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Artificial Intelligence (AI) Strategy: Top Priorities

Editor's Note: Take a look at our featured best practice, ChatGPT: Examples & Best Practices to Increase Performance (85-slide PowerPoint presentation). OpenAI ChatGPT has advanced language processing. It can write like a human and speak naturally. Texting, chatbots, and conversational history are some of its uses. This talk will show you how to use ChatGPT to improve your results in the above areas. We'll cover how to use ChatGPT to create [read more]

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Artificial Intelligence (AI) is one area considered by many executives to enable Automation and steer positive growth.  A couple of years ago, most executives thought that deployment of Artificial Intelligence isn’t a big deal.  However, revamping traditional systems, implementing AI, and scaling it, in reality, is not as simple as it seems.

A survey by PwC Research in 2020, which gathered responses of 1062 business leaders, validates that scaling and industrializing AI is not straightforward at all.  Only 4% of the respondents asserted that they plan on implementing organization-wide AI in 2020.  A year earlier, the same survey revealed 20% of the executives planning to do that.  The survey shows a significant decrease in the number of senior leaders thinking of executing AI.

The reason for this dwindling interest in AI deployment is mainly because of the tough prerequisites necessary—contemplation, resources, preparedness, overhauling legacy systems, and integration of technology applications—for enterprise-wide AI implementation.

A robust AI Implementation Strategy needs to be first devised in order to assist the organizations in moving forward with their AI deployment plans.  Research reveals 5 key priorities of AI Strategy that businesses should follow to position themselves as AI leaders and reap value from Transformation in future.  These priorities not only highlight the key requirements for AI deployment but also pinpoint ways to maximize pay offs associated with the initiative:

  1. Boring AI
  2. AI-ready Workforce
  3. Responsible and Ethical AI
  4. AI Operationalization
  5. Business Model Innovation

Let’s delve deeper into a few of these key priorities.

Boring AI

One of the key reasons to employ AI, as cited by PwC research, is to automate routine administrative functions—e.g., using AI to pull information from tax forms, bills of lading, or invoices that can otherwise take up long hours of human effort.  44% of respondents revealed that AI will help them operate more efficiently.

To ensure AI adds value to the business, leaders should develop a strategy to identify the areas where AI can have a much deeper impact; build capabilities to do that; develop AI solutions, govern them, and embed them with existing systems.

AI-ready Workforce

Building or enhancing the capabilities of the workforce to become AI ready is critical today not only for technology enterprises but also for other businesses.  Organizations should identify the skills required for AI and train their people to deploy AI solutions.

However, thinking of achieving this through traditional means of offering training sessions isn’t a viable strategy to tap the opportunities offered by AI.  In addition to training people, organizations should cross-skill their people in multiple trades and provide them the opportunities to apply and hone in the skills learnt.  In fact, organizations should reward people who apply what they learn into real-time problem-solving and productivity enhancement.

Responsible and Ethical AI

AI can be perilous if adequate understanding of its responsible use and necessary procedures to protect against its risks and negative usage are not taken.  There are growing apprehensions around AI related risks e.g., biased algorithms, facial recognition tools, and deep fakes.  As per PwC survey, a large majority of respondents, using AI routinely, declared readiness in their organizations in terms of taking sufficient measures to protect against AI risks.

However, in reality most organizations are quite far from implementing controls around data and decisions generated using AI.  Just about 33% businesses mentioned having the ability to fully tackle risks associated with data powering AI, AI models, outputs, and reporting. It is imperative to have rigorous Risk Management processes in place to effectively use AI in the workplace and address the risks associated with it.   AI risks can be mitigated by integrating processes, tools, and controls needed to address AI bias, explainability, security, accountability, and ethics.

Interested in learning more about the other key strategic priorities essential for AI deployment readiness?  You can download an editable PowerPoint on Artificial Intelligence Strategy: Top Priorities here on the Flevy documents marketplace.

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This is a primer to ChatGPT and includes foundational concepts to understanding the application, including GPT (Generative Pre-trained Transformer) and Prompt Engineering. First, what is ChatGPT? ChatGPT is a pre-trained language generation model developed by OpenAI. It uses deep learning [read more]

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About Mark Bridges

Mark Bridges is a Senior Director of Strategy at Flevy. Flevy is your go-to resource for best practices in business management, covering management topics from Strategic Planning to Operational Excellence to Digital Transformation (view full list here). Learn how the Fortune 100 and global consulting firms do it. Improve the growth and efficiency of your organization by leveraging Flevy's library of best practice methodologies and templates. Prior to Flevy, Mark worked as an Associate at McKinsey & Co. and holds an MBA from the Booth School of Business at the University of Chicago. You can connect with Mark on LinkedIn here.

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