When to leverage AI
In today's fast-moving business world, the real question isn't whether to use AI, but when it's the right choice. AI can reshape how you operate, improve customer experiences, and spark growth in almost any industry.
Equally important is knowing when not to use AI, as highlighted by Jeroen Baert, a keynote speaker at a recent AI event of ours.
Let’s break down when AI makes sense, when human expertise should take the lead, and practical tips you can use to drive growth effectively.
The foundation of AI success.
Jeroen’s key message was simple but powerful: “Shit stays shit.” No matter how advanced your AI system is, if you feed it bad data, you’ll get bad results. He illustrated this with a story about an AI model that was trained to differentiate between wolves and huskies.
The catch? The model wasn’t learning the differences between the animals at all, it was just recognizing if there was snow in the background. The lesson is clear: AI only works if your data is clean, accurate, and relevant. Before diving into AI, make sure your data meets these standards.
AI data quality is critical for businesses because it forms the backbone of successful AI implementations. High-quality data ensures that AI models are accurate, reliable, and capable of delivering actionable insights. When data is incomplete, outdated, or biased, AI outcomes can be misleading, leading to poor decisions, financial losses, or missed opportunities.
For example, in manufacturing, accurate and clean data helps in predictive maintenance. By ensuring data quality, AI models can precisely forecast machine breakdowns, leading to a reduction in downtime by up to 30% and saving significant operational costs.
When to use AI for business.
Process automation for improved efficiency.
Why: Automating repetitive tasks with AI can lead to increased efficiency, cost savings, and resource optimization in manufacturing and industrial sectors.
Example: In a manufacturing setup, AI can automate quality checks using computer vision, ensuring consistent product standards and reducing manual inspection times, which improves overall efficiency by up to 30%.
Predictive analytics to optimize innovation and operations.
Why: AI’s predictive analytics can be pivotal in identifying trends, anticipating market demands, and optimizing operational processes.
Example: AI can predict maintenance needs in industrial machinery, allowing operational managers to schedule proactive maintenance and reduce unexpected downtimes by 25%, which in turn improves the company’s ROI.
Personalized customer experience in product design.
Why: AI enables the creation of highly personalized products and services based on customer insights. This approach drives innovation and helps businesses achieve market differentiation.
Example: In consumer industries, AI can be used to gather insights from customer feedback and usage data, enhancing customer satisfaction and loyalty.
Data-driven decision-making to enhance product leadership.
Why: Data-driven insights empower businesses to refine and tailor products, even for niche markets. Synthetic panels simulate real-world responses based on industry-specific data, providing valuable feedback when a large audience is inaccessible.
Example: When Made is helping a manufacturing client validate a new AI-driven predictive maintenance tool, synthetic panels can simulate the reactions and preferences of maintenance managers, reflecting their operational challenges and workflows. This approach allows the client to refine the tool’s features and usability, ensuring it aligns with market needs before a broader rollout.
Leveraging AI tools for product sketching and prototyping.
Why: AI tools streamline product design by automating routine tasks, suggesting real-time design tweaks, and enabling rapid prototyping. This accelerates design while allowing designers to experiment with innovative ideas
Example: In consumer product design, Made can leverage AI-powered design tools to generate multiple product sketches based on initial inputs and preferences. For instance, when designing a new ergonomic chair, AI can quickly create variations of the chair’s structure, materials, and aesthetics. Designers can then refine these AI-generated sketches, reducing the time spent on manual iterations by up to 50%, and focusing their efforts on perfecting the design details that matter most to end-users.
When not to use AI for business.
Lack of high-quality, industry-specific data.
Why: AI models need accurate and relevant data to function effectively. Without high-quality or industry-specific data, implementing AI could lead to inaccurate insights or decisions, which can be damaging in highly regulated sectors like healthcare or manufacturing.
Example: In healthcare, attempting to implement AI for patient diagnosis without comprehensive, accurate datasets can lead to incorrect medical decisions, undermining both patient trust and regulatory compliance.
High costs without clear value proposition.
Why: Implementing AI requires significant investment in training, tools, and maintenance, and businesses must be sure it will yield clear benefits. For some projects, traditional approaches may offer better value.
Example: For a client with budget constraints in the manufacturing industry, implementing AI for predictive maintenance without clear ROI metrics might result in higher costs with minimal immediate impact.
Challenges requiring human creativity and empathy.
Why: AI lacks the human touch, which is often crucial in marketing, design, and client relationships areas where Made excels in creating innovative and impactful solutions.
Example: In projects that require deep understanding and empathy, such as user-centered product design for healthcare or consumer industries, over-reliance on AI could miss critical emotional or human-centric elements.
Complex integration with legacy systems.
Why: Businesses with entrenched legacy systems may face operational disruption when integrating AI solutions.
Example: Implementing AI-driven production optimization in a traditional industrial setup could require significant infrastructure changes and staff training, which may not align with every client’s readiness or budget.
Regulatory compliance concerns.
Why: Industries like healthcare and manufacturing are heavily regulated. Implementing AI without strict adherence to these regulations can result in legal risks.
Example: In the medical device sector, using AI for diagnostics without thorough validation against regulatory requirements could lead to compliance violations, legal issues, and potentially harm patients.
If you’re curious about how AI can drive personalized growth for your business, let’s chat.
