AI is no longer an option for enterprises now. Sooner than later, businesses need to get onto the AI journey. For those who are a little late to board the AI bus, can they successfully leapfrog their way through?

Leapfrogging AI journey for impact

 

All Aboard for AI?

The buzz over AI has grown to such an extent that non-tech businesses are thinking about how to bring it on board in their organizations. But while excitement surrounding AI is high, it comes with a generous side dish of confusion in the C-suite: How should we adopt AI? What will it really do for us? Is it the right choice for our needs?.

And while adopting AI in the enterprise is not as difficult as you might imagine, it does come with its own challenges.

 

Hurdles to AI Adoption

Like any new technology, AI presents decision-makers with some perplexing questions:

  • What impact will this have on our current technology infrastructure? What additional technologies will we need to add?
  • Who should be responsible for overseeing AI’s implementation in our organization?
  • How much will this cost in the short-term? And in the long-term?
  • What adjustments in mindset and processes will we need to make?
  • What additional skill sets will we need to acquire?

And, of course, the big question: What impact will AI have on our business?
Realizing that comparatively few organizations have fully adopted AI (throughout their organization, not just in limited trials or select areas) should also help leaders to realize that these are very common issues. When we see organizations that have succeeded in integrating AI into their business, we can conclude that concrete answers to such questions exist. Let’s consider some ways that you can prepare your organization for AI – and get a head start on your competitors.

 

11 Ways You Can Make the Race to AI Easier

No matter where you are in your AI journey, the following guidelines can help make the transition easier:

  1. Have an AI plan. Consider the questions posed in the previous section. Having the answers will help your organization be better equipped to start your AI journey. Some companies struggle with implementing AI because they don’t take the time to first nail down their expectations, plans, and data needs.
  2. Adopt an AI-Driven Mindset. In other words, prepare the people, not just the technology. Make sure that users and decision-makers understand the benefits and potential pitfalls (yes, AI has them). Many organizations find that the key challenge in adopting AI isn’t the technology; it’s getting people ready to work with a new technology. This may require ongoing reskilling for employees as well as a shift in managerial thought that prioritizes effective decision-making. Fostering a culture of learning and improvement is essential. If AI isn’t your area of expertise, consider getting some experts in to answer questions and help train users.
  3. Build Your Digital Capabilities. This includes your current computing infrastructure, which may not be up to the challenge of AI’s resource-intensive needs. Many companies ultimately decide to use a Cloud-based infrastructure for part or all of their new requirements. Consider this as both an investment in digitalization and as part of your AI investment; AI itself is not terribly expensive. And the return on an AI investment is usually about the same as that for Big Data or advanced analytics.
  4. AI Strategy = Data Strategy. For AI to unlock your hidden insights – and return those results you’ve read about – you need to have the right data strategies in place. Remember, AI is only as good as the data going into it and the humans who program it. Without a data-sourcing strategy, you’ll be left scrambling for an essential part of AI.
  5. Explore the Facets of AI and Assess Their Relevance. AI has many sub-disciplines, including deep learning, natural language processing, and computer vision. These have various areas of application and are in various stages of maturity; while you probably don’t need all of them right now, it’s a good idea to keep an eye on their progress and consider whether they’ll meet any future use cases. It’s also wise to identify where these technologies can meet (or cause) future skill gaps.
  6. Identify What You Actually Need. In a related point, don’t get seduced by the buzziest, shiniest aspects of AI. For example, machine learning can do amazing things, but it’s not needed for every single project. Focus on creating value with AI, not on building the most powerful solution possible. While you do need to keep future needs in mind, overkill is not the best solution.
  7. Divide Your Journey. It’s okay to start small, using AI in one application, and then expand. There are a wide variety of AI tools that you can use to prove its worth. Consider implementing AI in a three-stage process:
    • Short-Range Goal: Find use cases that can be met with proven AI solutions (e.g. a specific tool). Scale this application across the enterprise as its value becomes apparent.
    • Medium-Range Goal: Consider which emerging technologies can be applied to specific needs and run these in limited business cases to demonstrate value.
    • Long-Range Goal: Use experts or a third-party AI vendor to apply cutting-edge tech to important or innovative problems, thus securing an early-adopter competitive advantage.
  8. Always Think Scalable. Once you’ve run a few pilots and can verify AI’s potential impact on your organization, start thinking about enterprise-level applications. Usually, it’s best to start with revenue-generating applications, followed by cost- and time/labor-saving ones. We’ve often said in this blog that AI is not one-and-done; it’s a long-term project, and one that will always require you to be thinking a step ahead of where you are today. Companies that stay just in front of the trend reap the biggest rewards, and this is especially true of AI; it means saving time and money on future deployments and initiatives.
  9. Get the Analytics Unit Involved. If you already have an analytics unit, you have a head start in deploying AI. Enterprise-level AI is fairly similar to what your analytics team has already been doing; this can be a good place to start your AI journey. It might be as simple as expanding some of your current analytics endeavors.
  10. Consider an AI Partner: Even tech giants (and AI frontrunners) like Amazon and Google have partnered with other companies and experts to fine-tune their AI. If your company doesn’t have the personnel, budget, skills, or experience to have its own AI unit, turn to an AI vendor or partner. They’ll have tools and consultants that will help you reach your goals.
  11. Opt for Broad Leadership. AI needs more than just technical foundations to succeed; it also needs business context and understanding. So, don’t put AI leadership all in the tech court; get business leaders involved as well. This will ensure you’re focusing on the most valuable applications for AI and also that you’re not missing out on some vital input.

 

Get Ready, Get Set … Get On Board with AI

While any change comes with its own process, you can certainly prepare yourself to jump ahead on your AI journey. This calls for a mix of boldness and insight. In many cases, adopting AI ahead of other companies has allowed organizations to weather disruptions and reap greater advantages than their competition. Be willing to adapt, developing new business models and building a robust, digitally focused growth path. By being proactive, you’ll avoid behind the pace of technical progress.
Even though other companies have yet to make their move into AI, there’s no good reason to delay. AI has entered the business world to stay, and organizations that recognize that fact and start preparing now to embrace it will be better placed for future growth.

Authored by Anil KaulCo-founder and CEO of Absolutdata

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