Choosing the right AI projects is harder than it looks
Global spending on AI systems is projected to reach nearly US$100 billion this year, according to IDC. With that level of investment, the pressure to pick the right projects has never been higher. Yet many organisations still struggle with where to begin.
Over the years, we have refined several rules of thumb that have helped companies quickly derive value from generative AI. These principles are backed by industry research and real-world case studies, and they offer a practical starting point for teams navigating their AI journey.
Rule 1: Buy Before You Build
Rather than reinventing the wheel, leveraging off-the-shelf solutions can significantly accelerate time-to-value. Gartner research indicates that organisations integrating existing AI platforms experience up to a 30% reduction in deployment time compared to custom-built solutions.
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%, demonstrating that existing solutions can often deliver faster wins.
This aligns with our broader philosophy at Hypergen: always start with the simplest effective solution. If a platform already solves 80% of the problem, build the remaining 20% rather than starting from scratch.
Rule 2: 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 organisations 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 help you stay nimble and build momentum for future AI initiatives.
Rule 3: Prioritise Core Business Functions
Projects that directly impact core operations, such as sales, customer service, and operations, tend to offer the greatest returns. McKinsey 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.
Rule 4: Don't Wait on Perfect Data
Traditional machine learning projects often stumble over the need for pristine data. Some studies suggest 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 (stored in systems such as SharePoint and Google Drive, not in a data platform) means that organisations do not have to wait for a flawless dataset before beginning. Starting now not only accelerates learning but also allows data strategies to be refined iteratively as initiatives scale.
Rule 5: 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, teams minimise 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.
This is exactly why our AI Accelerate workshop is structured around compressed timeframes. Organisations identify high-potential use cases and build working prototypes in days, not months, keeping momentum high and risk low.
Rule 6: 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 optimise its marketing campaigns, and within the first year, it achieved a 25% growth in market share. If competitors are investing in AI, any delay risks widening the gap.
For organisations looking to move quickly and strategically, a structured approach like the Beachhead, Boost, Breakthrough framework can help prioritise initiatives that deliver value at each stage of maturity.
Putting It into Practice
In today's market, the right generative AI project can drive significant operational improvements and open new avenues for growth. Whether it is leveraging existing platforms to accelerate innovation, making short-term investments that yield quick returns, or prioritising projects that directly impact core functions, the key is to start now. As the technology continues to evolve, early and agile adoption will be critical to staying ahead.
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
Ready to Identify Your Next AI Project?
Selecting the right generative AI projects requires balancing ambition with pragmatism. If your team is weighing up where to invest, we can help you assess your options, prioritise high-value use cases, and move from ideas to working solutions quickly.