Interruptive, Disruptive and Other Rude Technologies: A Guide for Untangling AR, AI, and ML

If you are a CEO or C-Anything, your board is likely already requesting you produce a strategy for leveraging disruptive technologies like Augmented Reality (AR), Artificial Intelligence (AI), and Machine Learning (ML).

These innovative technologies are transforming the way we interact with the digital world and revolutionizing industries across the board. While AR, AI, and ML are related, they are distinct yet interrelated technologies, each with its unique purpose and capabilities. In this article, we will explore these technologies and consider how best to prepare for their implementation.

Understanding the Basics

Artificial Intelligence (AI) is intelligence demonstrated by machines to mimic human cognitive functions like learning and problem-solving. AI encompasses a broad spectrum of techniques, from rule-based systems to deep learning.

Augmented Reality (AR) refers to technology that overlays digital information and visuals on top of the real world, typically through a smartphone or AR glasses.

Machine Learning (ML) is a subset of AI and AR that focuses on algorithms that can learn from data and improve a machine’s predictions or operations over time without being explicitly programmed.

These technologies, in their various combinations, enable applications like computer vision, visual verification, object recognition, speech recognition, optical character recognition, i3D modeling, and natural language processing.

AI enables human-like intelligence in machines, AR integrates digital information with the real physical world, and ML is one approach to creating AI and AR that learns from data.

AI, AR, and ML Technologies are driven by Data

One of the biggest hurdles for companies to overcome in leveraging these new technologies is accessing and codifying their corporate knowledge.  These data sit in a variety of quasi-digital formats (PDF, DOC, PPT, MP3, MP4, etc.) and in the brain tissue of employees.  These data must be digitized to enable your organization to take advantage of AI.

Let’s consider a similar challenge faced by CIOs a few years ago.  CIOs could not leverage emerging technologies impacting their IT strategies without first understanding the elements in their IT configurations.  Several factors drove CIOs to invest heavily in curating their unknown IT configurations into what is called a Configuration Management Database (CMDB) including:

  • Need for visibility into the IT assets of the company,
  • Increasing focus on IT automation for improved efficiency;
  • Managing the cost of IT Service Management processes,
  • Making the shift to a digital business strategy,
  • Cloud adoption requiring high visibility and control across on-prem and cloud environments
  • Mergers, acquisitions, and consolidations of disparate IT environments for unified management.

Do these challenges sound familiar?

Managing Data to Claim Your Rightful Place as an AI-AR-Enabled Company

So, how can your company digitally capture and curate these corporate assets?  You should consider investing in what we will call a Digital Knowledge Management Database (DKMDB).

What is a Digital Knowledge Management Database? DKMDBs are central repositories for an organization’s critical information, data, and intellectual assets.

Various factors are driving the necessity for a DKMDB, including:

  • The lack of searchable access to partially digital repositories of company know-how.
  • Undigitized tribal knowledge that resides in online manuals, PPT files, DOC files, and in the brain tissue of your subject matter experts.
  • Employee turnover is causing a problem with valuable data leaving with departing employees.
  • Inability to break down knowledge silos where capabilities are trapped in cross functionally disabled data repositories.
  • The AR knowledge-sharing environment lacks an application platform to consolidate digital knowledge and enhance user experience.

As organizations strive to adapt to the evolving digital landscape, the importance of these databases will only continue to grow.

Considerations for implementing AI/AR technologies

As AI and AR technology continue to revolutionize how businesses operate, some companies face barriers to fully embracing these cutting-edge technologies.   Overcoming these barriers to claiming your rightful place as an AI/AR-enabled company requires:

  1. Instituting the right processes for capturing and curating your know-how.
  2. Evaluating the right technology platforms to create, digitally publish, and enable your knowledge exchange.
  3. Determining the right AI/AR talent mix of technical roles and business roles.
  4. Developing a trustworthy technology partnership understanding that AI/AR are emerging, disruptive, and yet to be fully harnessed technologies.
  5. Planning for organizational change management to promote AI/AR literacy and adoption.
  6. Tracking benefits and ROI of AI/AR implementations, to demonstrate business value quantitatively and qualitatively.

AI/AR technologies offer the potential to revolutionize how businesses operate and engage customers. To ensure a successful AI/AR journey, it is crucial to align with business goals, quickly test and adopt new technologies, and prioritize employee and customer satisfaction. Embrace the future with confidence so your organization can harness the full power of AI/AR.

Wherever you are on the AI/AR customer experience journey, the CareAR platform could be right for you. You can discover more on the CareAR website.

About the Author

Craig Nelson has been consulting leading global Fortune 500 companies across multiple industries for over 30 years.  He is passionate about driving positive change through the application of disruptive technologies and possesses a unique set of business and technological know-how for driving return on investment and capex value for customers.

He is a forward-thinking digital change agent and professional services leader for CareAR with a proven track record of leveraging people, processes, and technology as a force multiplier to drive the adoption of transformational digital technologies.

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