Today organizations are rushing to incorporate AI into their workflows, and it is top of list in most strategy discussions. AI can be a game-changer, offering valuable insights, automating tasks, generating content, and creating new opportunities. However, the landscape is littered with AI initiatives that never progress beyond a concept or pilot that never sees real-world deployment. Why does this happen? It often occurs when organizations pursue “AI” for its own sake, rather than viewing it as a means to an end.
AI can be a potent tool capable of delivering sustainable value, but you need to ensure you have the right focus. Instead of beginning with the idea of implementing “AI”, let’s first consider where within your organization you can create more value for your customers or internal stakeholders. If your organization is driven by a commitment to value creation and problem-solving, you’ll naturally identify areas where AI can propel your business forward.
Once you’ve pinpointed where AI can generate value, and you are forming teams to develop AI algorithms and tools, there are key things to consider. In our experience these are 3 critical considerations when adopting AI to ensure you will get long lasting value;
1. Data Management: The Lifeblood of AI
The quality of AI’s output is significantly linked to the data it uses. Ask yourself: Do you possess the necessary data, or can you obtain it?
High-quality, relevant data serves as the lifeblood of AI systems, underpinning informed predictions and decisions. It’s fuel that propels the engine of AI innovation.
For every data scientist within your AI team, be prepared to assemble a team of data engineers. This team ensures not only data access but also meticulous data management and quality control.
2. Model Maintenance: Ensuring Long-Term Viability
Consider whether your organization possesses the capability to maintain the AI model effectively or if external service providers will be required for quality control and ongoing maintenance.
If the challenges of maintenance and accessibility begin to loom large, it’s a sign that you should pause and reassess the project’s Return on Investment (ROI) and its relevance to your organization’s overarching goals.
3. Realistic Expectations: Understanding AI’s Capabilities
Understanding the capabilities and limitations of your chosen AI tool is paramount. For instance, take tools like ChatGPT, which excel at generating text and interpreting human language within a given context. They provide probable responses, not absolute answers.
Using AI as a source of truth rather than merely a language model can lead to ill-considered decisions. It’s essential to understand the boundaries of its capabilities.
If any of these considerations present a challenge to you, you should consider these potential investments in parallel to driving your AI initiatives:
Data Accessibility: Access to the right data is a must. If you don’t have good data accessibility within your organization explore initiatives that open the door to essential data access. You will need to understand and solve your architectural constraints before you can scale AI.
Financial and Technical Sustainability: Evaluate the financial feasibility and the availability of technical support for various initiatives. This assessment will help determine what aligns best with your organization’s capabilities and long-term objectives. AI models need long term technical and financial investment to ensure they continue to add value over time.
Understanding the Tool: Make it a priority to enhance your organization’s understanding of AI and automation technologies. This investment ensures alignment with your overarching objectives, allowing you to harness AI as the value-driving tool it can be.
By maintaining a keen focus on these core considerations, you’ll navigate the AI landscape with clarity, ensuring that AI serves as a catalyst for value creation rather than an end in itself.
Approaching AI as a tool within your value-creation toolbox enables you to align your efforts with genuine business objectives. It’s not about chasing the AI trend; it’s about solving a real-world problem and enhancing your organization’s capabilities. So, begin with value, not with AI, and you’ll unlock the true potential of this transformative technology.