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    Executive Guide to Artificial Intelligence

    By Josh Adragna on September, 19 2017

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    Josh Adragna

    Several recent studies and surveys’ across several sources has shown that implementing an artificial intelligence strategy can be a challenging maze to navigate. With a lack of extensive business cases and A.I. implementation examples it can create a needle in the haystack scenario for business leaders not only looking for relevant A.I. solutions, but also successfully creating A.I. initiatives.

    Current state of AI:

    It’s no wonder that executives can feel in the dark when thinking through an AI strategy. According to HBS, while AI investing heats up, AI deployment is still in it’s infancy stages. With a total AI investment worldwide around 30 billion, less than 20% of enterprise businesses have actually rolled out an an AI technology (either partially, or aggregately). This is good news for executives looking to gain a competitive edge with AI as there is still time to climb the staircase. Additionally, executives should believe what they are hearing in the market - that AI can be transformational and gain efficiency increases - 30% of early adopters are seeing significant paths to to better margins and/or gaining more market share through product market penetration with many leading AI adopters forecasting a 3-5% increase in profitability than industry competitors in their respective spaces.

    Expectations vs Action:

    Expectations run high across industries, company sizes and applications. A leading MIT study suggests that 63% of respondents expect AI to deliver substantial positive net effects over the next 5 years. With executives talking expansively around implementing AI, there is a clear gap between built initiatives and where to start when deploying AI. Going at it “alone” will only lead to more questions, confusion and feeling left behind as the competitive market ramps AI. This blog will seek to surface key approaches to not only successfully deploying AI, but gaining support from the C-suite and board. In fact, most of’s customer base has reported that without help from leadership, their initiatives would have died long ago. They add that strong support not only comes from the CEO or IT team, but from all other C-level officers, including the board.

    Executives who successfully deploy AI - Don’t go at it alone:

    Many Enterprises report the need to rush to hire data scientists and engineers to build out capability “in-house”. After a decade-long AI winter, there are more AI core business solutions (manufacturing, customer service, etc) than ever before since AI was defined in 1956. Avoiding the trap of designating teams and IT to evaluate A.I. should be thwarted - as multiple solutions can be created without establishing a compelling business case. For max focus on specific use cases, AI projects should be assessed and led by both business and technology leaders - an approach that has been proven successful by not only Kylie customers, but strategic thinking executives across the board.

    Take the initiative:

    Many successful executives report that taking a more proactive approach to A.I. has led to early deployment’s with positive forecasts on increasing profitability over the outlook of companies with a “reactive” approach to A.I. With any initiatives in A.I. it’s important to note that digital capabilities come long before an AI strategy. Our research has indicated that industries leading the A.I. charge (like high-tech, automotive, telecom) are the ones that have become the most “digitized” with sound big data and infrastructure strategies that give executive leaders more ease in accessing key data points needed to train machine learning and advanced deep learning algorithms. In fact, companies that have made the digital transformation hurdle, are 5% more likely to have a positive and successful A.I. experience.

    Change Management:

    As people and processes embody the biggest challenges for change management in an organization seeking A.I. it’s key to note that the technological implementation is far less of a hurdle than managing people and how to properly structure an A.I. evaluation. Additionally, deciding on which new processes AI will start to handle v.s. what new focus employee’s will have post A.I. needs considerable thought. As the A.I shift is currently happening leaders will need to shift from core competency focus, to focus on decision management effectiveness. This new culture shift will be quite dependent on continuous learning and improvement.

    The tsunami of technology is here in the form of A.I. While the tide retracts and the beach dries, there is still time to make A.I. a highly competitive advantage before the wave is soon to engulf us all.

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