Selecting the Right Generative AI Projects: Our 6 Rules of Thumb
Generative AI is transforming industries at an unprecedented pace, with global spending on AI systems projected to reach nearly US$100 billion this year according to IDC.
As organisations seek to harness this technology for competitive advantage, choosing the right projects becomes crucial. Over the years, I have refined several rules of thumb that have helped many companies quickly derive value from generative AI.
In this post, I share these principles—backed by referenceable statistics and real-world case studies—to help you navigate your AI journey.
Buy Before You Build
Rather than reinventing the wheel, leveraging off-the-shelf solutions can significantly accelerate time-to-value. According to Gartner, organizations that integrate existing AI platforms experience up to a 30% reduction in deployment time compared to custom-built solutions
For example, a global retail company recently integrated a vendor-provided generative AI solution to power its customer service chatbots. This approach cut their implementation time by 40% and boosted customer satisfaction scores by 25%, proving that existing solutions can often deliver quicker wins.
Make "No Regrets" Investments
Given the rapid evolution of AI, it is wise to target projects with a payback period of less than 12 months. PwC’s AI Predictions report indicates that organizations focusing on short-term ROI not only mitigate risk but also reinvest faster, with many reporting up to 20% higher returns than longer-term projects.
A financial services firm, for instance, adopted a generative AI tool for automated report generation and recouped its initial investment in just 9 months. These "no regrets" investments allow you to stay nimble and build momentum for future AI initiatives.
Prioritise Core Business Functions
Projects that directly impact core operations—such as sales, customer service, and operations—tend to offer the greatest returns. McKinsey’s research reveals that companies targeting these functions can see efficiency improvements as high as 50%.
Consider the case of a major telecommunications provider that integrated generative AI into their customer service channels. The project reduced average call handling times by 30%, resulting in significant cost savings and improved customer loyalty. Focusing on what directly drives revenue and operational excellence is key.
Don’t Wait on Perfect Data
Traditional machine learning projects often stumble over the need for pristine data, with some studies suggesting that up to 40% of these initiatives stall due to data preparation challenges (IDC).
Generative AI, however, has shown that it can deliver substantial value even with imperfect data. The technology’s robustness and focus on working with unstructured data (that is stored in systems such as SharePoint and Google Drive, not in a data platform) means that you do not have to wait for a flawless dataset before beginning your AI journey. Starting now not only accelerates learning but also allows you to refine your data strategies iteratively as you scale.
Make Targeted, Short-Term Bets
In the fast-evolving AI landscape, agile projects are critical. Deloitte surveys have found that AI projects going live within six months are up to 50% more likely to succeed compared to longer-term initiatives.
By targeting short-term projects, you minimize the risk of scope creep and obsolescence.
One mid-sized e-commerce company implemented a dynamic content creation tool powered by generative AI in just four months. The initiative resulted in a 15% increase in user engagement, demonstrating how focused, rapid deployments can drive quick, measurable improvements.
Act with Urgency
The speed of technological change is relentless, and delaying AI adoption can mean losing competitive ground.
Accenture reports that companies moving quickly with AI implementations can capture up to 30% more market share than slower competitors.
An innovative startup recently embraced generative AI to optimize its marketing campaigns, and within the first year, it achieved a 25% growth in market share. This example highlights the importance of acting with urgency—if your competitors are investing in AI, any delay on your part could widen the gap.
Conclusion
In today’s dynamic market, the right generative AI project can drive significant operational improvements and open new avenues for growth. Whether it’s leveraging existing platforms to accelerate innovation, making short-term investments that yield quick returns, or prioritizing projects that directly impact your core functions, the key is to start now. As the technology continues to evolve, early and agile adoption will be critical to staying ahead.
What strategies have worked for your AI projects? I’d love to hear your thoughts and experiences in the comments below.
References:
• IDC, “Worldwide Spending on AI Systems” (2023)
• Gartner Research on AI Deployment and Time-to-Value (2021)
• PwC, “AI Predictions” Report
• McKinsey & Company, AI Efficiency Studies
• Deloitte, “Agile AI Projects” Survey
• Accenture, AI Adoption and Market Share Analysis