Throughout history, the business landscape has continually changed as new conditions and new technologies emerge. In recent years, artificial intelligence (AI) has been one of the most significant technologies reshaping the business landscape. Reporters from i-Scoop observe, “Although the interest for leveraging artificial intelligence in business is high, implementation is still low.”[1] They go on to note that Gartner analysts predict “we’re about to see a growth in ‘meaningful’ artificial intelligence deployments.” They use the term “meaningful” because “46 percent of [surveyed] CIOs [have] developed plans for AI implementation” (i.e., they plan on significant AI spending). Companies are taking a cautious approach to AI implementation because many decision makers remain unclear about the benefits AI can provide. As a result, IDG analysts have found, “Only one in three AI projects are currently succeeding.”[2] The problem is not with AI but with associated challenges involving “data incompatibilities and organizational frictions.”

Adopting AI solutions is critical for future success

Despite AI’s slow start, many analysts believe AI implementation is essential for future business success. Doug Bordonaro, Chief Data Evangelist at ThoughtSpot, notes business leaders have for years used business intelligence to help them make better decisions. They are always looking for better tools to help them ask a question and/or make answers easier to understand.[3] Bain analysts, Michael C. Mankins and Lori Sherer (@lorisherer), assert that if you can improve a company’s decision making you can dramatically improve its bottom line.[4] They explain:

“The best way to understand any company’s operations is to view them as a series of decisions. People in organizations make thousands of decisions every day. The decisions range from big, one-off strategic choices (such as where to locate the next multibillion-dollar plant) to everyday frontline decisions that add up to a lot of value over time (such as whether to suggest another purchase to a customer). In between those extremes are all the decisions that marketers, finance people, operations specialists and so on must make as they carry out their jobs week in and week out. We know from extensive research that decisions matter — a lot. Companies that make better decisions, make them faster and execute them more effectively than rivals nearly always turn in better financial performance. Not surprisingly, companies that employ advanced analytics to improve decision making and execution have the results to show for it.”

Bordonaro asserts AI, which incorporates advanced analytics, can greatly improve decision making. He explains, “The machine learning behind search suggestions can allow end users to ask questions without significant training. We all know how to use a search bar, after all. And algorithms that pick the best visualization, or decide that the best way to show an answer isn’t a visualization at all, but a table, promise to make the answer easier to understand as well. But the promise of AI goes further than just rapidly improving the two existing areas of growth. AI adds a third — helping determine the right question to ask in the first place.” When pundits talk about asking questions and getting answers from cognitive platforms, there is an implicit understanding that a massive database is being queried. AI and data have a symbiotic relationship. Data has become the lifeblood of most organizations as they transform into digital enterprises.

Brian Johnson, a data scientist, insists going digital (i.e., becoming a digital enterprise) isn’t going far enough. He believes digital enterprises need to become intelligent enterprises. He explains, “This past year, we have seen AI becoming a vital enterprise technology to create new business models, provide a better customer experience, modernize organization’s existing business processes, and reduce cost to build an ‘Intelligent Enterprise.’ Intelligent enterprises are defined by their use of data to achieve desired outcomes faster and with less risk through better cognition, automation, and integration. AI helps enable the intelligent enterprise by automating the detailed analysis of large volumes of structured and unstructured data to achieve valuable and actionable insights to help shape business outcomes.”[5]

Becoming an intelligent enterprise

Because data and AI are inextricably connected, AI success relies on having good data. Bharath Gowda, Vice President of product marketing at Databricks, explains, “Data is the fuel that powers AI, [and] large amounts of reliable data that data scientists can iterate on is the key to AI success. … But organizational silos between data science and engineering cripples the iterative model development process.”[6] In other words, the best place to start to ensure AI implementation success is with the data. Integrating data is absolutely essential. Gowda notes, “A unified analytics platform [can help integrate] big data with AI and fostering better collaboration between data science and engineering.” Cognitive computing platforms foster the kind of collaboration encouraged by Gowda because they can handle both structured and unstructured data. Rohit Adlakha (@rohitadlakha17), Vice President and Global Head for Automation for Wipro HOLMES, adds, “To realize the full potential of AI, a comprehensive strategy — one that includes full enterprise-wide integration — must be defined by leadership teams and stakeholders across all business units. Understanding and prioritizing specific applications of AI initiatives across business units encourages stakeholder buy-in and ensures maximum return on investment while reducing the risk of creating information silos.”

