By Jessica Hurwitz, AI Researcher
AI is no longer the question; how you use it is. Many teams pilot AI and see pockets of success but struggle to turn that into consistent, scalable business value. The reason isn’t the technology; it’s the decisions made around how AI is implemented, governed, and evolved and where humans fit.
Based on our experience building and deploying AI across supply chains, document-heavy workflows, and commodity markets, here are the key decisions businesses must make to accelerate real benefits from AI.
Decision 1:
What problems are worth solving with AI?
AI delivers the fastest ROI when applied to repeatable, data-rich problems that already cause operational friction.
- Look at where data exists and outcomes are measurable.
- Prioritise clear business development over novelty.
In practice, this means building AI systems that combine flagship models with techniques like retrieval-augmented generation and selective fine-tuning, allowing businesses to get contextual accuracy without exposing confidential data.
Consider this: If the problem isn’t valuable without AI, it won’t be valuable with AI.
Decision 2:
How will AI value be measured?
Acceleration requires clarity on what “benefit” means.
- Success and risks need to be defined upfront to save time, reduce errors and uplift margins.
- Agree whether value is measured at POC or scale (sustained ROI).
With the correct success metrics, AI can be a business accelerator, instead of a research experiment.
Decision 3:
Do you build AI as a capability or a one-off solution?
AI has made previously intractable tasks possible.
- It is most beneficial for automating mundane activities, freeing up humans to be more creative and take on more complex concepts.
- AI excels at summarising information and finding patterns, but it improves with feedback and data.
- Expect to iterate – models, prompts, data, and guardrails will all evolve.
It is important to understand that AI is a powerful tool, but it is not the same as hiring more employees. Using AI to build a deterministic approach is how we get the best out of it.
Decision 4:
What AI operating model and tools fit your risk profile?
While AI is a brilliant facilitator of ideas, to get the best business benefits out of AI, it is important to understand its limitations.
- AI is powerful but probabilistic.
- It is prone to hallucination without constraints.
It is important to ensure that the type of AI being used is properly geared towards the problem at hand. It is also imperative to put guardrails up in the software to catch hallucinations before user involvement.
Decision 5:
How much context do you give AI and how do you control it?
The benefits of AI depend on context discipline.
- Use prompt engineering to specify task, role, format, and constraints.
- AI does not have understanding; it approximates – give it the right ingredients.
- Align business terminology and document structure.
However, large language models have limited context windows. It is a fine line between providing the AI with enough context within the limited context window.
Decision 6:
Where do humans stay in the loop and why?
With all of this said, AI cannot take responsibility. The responsibility sits with the user.
- Use human-in-the-loop where outputs affect customers, compliance, or capital.
- Design workflows so users verify more than edit. Aim for ‘click confirm’.
Automation is becoming more possible as AI agents mature, but the accountability should be kept on the human.
Decision 7:
How is data governed, secured, and permissioned?
AI sits across business domains and without clear ownership, adoption stalls.
- Establish who will be accountable for each AI workflow.
- Define data governance: permission, lineage, anonymisation, and retention.
- Protect confidential client data end-to-end.
Trust in AI is built on data governance, not just model sophistication.
Decision 8:
What not to automate?
If a process can be done with deterministic means, take that approach instead.
- Keep humans in control of approvals, exceptions, and escalations.
- Ask yourself if a process can be automated without AI first.
Mature AI strategies are defined as much by what they refuse to automate as by what they do.
Decision 9:
Are you investing in AI skills, change, and adoption?
AI works fast, but for AI to work well and consistently takes time and expertise.
- Build an AI team with research, engineering, ops and domain knowledge
- Give researchers time to understand the problem and find the best solution, test rigorously
- Be transparent with clients and internal users
AI is a skill that needs to be nurtured. Not only is it developing at a rapid pace, but respect also that it requires skills beyond what it looks like on the outside.
How we operationalise this at TechniCap
We combine flagship models with RAG / fine-tuning approach to ensure context accuracy while protecting confidential data. We’ve built real-time supply chain intelligence using knowledge graphs for commodity understanding and document extraction tools adaptable across formats – solving decade-old pain points with measurable precision and throughput improvements.
The bottom line
AI can deliver value quickly, but sustainable value comes from deliberate decisions.
The organisations seeing the biggest gains aren’t the ones chasing the latest models. They are the ones investing in the right operating model, building guardrails, keeping humans accountable, and developing AI skills over time.
AI is not a shortcut to replacing people. It’s a force multiplier for organisations willing to use it thoughtfully, responsibly, and continuously.
How is your organisation approaching these decisions today?
