Turning AI Complexity Into a Clear Business Advantage – Exclusive Interview with Jeremiah Johnson
- Brainz Magazine
- 6 days ago
- 5 min read
Jeremiah Johnson’s professional foundation was formed before technology, shaped by constraint, discipline, and self-directed learning. Born in Lagos, Nigeria, he grew up in a third-world environment defined by scarcity and instability, developing early resilience and a practical understanding of trade-offs. After moving to the UK around age seven, music became the first system he pursued seriously.
Jeremiah Johnson, Creative AI Expert
Who is Jeremiah Johnson?
I’m an AI consultant working at the intersection of creativity, technology, and systems thinking. My background spans tech sales, music, sound engineering, venue management, research, and education. Outside of work, my interests are still system-led: music, fitness, reading, learning, building apps, and breaking complex problems into structures that actually work. In business, I’m known for translating abstract technology into practical advantage without jargon or hype. My clients remember me for my creative approach to solving operational problems.
What inspired my journey into AI consulting and technology guidance?
My entry point into AI was necessity, not curiosity. While running a studio, I began using AI tools to organise work, reduce friction, and increase output. The real shift came when I had the epiphany that technology is simply creativity in disguise. Once that clicked, the move from creative work into applied AI became inevitable, reinforced by growing demand for my AI education as I published daily research-driven insights online.
What is my core mission and intended impact?
My mission is to reduce cognitive and operational waste. I help clients use AI to think more clearly, decide faster, and execute with less friction. The impact I aim to create is leverage: fewer people doing higher-quality work with more autonomy and confidence. I aim to show clients that creatively designed operational solutions can be as effective, and sometimes more effective, than traditional approaches rooted primarily in academic rigour.
What are the common challenges clients face before working with me?
Most clients are overwhelmed by noise. They have too many tools, unclear priorities, fragmented workflows, and no unifying logic. They know AI matters, but they don’t have a practical path from experimentation to measurable outcomes. A very common challenge is an additive approach to AI.
Businesses should initially approach AI from a subtractive perspective, ruthlessly eliminating tasks that can be automated, simplified, or made redundant through better workflow design. Another common issue is the segmentation of data in silos and unclean or large amounts of non-standardised data. This issue will be a primary reason many legacy businesses and corporations fail to adapt in the future.
How do I turn AI complexity into a strategic advantage?
I focus on constraints, sequencing, and outcomes. Tools come last. We first get clear on decisions, bottlenecks, and incentives. It often helps to map out a maturity grid showing the client's current position and where they'd like to be in future.
AI is only applied where it removes friction or amplifies judgement. Complexity is reduced by design, not explanation. AI complexity should be reduced to three simple categories: inputs, processes, and outputs. Anything else is noise.
What results do clients see after working with me?
Clients typically experience faster research cycles, clearer communication, reduced manual workload, and tighter decision-making loops. Productivity increases without increasing headcount. In many cases, teams regain confidence rather than burn out and can easily quantify the amount of time saved, which is typically 3-8 hours per week. I've received a perfect NPS score of 100 multiple times when educating corporate clients and have never received a score below 35. My scores are typically between 70-90 on average.
What is the common misconception about AI?
The biggest misconception is that AI replaces thinking. In reality, it exposes work ethic, clarity, and decision quality. AI mirrors the standards of the system it is placed into. Used well, it sharpens judgement, surfaces assumptions, and makes weak thinking impossible to hide. Used poorly, it amplifies disorganisation, indecision, and avoidance. Automation without clarity does not create efficiency, it accelerates confusion at scale.
Can you share a representative success story?
Last quarter, I was working with Bloomberg Media on an internal AI Challenge they were running for their employees. The challenge was to create a GPT or a Gem that would help them in their daily work across the different aspects of the organisations. We had a variety of participants from different departments and different levels of seniority in the organisation, and they all had different goals.
The winning project was pitched to senior leadership. We had training sessions at the beginning and the end, and weekly office hours, which is where most direct contact time took place. I initially had to ensure participants set realistic goals, encouraging them to think in terms of narrowly scoped solutions. Participants highlighted several aspects of the programme in their feedback:
"An expert point of view and advice on best practices."
"They were very down-to-earth and made the application of the tools easy to understand."
"They went deep into our projects and questions and provided broad answers and follow-ups."
What key tools and approaches do I use?
My approach is system-first. I look at existing workflows on a granular level and we start finding areas where modalities are transformed such as data to story (turning spreadsheets into reports), or summarisations, or creative visual generation (turning briefs to images). Tools vary by context, but commonly include language models (both small and large) for research and synthesis, automation platforms for workflow integration, and simple interfaces that reduce cognitive load.
Personalisation at scale is one of the most valuable solutions AI can provide to businesses, as generative AI allows us to represent (or, as I like to say, re-present) information in multiple different formats, to different audiences. The constant is intentional design and human-in-the-loop control. There should always be a human who's accountable for the successes, learnings, and risk management of deploying AI tools into workflows.
How do I stay current with AI?
I stay current through daily applied use. Tools are tested against real work, not demos. What survives is documented, simplified, and reused. What doesn’t is discarded quickly. Translation into client value is the only filter that matters. Posting daily about new AI tools for over 300 consecutive days as an exercise in public accountability means I have an unusually high signal-to-noise understanding of the landscape, allowing me to recommend tools precisely aligned to a client’s specific needs without guesswork.
What advice can you give to leaders who are unsure where to start with AI?
Don’t start with tools. Start with one decision or process that matters and is currently slow or unclear. Apply AI narrowly, measure the effect, then expand. Small wins compound faster than grand strategies. If a process is manual, time-consuming, or performed very frequently, it is a strong candidate for AI support. It's also important to note that the three primary use cases of AI are research, communications, and automation, the holy grail of which is automation, as this allows us to put the first two concepts on a flywheel that requires minimal human involvement.
The approach to automation should start by simply identifying where information is copied and pasted between platforms, interfaces, and websites. Research should then be done to see how an automated pipeline between those platforms' interfaces and websites can be created, reducing what I've called "copy-paste friction".
What’s next for me and my work?
My focus is on codifying consulting frameworks into repeatable systems and products. This includes agent-based workflows, education formats, and tooling that allows organisations to scale judgement, not just output. I’m constantly vibecoding new applications that are narrowly scoped and challenge long-standing preconceptions of how high levels of productivity and operational resilience can be achieved and maintained, and I’m open to connecting with others who share the vision that technology is simply creativity in disguise.
Read more from Jeremiah Johnson