Once an enterprise is sure its data is up to snuff, Gartner analysts suggest there are four AI implementation steps that should be followed.[8] They are:

1. Begin by aiming low with your AI projects. At Enterra Solutions® we recommend a “crawl, walk, run” approach when implementing cognitive solutions. Gartner analysts believe the “aim low” strategy is important because many organizations “still have a learning curve ahead of them and need to better understand artificial intelligence business benefits.”

2. Focus on augmenting people. Many analysts, including those from Gartner, believe intelligence augmentation should be the goal of most businesses. They explain, “It’s probably better to drop the job replacement mentality and look at how artificial intelligence can augment worker’s capabilities to avoid resistance but also to align with business reality of AI as it is, in most cases a decision-enabler and indeed augmentation of workers.”

3. Plan for knowledge transfer. Gartner analysts note, “Of the skills that lack most, data science skills are cited most frequently.” Fortunately, many cognitive platforms include embedded analytics that can compensate for lack of data science skills in a company. Gartner analysts conclude, “On top of being able to understand, manage and exploit data it is key to make sure the right data are fed to the AI engine in order to get the right outcomes. This is key and needs to be understood in the organization as part of that knowledge transfer.”

4. Select the proper AI solutions in the initial stages. Ensuring the right mathematical model is used to achieve desired results is critical. Too many AI solutions are black boxes. Gartner notes, “It’s mainly important to choose transparent AI solutions: there needs to be an insight into how the AI engine comes to specific decisions.”

Summary

Blake Morgan (@BlakeMichelleM), a self-proclaimed customer experience futurist, writes, “Automation and AI can sort through huge amounts of data to find trends and make predictions. It can understand human language to answer questions or provide information through chatbots. Automation and AI can improve efficiency in our personal and professional lives. By automating many of our mundane tasks, we are free to focus on human elements like creativity and personal relationships. Artificial intelligence is definitely the future.”[9] Analysts from Seeking Alpha conclude, “AI & Big Data is emerging as one of the most potentially disruptive themes in the digital world. As the world’s data grows exponentially, AI capabilities are tracking close behind, the far-reaching implications of which are becoming clearer every day.”[10] Companies without plans to implement artificial intelligence into their operations are likely to find themselves tossed into history’s dustbin because they will be unable to navigate the business landscape of the future as well as their more agile competitors.

Footnotes
[1] Staff, “Artificial intelligence in business: CIO challenges and recommendations,” i-Scoop, April 2018.
[2] Elliot M. Kass, “Only one in three AI projects reported to succeed,” Information Management, 6 August 2018.
[3] Doug Bordonaro, “How Is Artificial Intelligence Changing the Business Landscape?” Forbes, 31 July 2018.
[4] Michael C. Mankins and Lori Sherer, “Creating value through advanced analytics,” Bain Brief, 11 February 2015.
[5] Brian Johnson, “Building an Intelligent Enterprise with Artificial Intelligence (AI),” Medium, 29 August 2018.
[6] Kass, op. cit.
[7] Rohit Adlakha, “How C-Suite Leaders Can Best Implement AI,” InformationWeek, 14 May 2018.
[8] i-Scoop, op. cit.
[9] Blake Morgan, “How To Leverage Artificial Intelligence To Gain A Business Advantage,” Forbes, 30 August 2018.
[10] Staff, “AI And Big Data: The Future Of The Digital World,” Seeking Alpha, 15 May 2018.