top.” Executive buy-in seems to be one of the biggest challenges facing companies; but, there are other challenges as well. John McCormick (@McCormickJohn) reports, “Many organizations have deployed artificial intelligence, but wider adoption of the technology is being held back by issues including a corporate culture that doesn’t recognize the need for AI, a lack of data and a shortage of talent.”[6] He draws those conclusions from a report published by O’Reilly Media Inc.

Achieving a successful AI implementation

Bernard Marr (@BernardMarr), a strategic business & technology adviser, asserts, “Those businesses that don’t capitalize on the transformative power of AI risk being left behind.”[7] He suggests successful AI implementation begins with a solid AI strategy and he recommends that strategy focus on nine business areas. They are:

1. Business strategy. “Creating an AI strategy for the sake of it won’t produce great results,” he writes. “To get the most out of AI, it must be tied to your business strategy and your big-picture strategic goals.”

2. Strategic AI priorities. Marr writes, “Now that you’re absolutely clear on where your business is headed, you can begin to identify how AI can help you get there. In other words: What are our top business priorities? What problems do we want or need to solve? How can AI help us deliver our strategic goals? The AI priorities that you identify in this phase are your use cases.”

3. Short-term AI adoption priorities. “Transforming products, services or processes is never going to be an overnight task,” Marr stresses. “It may take some time to deliver the use cases you’ve identified. For that reason, I find it helps to also identify a few (as in, no more than three) AI quick wins – short-term AI priorities that will help you demonstrate value and gain buy-in for bigger AI projects.” At Enterra Solutions®, we call this a crawl, walk, run approach.

4. Data strategy. Marr writes, “AI needs data to work. Lots and lots of data. Therefore, you need to review your data strategy in relation to each AI use case and pinpoint the key data issues.”

5. Ethical and legal issues. “Let’s not beat around the bush,” Marr writes, “the idea of super-intelligent machines freaks people out. It’s therefore crucial that you apply AI in a way that’s ethical and above board.” Ethical behavior also protects you from undesired legal consequences.

6. Technology issues. Since AI is a new technology, there will be technology issues. Marr writes, “Here you identify the technology and infrastructure implications of the decisions you’ve made so far.”

7. Skills and capacity. Like McCormick pointed out, many organizations suffer from a shortage of talent. Marr suggests conducting an in-house AI skills and capabilities assessment and work out where you need a skills injection.

8. Implementation. Here is where you look for quick wins and utilize a crawl, walk, run approach.

9. Change management issues. Change is never easy. Marr notes, “Because people are so wary of AI, particularly what it might mean for their jobs, change management is a really important part of any AI project.”

Concluding thoughts

In today’s business environment, it shouldn’t be too difficult to make a business case for artificial intelligence. As Marr points out, however, you need to have a clear vision of how AI will be used. Saayman concludes, “The bottom line is that AI is the game-changer of our future. Its impact will be felt on all aspects of our business and personal lives. Business leaders need to understand this new world quickly, if they hope to not only thrive, but survive.”

Footnotes
[1] Alison DeNisco Rayome, “61% of businesses have already implemented AI,” TechRepublic, 17 January 2017.
[2] Gartner, “More Than 35 Percent of Organizations Are Using AI, Survey Shows,” Supply Chain Brain, 22 January 2019.
[3] S. Ransbotham, D. Kiron, P. Gerbert, and M. Reeves, “Reshaping Business With Artificial Intelligence,” MIT Sloan Management Review and The Boston Consulting Group, September 2017.
[4] Laurie Sullivan, “Why 85% Of Artificial Intelligence Projects Fail,” MediaPost, 21 June 2019.
[5] Pinnacle, “Activating intelligence in your business,” TechCentral, 29 May 2019.
[6] John McCormick, “AI Adoption Held Back by Company Culture, Talent Shortage, Data Issues,” The Wall Street Journal, 22 February 2019.
[7] Bernard Marr, “How To Develop An Artificial Intelligence Strategy: 9 Things Every Business Must Include,” Forbes, 19 March 2019.