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9 Steps to a Successful AI Adoption Strategy

Sudhakar Nagasampagi

Sudhakar Nagasampagi

9 Steps to a Successful AI Adoption Strategy for Organizations

Artificial Intelligence (AI) technologies are constantly evolving and so are the possibilities. Organizations should capitalize on the transformative power of AI as early as possible, else they face the risk of being left behind.

Steps to a Successful AI Adoption Strategy 

To make an AI project succeed, you need a clear long-term strategy. Enabling AI cannot be accomplished overnight. Going all-in or rushing into the process is certainly not a recommended approach. Here are 9 steps to realize a successful AI adoption for your organization:

Understand What AI Is and What AI Is Not

The first step for CxO’s and senior executives is to increase their awareness and knowledge about AI terminology and its capabilities. A clear understanding of the AI paradigm and its use cases will help executives in identifying the possible applications and ways of implementation that will bring actual business value to the enterprise. Equally important is to know what you cannot achieve with AI.

Identify and Analyze Current Business Problems

Leaders will need to prioritize the use cases for the business – whether to enhance customer services or enable the growth of products, to automate labor-intensive tasks or improve employee/workplace productivity and so on. Applications and projects should be undertaken not because they use AI, but rather to solve a business problem. Most executives are still unable to recognize business problems that can potentially be solved with AI. Even if they understand the significance of AI, they need to figure out how it works and benefits the organization. 

Ensure Leadership Buy-In at Every Phase

Leaders across the organization need to be appraised about the benefits, risks, likely investments, and ROI of AI transformation. Only if they are convinced, can they effectively communicate the AI strategy across the workforce resulting in a faster and smoother acceptance and implementation. Additionally, they need to propagate the fact that AI augments staff, not replaces it.

Adopt a Strong Data-Driven Culture

Data excellence is essential for any AI strategy because everything starts and ends with ‘data’. The quality of data that ‘trains’ and fuels AI algorithms determines the quality of the AI implementation. Data labeling should be performed accurately, else AI can be misinterpreted and deliver incorrect results. All data sources and data growth are key factors in an AI strategy.

Interact With People From the Industry or Like-Minded Organizations

Identify sources that have developed and implemented similar applications and get a realistic understanding of what it would take to implement them yourself.  Check out if something similar has been done and note how long it took to adopt AI successfully. What kind of in-house/partner expertise or outsourcing did it take? What pitfalls were avoided? Note the lessons learned in the process.

Decide In-House Development vs Outsourcing

Most enterprises today lack the in-house expertise to create, deploy and manage AI technologies suitable for the organization. Technology areas like Machine Learning, Deep Learning, Robotics, etc. need to be clearly identified. Organizations should evaluate whether to develop in-house or buy or outsource AI solutions. Besides human resources, organizations should also analyze whether they have the appropriate infrastructures (on-premise / cloud), algorithms and visualization tools.

Think Big, Start Small, and Scale Fast

For organizations, the best approach to a successful AI adoption is to have

  • a long-term vision,
  • a comprehensive and holistic strategy,
  • extensively but inexpensive tested prototype(s),
  • a couple of pilot projects focused on solving small specific business problems and

then applying the experience to form solutions for bigger and more complex tasks.

Be Cognizant of Ethical Concerns

To become successful at AI adoption, organizations and businesses will have to complement technology with proper governance, ethics, and trust. For example,

  • Data must be highly accurate, free from bias (age, gender, race, etc.), and used in compliance with regulations such as HIPAA, GDPR, ISO, etc.
  • Privacy of information to be respected — data concerning individuals, customers or that of organizations not exploited for commercial or political gain.
  • Algorithms and logic should be transparent in order to inspire trust.
  • Responsibility or critical decisions should always be handled by a human and not a robot.
  • Organizations must re-educate the workforce where job loss is imminent due to automation, and to encourage employees to acquire new skills for the AI-era.
  • The use of AI should not pose a threat to the safety and very existence of humanity.

Realize That Innovation Steers You Ahead of the Competition

More profits can be derived by optimizing processes (as your competition is likely doing right now), but to stay ahead of the competition, it is important to do different things or do things differently from the competition. Your AI strategy and more so, your business strategy, should be unique and different.

Remember AI Is Not a Magic Ingredient

Do not get carried away by the hype and some of the (un)realistic promises that AI portrays. There is nothing magical about AI. Jumping blindly on the AI bandwagon (because others are following the norm), is definitely a detrimental approach. Every aspect of AI that you would like to implement in your organization must be understood, evaluated, trained and measured clearly, just as you would with any other technology or business.

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Sudhakar Nagasampagi

About the Author

Security Specialty Trainer; AWS Architect; CCC Master Trainer, Author of CTA, CTA+ & IoT; and Accredited DASA DevOps Trainer with 25+ years of IT experience.

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