Five leadership challenges faced by techies and how to meet them
Learn effective strategies for techies transitioning into leadership roles, including embracing human nature and fostering team cohesion....
Brain Circuits
The dos and don’ts of Gen AI
Many companies try to use Gen AI as a hammer, looking for nails to hit – but if you don’t know what problem you’re solving, you’re building the wrong solution. Here’s how to use it – and what not to do.
1. Achieving sale
Organizations are finding it hard to use AI to achieve scale. Reasons include:
- Not doing your homework. Do you know where you want to go and how you’re going to get there? Do you have the capacity to scale this idea? Start with a clear objective in mind, rather than just asking, “So, what can we do with this Gen AI?”
- Treating scale as a one-dimensional problem. What does it mean for the whole business? What does it mean for the users of this product or service, internally and externally?
2. Education and communication
Another recurrent problem is a lack of education and communication. Everyone in the organization needs to understand what AI is and what it means for them:
- Ensure everyone knows the difference between past and future. This applies to business operations and processes.
- Communicate the benefits of change to people. If you have a new product or service with the potential to help people but don’t show them how, they won’t adopt the innovation and your efforts will be wasted.
3. Supply chain as a competitive tool
Having predictability in the supply-chain process is extremely valuable and a potential source of competitive advantage. You need to:
- Build maximum predictability into your supply chain
- Re-envisage your supply chain as a competitive tool.
4. AI at the core of business
The way to use AI successfully is to place it at the core of operations (or at the core of products and services). You need to think end-to-end:
- Ask simple questions about processes. These include issues such as: “If we hadn’t known how to do this before, how would we do it now?”
- Scale experimentation and proof-of-concepts across the organization. Ensure that people sitting close to a business problem can understand it, so the technical people will know how to experiment when looking for solutions.
Key learning
Always retain a product mindset when you’re deploying AI – you’re building products that you’re selling either to your employees or to the outside world. Keep the following issues front of mind:
- How are you going to sell it, internally and externally?
- What’s the go-to-market plan on top of building the core product?
Authors
Related
Six strategies to navigate charged DE&I discussions
Explore key techniques to keep DE&I dialogue constructive in hostile settings, including asking questions, reframing language, and knowing when to exit....
How to lead when you’re not the expert. Part 1: Internal doubt
Senior leadership is more about helping others find solutions than having all the answers yourself. Here’s how to lead when others know more than you....
Stop developing an obsolete AI strategy part 2: Enterprise risk
Following on from our Brain Circuit on the risks that can arise from your own implementation of AI, here’s how to defend against external disruption. ...