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The Human Side of AI: Preparing Your Organization for Intelligent Change
By Saranga Baruah
As organizations rush to implement AI solutions, we often focus on the technology itself—the algorithms, the models, the infrastructure. But the real challenge isn’t technical; it’s human. At our Blue Chip company, as we establish our Tech & Data Function in Multiplier’s Field Marketing Solutions services, we’re learning that successful AI adoption depends less on the sophistication of our models and more on how well we prepare our people for this transformation.
From Linear to Lateral: Rethinking How We Think
For decades, we’ve rewarded linear thinking—the ability to follow logical steps from A to B to C. AI doesn’t eliminate the need for this, but it dramatically shifts what humans need to contribute. When AI can process thousands of data points and identify patterns instantaneously, our value lies in lateral thinking: making unexpected connections, asking unconventional questions, and challenging the AI’s outputs with creative skepticism.
This isn’t about becoming “more creative” in some abstract sense. It’s about developing the muscle to see what AI misses—the context, the nuance, the human factors that don’t show up in datasets.
Cognition vs. Meta-Cognition: Understanding Our Own Thinking
Here’s where things get interesting. Meta-Cognitive Theory distinguishes between cognition (thinking) and meta-cognition (thinking about thinking). AI excels at cognition—processing information, identifying patterns, making predictions. But meta-cognition remains distinctly human territory.
This means our role shifts to monitoring, evaluating, and directing both our own thinking and the AI’s outputs. We need to ask: Why did the AI suggest this? What assumptions is it making? What might it be missing? This requires developing a new skill: the ability to work with AI as a collaborative partner rather than simply accepting its recommendations or rejecting them outright.
Theory of Mind: The Irreplaceable Human Advantage
AI can analyze sentiment, predict behavior, and even generate empathetic-sounding responses. But it fundamentally lacks Theory of Mind—the human capacity to understand that others have beliefs, desires, intentions, and perspectives different from our own.
In Field Marketing Solutions, this matters enormously. When we’re crafting campaigns, interpreting customer feedback, or navigating organizational change, we’re constantly reading between the lines, understanding unstated needs, and anticipating how different stakeholders will react. This deeply human capability becomes more valuable, not less, as AI handles more routine analysis.
The 20-Hour Rule: Building AI Competency
Research suggests that 20 hours of focused practice is enough to develop basic competency in a new skill. For AI literacy, this means our teams don’t need to become data scientists, but they do need structured exposure to:
- Understanding what AI can and cannot do
- Practicing prompt engineering and AI interaction
- Evaluating AI outputs critically
- Identifying use cases in their own work
We’re building this into our onboarding and ongoing development, treating AI literacy as essential as email proficiency once was.
The Six Thinking Hats: A Framework for AI Collaboration
Edward de Bono’s Six Thinking Hats framework offers a practical structure for working with AI. Each “hat” represents a different thinking mode:
- White Hat: Facts and information (where AI excels)
- Red Hat: Emotions and intuition (distinctly human)
- Black Hat: Critical judgment (humans evaluating AI outputs)
- Yellow Hat: Optimistic perspective (humans identifying opportunities)
- Green Hat: Creative thinking (lateral connections AI misses)
- Blue Hat: Process control (humans directing the AI’s role)
By explicitly assigning these roles in our workflows, we ensure humans contribute where they add unique value while leveraging AI where it’s superior.
SCAMPER: Creative Thinking in an AI World
The SCAMPER model (Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, Reverse) provides a structured approach to creative problem-solving. In an AI context, these prompts help us:
- Substitute: What if AI handled this task instead of humans
- Combine: How can we integrate AI insights with human judgment?
- Adapt: What AI solutions from other industries could we apply here?
- Modify: How should we adjust our processes for AI collaboration?
- Put to another use: Where else could this AI capability add value?
- Eliminate: What steps become unnecessary with AI assistance?
- Reverse: What if we flipped the AI-human workflow?
This systematic creativity ensures we’re not just automating existing processes but fundamentally rethinking how work gets done.
Governance: The Foundation of Responsible AI
Finally, none of this works without robust governance. As we build our AI environment, we’re establishing clear principles:
- Transparency: Understanding how AI makes decisions
- Accountability: Knowing who’s responsible for AI outcomes
- Fairness: Actively testing for bias and ensuring equitable impacts
- Privacy: Protecting sensitive data throughout AI workflows
- Human oversight: Maintaining meaningful human control over critical decisions
Governance isn’t about slowing innovation—it’s about enabling sustainable, responsible AI adoption that builds trust with our teams and our clients.
The Path Forward
The human side of AI isn’t a soft skills afterthought; it’s the critical success factor. As we build our Tech & Data Function in Multiplier, we’re learning that the organizations that thrive with AI won’t be those with the most sophisticated algorithms. They’ll be those that most effectively prepare their people to think differently, collaborate with intelligent systems, and contribute their irreplaceable human capabilities.
The technology is ready. The question is: are we?