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The end of 2017 marked a snowball of hype around topics like blockchain, cryptocurrency, machine learning, and more. One of the most frequently discussed topics among business professionals was undoubtedly Artificial Intelligence (AI). Although many articles cover the potential benefits of AI, few address what preparation is necessary to make implementing this technology a truly competitive advantage for the enterprise.
AI is not a new term. It was first used in the summer of 1956 as a topic of discussion at the Dartmouth Conference. Even so, the term did not come about overnight but rather after hundreds of years of philosophical contemplation on the process of reasoning–dating as far back to the Greeks.
“If you look at the history of AI since its origin in 1956, it has been a story of peaks and valleys, and right now we are in a particularly exuberant time where everything looks like there is one magnificent peak in front of us.” — Vishal Sikka, Infosys Ltd. CEO and managing director
This new peak in hype largely owns itself to the growth of Big Data, or structured and accumulated data, that now enables AI to be realistically used to make business related predictions.
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What is Artificial Intelligence (AI)?
Artificial Intelligence (AI) is a branch of computer science that attempts to mimic human decision making. The term is often used interchangeably with Machine Learning, which is a sub-branch of AI that uses data to learn and make educated decisions.
Essentially what AI does is find patterns in data to predict business failures, successes, and other courses of action.
Although many professionals see the apparent value of AI, there is still a disparity between the expectation of what AI can accomplish for their business and the amount of effort needed for it to achieve scale.
According to a study done by MIT Sloan Management Group and BCG, “16% of respondents strongly agreed that their organization understands the costs of developing AI-based products and services. And almost the same percentage (17%) strongly disagreed that their organization understands these costs. Similarly, while 19% of respondents strongly agreed that their organization understands the data required to train AI algorithms, 16% strongly disagreed that their organization has that understanding.”
Nearly as many organizations that understand the amount of resources required to successfully implement AI, also have little to no understanding. In other words, there is a chasm between industry players who know the work involved and those who do not. Moreover, the study revealed that those who have a greater understanding of AI have incorporated the tech extensively in processes and offerings.
Based on the responses to the study’s survey questions, four organizational maturity clusters emerged: Pioneers, Investigators, Experimenters, and Passives. Those with the greatest understanding of AI are grouped together as Pioneers, and they proved to be more likely to use AI technology extensively in processes and offerings.
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Pioneers showed key differences from other maturity groups not only in their understanding of AI, but also in their use of comprehensive data, well-established processes, and internal AI professionals. Considering this, these three factors are useful in determining if your company is well-equipped to execute a successful AI initiative.
Is your company data ample and comprehensive?
If data made AI possible for business use, then data is the key to making it a success. AI reads data in order to find patterns in processes and situations, and then it provides predictions based on this data for the next course of action. Without an exorbitant amount of readable data, AI cannot provide accurate results.
This data cannot only be ample but it must also be comprehensive, providing both records of business successes and failures. According to Jacob Spoelstra, director of data science at Microsoft, “A mistake we often see is that organizations don’t have the historical data required for the algorithms to extract patterns for robust predictions. For example, they’ll bring us in to build a predictive maintenance solution for them, and then we’ll find out that there are very few, if any, recorded failures. They expect AI to predict when there will be a failure, even though there are no examples to learn from.”
An enterprise may have an efficient amount of data that covers favorable and less favorable outcomes. However, another common issue among most large enterprises is company data is fragmented, duplicated, and/or siloed. Considering this, the first major and probably most difficult step in preparing for AI implementation is cleaning up and collecting all the company’s data in a usable format for AI. But as many BI analysts will account to, there is no real end to investing in the company’s data pool.
Nonetheless, Matthew Evans, vice president of digital transformation at Airbus states “For every new project that we build, there’s an investment in combining the data… But we’re also able to reuse all of the work that we’ve done in the past, because we can manage those business objects effectively. Each and every project becomes faster. The upfront costs, the nonrecurring costs, of development are lower. And we’re able to, with each project, add more value and more business content to that data lake.”
This action of preparing data for AI is better viewed as a long term process and an investment. Each time the data is worked the opportunities for scale are amplified.
Are your business processes well-structured?
AI compliments, streamlines, and advances current business processes by taking over certain steps along a complex workflow. This means that enterprises must be able to look at their well-structured process and determine where in the process AI can be most useful. Without well-establish processes, implementing AI is likely to add more complexity to an already complex process.
Usually the steps AI takes over are repetitive and operational. AI rarely replaces entire workflows and the idea that AI will make many jobs obsolete is only partly true. AI is expected to free the human workforce from doing many monotonous tasks but not necessarily leave thousands or millions without work.
It’s probable that more skilled labor will be needed with the expansion of AI and that the current workforce will need to learn new skills.
This was the case with the popularization of ATMs in 1970s and onward. Initially, the amount of tellers decreased but for over a span of 40 years, ATMs allowed banks to save money on operations. In turn, this led to exponential growth in the number of branches and jobs until 2010 when advances in ATM capabilities led to another drop in the demand for tellers.
Respondents of the MIT & BCG study estimate it will take at least five years for AI to have a significant impact business offerings and the current workforce. Only time will tell if in 40 years there will be more or less job growth in industries affected by AI.
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Nonetheless, what many enterprises don’t fully consider in the short term is creating a fluid pass-off to and from human workers.
Julie Shah, an associate professor of aeronautics at MIT, says, “What people don’t talk about is the integration problem. Even if you can develop the system to do very focused, individual tasks for what people are doing today, as long as you can’t entirely remove the person from the process, you have a new problem that arises — which is coordinating the work of, or even communication between, people and these AI systems. And that interaction problem is still a very difficult problem for us, and it’s currently unsolved.”
One way to combat this integration problem is by selecting an agile orchestration layer that will help the transfer of work to be more fluid between human and machine workers. Some agile BPMs and other cloud-based softwares can be this layer that is both business user friendly and AI compatible.
Are you willing to hire in-house AI professionals?
The study revealed that enterprises with a greater understanding of AI invest more in hiring in-house AI professionals. One reason for this is that AI can’t simply be implemented and “run” like app software. AI algorithms need to be trained.
According to the MIT & BCG research “Training AI algorithms involves a variety of skills, including understanding how to build algorithms, how to collect and integrate the relevant data for training purposes, and how to supervise the training of the algorithm.”
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Outsourcing this kind of labor could mean that you are left with algorithms that won’t get the job done the way you expected. Investing in professionals who know how to properly feed, write, and monitor these algorithms for long-term business performance is essential for reaching scale.
A strong digital foundation is necessary for AI
All in all, preparing for AI takes active steps toward collecting data, developing well-structured processes, and hiring professionals that will dedicate toward long-term success. These are the digital foundations for AI adoption. Without this base, AI is an unrealistic step and could significantly waste business resources.
If your company lacks this solid base, consider taking steps toward digital transformation by finding agile software that will quickly enable your company to collect data and establish strong, fluid processes.
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