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The Uncertain Future of Big Data and AI

The Uncertain Future of Big Data and AI

The AI wars are heating up. Not in some sci-fi world where humanity valiantly struggles against superintelligent AI-powered robots bent on human destruction. The real action is taking place right now, as the adoption of AI and related technologies reaches a tipping point—and creating a dramatic competitive advantage for organizations at the forefront. The AI-powered "haves" already are outpacing the "have-nots" in implementing AI solutions to automate processes, augment human capabilities, and deliver breakthrough products.

We've written on this blog about the importance of building AI capabilities in-house and using data strategies for competitive advantage. And we are beginning to see greater leaps in the adoption of AI and other exponential technologies. AI adoption is spreading rapidly across every industry and domain. And the organizations that are developing competencies pairing AI with data analytics are developing the capabilities to leap ahead of competitors.

Early adopting pioneers who invested early in AI experiments and applications are differentiating themselves in several ways, according to MIT Sloan Management Review. Pioneering AI companies:

  • Use AI throughout the enterprise to drive improvements in operations and growth. This includes using AI to streamline—and sometimes reinvent—business operations.
  • Are delivering profitable new AI-powered products and services.
  • Invest more in AI applications than their less-experienced counterparts.
  • Understand the benefits of implementing a formal AI strategy, with more than 90% of respondents reporting that they have an AI strategy in place.

While traditional business strategies seldom included AI or data analytics components, today these emerging and converging technologies are a vital part of any large organization’s strategy development for future growth.

Building AI and data capabilities for real results

Many organizations watching the exponential growth of AI and data analytics are inspired by the potential combinations of these powerful technologies, but not sure how to harness them in support of their own strategic objectives. A useful first step may be to gauge your organization's current progress in building AI and data capabilities.

Artificial Intelligence

data capacity fundamentals

Research shows a growing gap between organizations that are simply aware of AI and those using it productively. The rapid pace of AI adoption and implementation—270% in the last four years, according to a 2019 Gartner survey—shows that organizations who are behind the adoption curve have good reason to be concerned for their survival.

Take a moment to assess your own organization’s AI capabilities: Rate your organization's AI capabilities on a scale of 1-5 (where 1 = completely disagree and 5 = completely agree):

  1. Awareness: We are aware of the potential of AI and related technologies to significantly improve business results.
  2. Understanding fundamentals: We have a reasonable understanding of AI and related technologies, and how they function.
  3. Understanding applications: We understand how AI is being used to answer questions, solve problems, and streamline processes.
  4. Hands-on experiments: We are experimenting with AI, but have not deployed applications in our organization.
  5. Production solutions: We have built and deployed an AI application that has achieved cost-savings or revenue impact.

How did you do? What will your next steps be?


ai capacity fundamentals

It’s easy to get lost in the mountains of data we’re producing each day. It’s a challenge to put that data to work for growth and competitive advantage. There are a number of drivers creating today’s unprecedented boom in global AI development. Computing power is becoming more available and less expensive, models and algorithms more powerful and sophisticated.

But perhaps the biggest driver is the remarkable volumes of data generated by our digital lives each day that provide the fuel for AI—and all that data becomes more valuable as we learn new ways to extract its knowledge and insights.

Take a moment to rate your own organization’s data analytics capabilities: Rate your organization's data analytics capabilities on a scale of 1-5 (where 1 = completely disagree and 5 = completely agree):

  1. Collecting: We collect and store data for later use, even if those uses are not clearly defined.
  2. Prioritizing datasets: We have standards and processes for organizing and prioritizing data sets according to our goals.
  3. Analyzing: We have defined and documented processes to extract value and insights from our data.
  4. Applying: We use data analytics to complement AI and to improve our products and processes.
  5. Exclusive advantage: We use proprietary data and algorithms to improve decision-making and competitive advantage.

How did you do? What will your next steps be?

AI and Data

A flywheel to power your enterprise

A popular way to illustrate the complementary nature of AI and data analytics is the flywheel model.

  • More powerful AI capabilities enable better and faster operations, customer experiences, and product development.
  • In turn, your organization attracts new users who provide additional data to improve operations, customer experiences, and products even further.

Over time, a nearly insurmountable competitive advantage can be realized. For example, imagine having to compete against Google’s search results ranking algorithm or Amazon’s recommendation engine.

How fast is your organization’s flywheel spinning? Are your AI capabilities on par with data capabilities? Assessing your AI and data analytics practices can help to prioritize your resources and identify imbalances that keep your flywheel from spinning smoothly. If your data analytics efforts are mature but you have not begun to experiment with AI, there’s lots of work to be done. But it’s important to press on, because organizations that score high on combined data and AI capabilities tend to score high in the marketplace, with more efficient operations and innovative products and services.

Exponential enterprise strategy planning

As you build your organization’s virtuous cycles, remember the goal is to generate your own datasets and build AI capabilities in a way that enables you to trust your own data, achieve more valuable insights, and make better decisions.

If you’re interested in transforming into an exponential enterprise and securing or growing your competitive advantage, you’ll want to focus on building data and AI capabilities together, to the highest levels you can attain. Your future strategy development must maximize the use of exponential technologies like AI and data analytics. The difference between success and failure lies in ensuring your organization’s capabilities and culture are setting you up with the right future-proofed foundation.

Ty Henkaline

Ty Henkaline is CTO at Singularity. He has over 15 years experience in data science and analytics.

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