How to avoid common mistakes when adopting AI

I’ll never cease to be amazed by the Olympic runners. As someone who has logged my fair share of runs, I’m totally mesmerized by these runners’ paces. I get short of breath just watching them on my TV.

Olympic runners are worthy of our admiration. But these athletes didn’t wake up the day before the Olympics and decide to hop a flight to Paris. Their freedom to run at break-neck speed required years of discipline and training.

They had a method. They trained. Step-by-step. Day-by-day. Until, one day in Paris, they were finally able to harness this power.

This is how we should view AI.

Just like training to be expert runner, a recent Gartner® report (which you can access here complimentarily) emphasizes the importance of a measured approach. According to Gartner, “The building blocks of AI adoption are various and diverse in real life. Nevertheless, when assembled, they follow general principles that support AI progress.” Gartner mentions that “applying these principles is necessary to set realistic expectations, avoid common pitfalls, and keep AI initiatives on track.”

You can’t be in the Olympics on day one — nor do you want to be in the Olympics on day one. Growing into an AI-mature organization is about following a roadmap — a proven method — and not biting off more than you can chew.

By defining a clear strategy, communicating frequently, and setting measurable outcomes, organizations can optimize their results and avoid common pitfalls.

The Gartner phased approach to AI adoption

AI can help you classify and understand complex sets of data, automate decisions without human intervention, and generate anything from content to code by utilizing large repositories of data. However, if you underestimate the importance of getting your priorities in order first, you may be forced to learn the hard way and suffer delays and frustration.

In the report, Gartner offers an AI adoption framework where “organizations will avoid major pitfalls and maximize the chances of successful AI implementation.” Gartner tells organizations to “use the AI adoption curve to identify and achieve your goals for activities that increase AI value creation by solving business problems better, faster, at a lower cost and with greater convenience.”

Let’s look at our takeaways from these key phases.

Phase 1. Planning

Start small. Getting into peak running condition starts with short runs. Identify and recruit an internal champion to help socialize efforts and secure support from key stakeholders. Establish three to six use cases with measurable outcomes that benefit your line of business.

Phase 2. Experimentation

Practice makes perfect. Invest in the humans, processes, and technology that ease the transition between phases, such as funding a Center of Excellence (COE) and teaching practical knowledge of cloud AI APIs. Build executive awareness with realistic goals. Experiment. Break things. And don’t be afraid to change course on your strategy. Be flexible and know when to pivot!

Phase 3. Stabilization

At this point in the process, you have a basic AI governance model in place. The first AI use cases are in production, and your initial AI implementation team has working policies to mitigate risks and assure compliance. This stage is referred to as the “pivotal point” — it is all about stabilizing your plans, so you are ready to expand with additional, more complex use cases.

With strategic objectives defined, budgets in place, AI experts on hand, and technology at the ready, you can finalize an organizational structure and complete the processes for the development and deployment of AI.

Phase 4. Expansion

High costs are common at this stage of AI adoption as initial use cases prove their value and momentum builds. It’s natural to hire more staff, upskill employees, and incur infrastructure costs as the wider organization takes advantage of AI in daily operations.

Track spending and be sure to demonstrate progress against goals to learn from your efforts. Socialize outcomes with stakeholders for transparency. Remember, just like run training, it’s a process of steady improvement. Track your results, show progress, and build on your momentum. As you grow more experienced, you should expand, evolve, and optimize. Providing your organization sees measurable results, consider advancing efforts to support more high risk/high reward use cases.

Phase 5. Leadership

AI will succeed in an organization that fosters transparency, training, and shared usage of across business units, not limited to exclusive access. Build an “AI first” culture from the top down, where all workers understand the strengths and weaknesses of AI to be productive and innovate security.

Lessons from the AI graveyard

AI adoption will vary and that’s okay! Follow these steps to ensure you stay on the path most appropriate for your business. Avoid common mistakes of caving to peer pressure and focus on creating a responsible use of AI that enables you to reduce technology risks and work within the resources currently available. Here’s some advice from those that hit a speedbump or two.

1. Choose your first project carefully; most AI projects fail to deploy as projected.
2. Don’t underestimate the time it takes to deploy.
3. Ensure your team has the right skills, capacity, and experience to take advantage of AI trends.

No two AI journeys are the same

According to Gartner, “By 2025, 70% of enterprises will have operationalized AI architectures due to the rapid maturity of AI orchestration platforms.” Don’t get discouraged if you are in the 30% that may not be on that path.

Every organization will choose to adopt AI at the rate that is right for them. Some organizations consider themselves laggards, but they are learning from their peers and are taking the necessary steps to create a successful AI implementation. “By 2028, 50% of organizations will have replaced time-consuming bottom-up forecasting approaches with AI, resulting in autonomous operational, demand, and other types of planning.”

Read the complementary report to learn more about key adoption indicators and recommendations to ensure data is central to your strategy—from determining availability, to integration, access and more. This Gartner report provides hands-on, practical tips to help build confidence with tips and recommendations to help embrace the AI journey from planning to expansion.

Source: cisco.com