Innovation Bre8k: 1/27 - 1/31/25
Human-Centric AI, Sustainability Tips for AI Builders, Workforce, and a Bit of a Rant
Every week-ish Anim8 Collective curates insights into business growth and strategy, emerging technology, leadership and change management, networking and collaboration, and sustainability. Take an Innovation Bre8k with these top picks!
Recently we covered:
🚀 AI is Shaping Our World - But is it Doing so for Everyone? 🌍
💡 Top Resources for Building AI with Sustainability in Mind
🤖 How Can Leaders Prepare Their Workforce for the Generative AI Era?
✨ Digital Transformation Is a Journey - But These Days With AI It’s Feeling Like a Sudden Leap
📽️ Video Coming Soon: What is the #1 Reason my Business is Turning Around Right Now?
This post is written from the perspective of Anim8 Collective founder, Sarah O’Sell’s experiences.
🚀 AI is Shaping Our World - But is it Doing so for Everyone? 🌍
In the fast-paced AI era, fairness and inclusion aren't just buzzwords, they're necessities. The World Economic Forum highlights a critical truth: AI has the potential to benefit every corner of society, but only if it is built on inclusive principles.
👉 Read Article Here
🌟 Characteristics of Human-Centric AI
Purpose-Driven AI: It’s not just about efficiency but ensuring equitable access to the benefits of AI advancements.
Diverse Voices Matter: A lack of diversity in AI development leads to biases that harm underrepresented communities.
Human-Centric Innovation: Prioritizing ethics and societal impact ensures AI aligns with humanity’s best interests.
💡 The future of AI should reflect the diverse fabric of our world. Inclusivity is not optional, it’s essential.
What’s your role in this transformation?
Support diverse voices in tech.
Advocate for ethical AI policies.
Design AI systems with fairness at their core.
Others to Follow: Sinead Bovell, All Tech Is Human, World Information Architecture Day, Interaction Design Foundation
💡 Top Resources for Building AI with Sustainability in Mind
Across the tech ecosystem, experts are championing strategies to create energy-efficient and sustainable AI systems. Here are the top resources we found ahead of our event last week.
🌟 Key Strategies for AI Sustainability:
1️⃣ Optimize Model Design
Simplify models using pruning and quantization to reduce energy use. These tactics help reduce model size by removing unnecessary parameters and tuning the precision of data for the outcomes needed, also beneficial for moving AI to the edge! (via EY Insights)
Build AI that’s sustainable by design with renewable-powered data center operations infrastructure. (via Microsoft)
2️⃣ Energy-Efficient Training
Use transfer learning or the reuse of a pre-trained model to save computational resources. Implement early stopping techniques, halting the model at peak performance, to avoid unnecessary computation on "noise" or generated data. (via AWS)
Solutions exist to help reduce the energy demands of data centers by 10-20%. (via MIT Sloan)
3️⃣ Greener Deployment
Leverage efficient cloud and data center practices to balance AI’s growth with sustainability. (via Brown Advisory)
AI in smart building automation reduces energy usage while enhancing performance. (via TIME)4️⃣ Broader Environmental Considerations
Understand AI's water consumption to assess its true resource footprint. (via OECD AI)
Explore the socio-environmental impacts & regulation pathways for emerging AI systems. (via Nature)
Work with and listen to stakeholders at the planning phase (via TIME)5️⃣ Interaction Design for Resource-Efficient User Experience
Freeform inputs open opportunities for discovery, however, without prompt structuring to guide outputs... hallucinations, incorrect responses, and straying from established processes are all common resource waste experiences in these systems. (Anim8 Collective's experience)
🌱 Why It Matters:
If we don't address the environmental costs of AI now, we risk limiting its potential for global good. With these insights, businesses and developers can lead the way toward a sustainable AI future.
🤖 How Can Leaders Prepare Their Workforce for the Generative AI Era?
In today’s fast-evolving tech landscape, upskilling and reskilling are no longer optional, they're critical.
👉 Read Article by McKinsey Here
🌟 Roadmap for Talent Development in the AI Era:
1️⃣ Invest in Digital Literacy:
Ensure employees understand generative AI basics and its applications in their roles.2️⃣ Prioritize Flexible Learning Paths:
Leverage AI-driven training platforms to personalize learning experiences and meet employees where they are.3️⃣ Redefine Leadership Development:
Equip leaders with tools to drive innovation, foster collaboration, and navigate AI-related disruptions.4️⃣ Adopt a Skills-Based Hiring Approach:
Focus on competencies rather than traditional job roles to build agile, adaptable teams.5️⃣ Create an AI-Native Culture:
Empower teams to experiment with AI tools, fostering confidence and creativity in problem-solving.
🚀 Organizations that embrace upskilling will not only future-proof their workforce but also thrive in the generative AI era.
How are you supporting your teams in adapting to AI-driven change?
I recommend courses in Data Science and strategy for AI adoption. Having a tool is one thing, smart applications through systems thinking are where you scale.
✨ Digital Transformation Is a Journey - But These Days With AI It’s Feeling Like a Sudden Leap
Last week at our monthly Innovation Bre8k event, someone asked for a quick overview of the evolution of AI. I wasn’t satisfied with the answers the panelists and I gave and I want to share with those who are starting their learning! This article by Aparna T.A. includes the clearest, most concise diagram I’ve found to explain it.
🧠 What’s changed, and what hasn’t? (personal opinions added)
The evolution of technology from desktop computers to the Internet and SaaS platforms took decades. But with AI, we’re seeing seismic shifts in just a few years.
Data Access and Analytics:
Tools like ChatGPT draw on vast digital records (e.g., the web) and perform analytics to infer insights. This has been something humans who love research (like myself) have performed until recently.AI-Driven Workflows:
Automation tools enable users to create structured processes with human checkpoints. Again, this is something people who love building systems, templatizing processes, and using design thinking to define experiences have done. It's still something humans are excellent at because we understand other humans, but there is now a layer of AI-sourcing templates for you. People who have not historically been great at this can now ask and receive what they need to do things they have never done before. Upskilling is no longer about acquiring vertical knowledge or best practices, it is about the ability to rapidly automate your own processes and absorb new ones in all directions.Personalized AI Experiences:
AI creates adaptive, tailored experiences for individuals, reshaping customer interactions. People who were previously doing all of the above are now structuring systems at this level, walking the fine line of delivering magic or being too creepy with the "knowing."AI Systems of Agents:
We’re now witnessing AI systems that collaborate, learn, and evolve autonomously over time. We're all afraid of this layer, and tech leaders are creating a PR nightmare for their future selves by boasting about the elimination of work without social systems or pro-social leaders being in place to mitigate the burden of this transition. It's already impacting the job market and there is a mega-fissure forming in society.
🌟 Why does this matter?
AI isn’t just a shiny, new tool, it’s traveling the journey of digital transformation in hyper-drive. The same principles driving past innovations apply here but at warp speed. In just 5 years, AI has pushed us further than decades of desktop, web app, and SaaS evolution combined.
