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Why AI-fueled organizations hold the competitive upper-hand

Throughout the marketplace today we see emerging technology trends disrupt the status quo in mind-blowing ways.

Intelligent interfaces are rapidly redefining the way humans, machines, and data interact. Serverless computing is shifting IT’s traditional focus from operations more to creating business outcomes. And, powerful connectivity building blocks like 5G, edge computing, and software-defined networking are ushering in a new era of advanced networking.

At first glance, these trends may seem like a grab bag of disparate innovations and opportunities. However, if you peel back their veneer, you will recognize they all share common ground: cognitive technologies and data. Yes, artificial intelligence (AI), machine learning (ML), and natural language processing (NLP)—along with rapidly growing stores of customer, operational, and market data—are powering much of the interesting work organizations are now doing in the areas of automation, data-driven decision making, and strategy development.

In Deloitte Consulting LLP’s Tech Trends 2019 report we explore the growing “AI-fueled organizations” trend in which organizations are deploying AI and other cognitive tools throughout their operations. As cognitive technologies and advanced data management techniques standardize across industries, becoming an AI-fueled organization is no longer just a strategy for success, it has become table stakes for survival.

But where to begin? To lay the groundwork for your own AI-fueled journey, consider taking the following steps:

Get your data in order

While AI offers an opportunity to process, analyze, and act on data at phenomenal speeds, quality data is necessary to stand up AI in the first place. Of the roughly 1,100 IT and business executives interviewed for Deloitte’s second annual State of AI in the Enterprise survey, 39 percent identified “data issues” as being among the top three challenges they face with AI initiatives.

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To realize the benefits of becoming an AI-fueled organization, you’ll need to put in place more dynamic data governance, storage, and architecture. Advanced data management fuels an enterprise AI engine and is a core building block for deriving autonomous insights from your vast data stores.

For companies looking to boost their data management capabilities, the Holy Grail lies in creating the architecture and processes required to handle growing volumes of data in an agile, efficient fashion. While there are any number of ways to pursue this goal, consider taking a couple of basic steps.

First, by performing an inventory of current systems and data, you can quantify how much manual effort is expended daily/monthly/yearly to keep the sprawl intact and functioning. This information will help you better understand your current data capacity, efficiency (or lack thereof), and costs, and provide a baseline for further analysis.

Second, consider developing a dynamic data steward that uses cognitive tools like ML, NLP, and robotic process automation to dynamically to resolve common data quality issues like duplicate records, misspellings, and inaccuracies.

Let data scientists work in teams

Among the most prized IT skills today are those in the areas of data analysis, data modeling and applications development. As AI adoption grows, companies will increasingly value expertise in data science, algorithm development, and AI system design—with special emphasis on the human-centered design skills required to develop personalized user experiences.

Unfortunately, few organizations currently have the skills sets and experienced talent they will need for an AI-fueled journey. The State of AI survey revealed that 31 percent of respondents face a “major or extreme skills gap,” with AI software developers and data scientists being among their top areas of concern.

Organizations can approach data science skills challenges by putting in place a small team of experienced data scientists who can then help train IT professionals for the journey ahead. While this strategy offers a potentially effective means of leveraging internal expertise, it comes with a “buyer beware” warning.

Some organizations have assigned individual data scientists to various IT groups rather than keeping them all together in single, focused team. This approach, while well-intended, can make data scientists more susceptible to “shiny object syndrome,” a situation in which they become distracted by lots of cool possibilities for data+tech, while losing sight of the company’s overarching technology strategy. Keep your data scientists aligned and focused in a single working team.

Don’t forget to educate the business

In many organizations, IT leads technology transformation efforts, with the business following several steps behind. This can put the business at a disadvantage, particularly in the areas of data science and machine learning. Unless business leaders are “fluent” in your company’s systems—their capabilities and adjacencies, their strategic and operational value, and the particular possibilities they enable—these decision makers won’t know what questions to ask or which opportunities to pursue.

In the context of data management and AI, tech-fluent business executives will likely be able to view technology transformation in its proper strategic context, use AI and data management tools more effectively, and adjust more readily to redesigned jobs and augmented processes.

For these reasons CIOs increasingly see tech fluency as an important driver of IT’s long-term success. In Deloitte’s 2018 Global CIO Survey, 96 percent of respondents said that it was their job to make employees throughout the business more tech fluent. Today, many professionals—and not just those working in IT—are dedicated to remaining tech fluent and staying on top of the latest innovations. To help them along, consider developing innovative ways of learning and institutionalizing training opportunities can help workers contribute substantively, creatively, and consistently to transformational efforts, no matter their roles.

In the next 18 to 24 months, expect to see a greater variety of cognitive technologies deployed in ever-more creative ways. Some may be discreet applications of AI or ML to automate inefficient tasks. In others, cognitive technologies will serve as enablers of other systems. They may also be foundational of large scale digital transformation efforts.

Are you—and your data—ready?

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