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Deploying AI Requires Understanding What’s Both Possible and Practical

Deploying AI

THE vast majority of today’s business leaders are either embarking on AI deployment to improve their operations or are considering it. Some 83 percent of organizations worldwide claim AI is a top priority for their business. The expansive growth of AI isn’t just a trend but a fundamental shift to the business ecosystem. The simple truth is if you don’t update your business processes to reap the rewards of growth, quality, or both that AI brings, your competitor will. It’s only a matter of time.

Although most C-suite executives, mid-level managers, and data practitioners aren’t AI experts — no one is at this pace of change — they shouldn’t implement AI for the sake of implementing AI. Deploying AI requires a clear understanding of what’s both possible and practical.

The business case for AI is that it can help you accelerate, facilitate, and amplify workloads and processes with better consistency and quality.

Consider these findings:

  • AI has proven to improve the productivity of 61 percent of employees. For example, agents who use AI can handle nearly 14 percent more customer inquiries per hour.
  • Some 54 percent of organizations say AI has been cost-effective for their business operations. Netflix, for example, claims to have saved more than $1 billion annually using machine learning.
  • Organizations can do more with less. They are now working smarter, not harder.

Just as email transformed communication and iPhones and apps created widespread connectivity, AI is reshaping everything from healthcare to education to manufacturing to travel. In time, no industry will remain untouched. The surge of AI can be attributed to a convergence of three key factors: ground-breaking algorithms, data explosion, and enhanced computing power. Together, they’ve created a tipping point for AI.

Singling out just one segment of industry, consider some of the ways retailers are putting AI to use: Every interaction online is analyzed to better understand customer’s shopping patterns, click-throughs, and page views. The system deterministically maps their preferences in order to provide relevant product recommendations. Chatbots provide customer service 24/7 without the need for human staff. Meanwhile, Crawlers and Bots scour the web and analyze customer reviews, competition, and social sentiment. This information guides retailers to align their offerings with emerging trends.

Embarking on your own AI journey

While your goal is to be innovative, relevant, competitive, and forward-looking, your approach should be to bend but not break your team and your operations. In order to be smart in your approach to an AI project, start with a strategy that you can realistically deploy.

Use the principle of “thinking big, starting small, and scaling quickly.” I learned this principle when helping clients brainstorm how best to leverage AI within their operations. I quickly came to recognize that executives and stakeholders knew how to think big, but they also wanted to start big without thinking through the risk implications.

  1. Thinking big - This is all about the art of the possible — embracing opportunities and imagining the possibilities. Envision the possibilities for your business and uncover ways to enhance them as you reclaim valuable time, maximize savings, and channel energy toward tasks and activities that give back. Don’t let restraints, resources, and funding hold you back. Just think about possibilities.
  2. Starting small - Imagine you’re a budding gardener testing your green thumb. Would you start with landscaping and planting an entire garden or nurturing a small spot first? This strategy is all about being realistic with what’s feasible and considering the practicalities of your business landscape to minimize risks, squandered money, and wasted time. By starting small, a use case that suits your organizational maturity and recourse, you gain firsthand experience with the processes, learn valuable lessons, and set yourself up for success on a grander scale.
  3. Scaling quickly - In the pursuit of innovation, thinking big is about laying the foundation of bigger things, starting small is about mitigating risk by only biting off what you can chew, and scaling quickly is about commitment. When transitioning to scaling, avoid analysis paralysis at all costs. As leaders, being decisive is paramount. After you’ve evaluated the output, looked at the data, and measured the business benefits, make the decision to either productionize your Proof of Concept into full-scale production, or pivot towards a new use case. This time period is critical as it signals your intentions to your team. The objective is to avoid decisional stagnation in pursuit of rapid and informed action.

AI isn’t just a trend but a fundamental shift touching every aspect of our lives and the broader business ecosystem. If you haven’t already, it’s time to explore how to embark on your AI journey that will catapult your organization toward a promising future.

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Leading Forum
Sol Rashidi is an esteemed executive, leader, and influencer within the AI, data, and technology space, having helped IBM launch Watson back in 2011, and has been doing “hands-on” AI deployments since. She’s held numerous C-Suite roles and has been named 50 Most Powerful Women in Tech, Top 100 Innovators in Data & Analytics, CAO of the Year, CDO of the year, Global Data Power Women, and holds eight patents. Her new book The AI Survival Guide: Scraped Knees, Bruised Elbows, and Lessons from Real World AI Deployments made it to the bestseller list in less than one month. Learn more at solrashidi.com.

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