When it comes to adopting AI, not every business needs to reinvent the wheel or start from zero. Instead, leaders should take a step back and ask: Where does my organisation stand today?
The reality is businesses exist across a spectrum of AI readiness. Some are just starting to explore the concept of AI, while others are already using it to drive innovation and strategy. The key to success is to evaluate your current stage and take targeted actions to move forward.
Understanding your current level of AI maturity is the first and most important step. This approach saves time, money, and effort by aligning your AI strategy with your actual capabilities and business needs.
The five phases of AI readiness
Let’s break down the Enterprise AI Readiness Framework into simple terms and real-world scenarios. Each phase comes with specific goals and examples to guide your journey.
- Awareness: “What is AI, and why should we care?”
This is the starting point for many businesses. At this stage, the goal is to build awareness of AI and how it could apply to your industry. Educate leadership through workshops and seminars. Research potential AI use cases for your organisation. Identify areas where AI could solve real business problems. Studies show that 60 per cent of organisations are still in this early phase, with no formal AI initiatives in place.
Example: A manufacturing company exploring AI might learn that predictive maintenance can reduce downtime by 20-30 per cent, saving millions annually. But they first need to understand the basics of how AI works.
- Exploration: “Let’s test the waters with small projects”
Here, businesses start experimenting with small-scale AI projects. These are low-risk, low-cost pilots that demonstrate the potential of AI. Form a small AI team (e.g., one data scientist, one engineer). Test off-the-shelf AI tools and analyse pilot results. Gartner reports that 25 per cent of companies in this phase see measurable returns within six months of starting AI pilots.
Example: A retail company might pilot an AI tool to predict inventory needs. By analysing past sales data, they avoid overstocking, saving US$100,000 in a single quarter.
- Operationalisation: “Let’s formalise AI across the organisation”
At this stage, businesses move from pilots to building infrastructure for scalable AI adoption. This includes setting up governance, ensuring data privacy, and deploying AI in real-world use cases. Establish an AI Center of Excellence (CoE), build scalable data platforms (e.g., data lakes), and create governance policies for compliance. According to McKinsey, businesses in this phase see a 20 per cent improvement in operational efficiency.
Example: A healthcare provider adopts AI to analyse patient data, reducing diagnosis times by 30 per cent. They build a centralised platform to ensure all AI models meet regulatory requirements.
- Proficient: “AI is part of how we work”
Now, AI becomes integrated into daily operations across the organisation. Advanced monitoring systems ensure models stay accurate, and employees are trained to use AI tools effectively. Scale AI solutions across departments. Train employees to use AI in their roles. Monitor models for performance and fairness. Proficient organisations report a 30-50 per cent increase in productivity across functions using AI.
Example: An e-commerce company uses AI to personalise customer experiences, increasing average order value by 15 per cent. AI tools also optimise warehouse operations, cutting costs by 10 per cent.
- Leader: “AI drives everything we do.”
This is the ultimate level of AI maturity. Businesses here use AI as a core driver of strategy, innovation, and operations. Use cutting-edge AI techniques like generative AI and autonomous systems. Foster an AI-first culture with continuous employee upskilling. Only 10 per cent of organisations globally are at this stage, but they account for 70 per cent of all economic gains from AI.
Example: Tesla uses AI not only in its cars but also to optimise factory production, cutting manufacturing costs by 25 per cent. AI also drives innovation in R&D, creating entirely new product categories.
Why starting with assessment matters
Imagine this: You wouldn’t buy a Formula 1 car if you’ve only just learned how to drive. Similarly, jumping straight into advanced AI tools without the right foundation can lead to wasted investments. A survey by MIT found that 85 per cent of AI projects fail—not because AI doesn’t work, but because organisations weren’t ready for it.
Also Read: COVID-19, the environment, and the tech ecosystem: what opportunity is available out there for us?
By assessing your current maturity level, you can focus on actions that deliver real value. For example:
- Awareness phase: Focus on leadership buy-in and identifying high-impact use cases
- Exploration phase: Invest in small, measurable pilots to prove AI’s value
- Proficient phase: Scale AI efforts strategically, focusing on ROI
Practical takeaways for leaders
- Start small: If you’re in the early stages, start with one pilot project. For instance, try using AI to automate customer service through chatbots
- Measure results: Document your wins and challenges. Did your pilot reduce costs or improve efficiency? Use these insights to build momentum
- Think long-term: Advanced AI maturity doesn’t happen overnight. Focus on sustainable growth by investing in talent, infrastructure, and governance
The roadmap to AI success
So, where does your organisation stand today? Assess your readiness, align your strategy, and take the next step toward unlocking AI’s transformative potential. Remember, AI isn’t a destination—it’s a journey. And every journey starts with knowing where you are.
—
Editor’s note: e27 aims to foster thought leadership by publishing views from the community. Share your opinion by submitting an article, video, podcast, or infographic.
Join us on Instagram, Facebook, X, and LinkedIn to stay connected.
Image courtesy: Canva Pro
The post 85% of AI projects fail: Here’s how to make yours succeed appeared first on e27.