Embracing AI with Dr. Gillian Hammah: Advanced AI skills to future proof your career

AI is moving from “interesting tool” to “invisible teammate.” It is now time to focus on more advanced skills that let you design, supervise and multiply that teammate’s impact, especially in workplaces where resources are tight and opportunity is huge.

 From tools to teammates: understanding agentic AI

Generative AI is the assistant that answers your questions or drafts content on demand. Agentic AI takes this a step further: instead of just responding once, an AI “agent” can plan, take multiple actions, and move towards a goal with minimal supervision.​

Imagine a company posts a job and gets 200 CVs. Instead of a human reading each one, an AI agent can:​

• Read the job description and extract the key requirements.
• Compare every CV against those requirements with the same level of attention.
• Summarise each candidate’s strengths and weaknesses.
• Rank candidates and email a report to the HR lead.

The HR professional still makes the final decision, but they’re now working with a shortlist and clear summaries instead of a tall pile of paper. That’s the heart of agentic AI: AI stops being a toy and becomes part of the workflow.​​

The advanced skill here isn’t “knowing the name of the latest AI tool.” It’s being able to imagine and design processes where agents take over the repetitive steps and humans focus on judgment, relationships and complex decisions.​

 AI workflow redesign

Most people approach AI like this: “I have this task, can ChatGPT help me?” That’s a good start, but advanced professionals flip the question: “What does my end‑to‑end workflow look like, and where can AI create the biggest lift?”​​

Here are three examples:

• A bank branch
  • Today: customers queue for balance enquiries, statement requests, and basic complaints.
  • With AI: a virtual assistant handles simple questions on WhatsApp, an agent automatically drafts follow‑up emails, and staff focus on higher‑value conversations like resolving disputes or offering tailored products.​
• A government office
  • Today: citizens fill long forms by hand; staff re‑key data, search files manually, and answer the same questions repeatedly.
  • With AI: forms are scanned and auto‑captured, an agent flags incomplete applications, and a chatbot answers common questions in English and local languages.​
• A small agribusiness
  • Today: yield predictions are guesswork, and market updates come late.
  • With AI: satellite and weather data feed simple models that suggest planting windows, and an AI assistant summarises price trends from multiple markets.​

In each case, the technical pieces (i.e., document AI, chatbots, simple prediction models) already exist. The advanced skill is workflow redesign: mapping how work actually happens in your context, then deciding which steps AI should support, automate, or leave entirely human.

Professionals who can draw that map and explain it clearly to both leadership and technical teams will be in strong demand, regardless of their original job title.​

 ML and LLM fundamentals (for non‑engineers)

You might be thinking, “This sounds great, but I’m not a data scientist.” The point of machine learning (ML) fundamentals for most professionals is not to turn you into an engineer; it’s to help you ask better questions and avoid bad decisions.​

At a high level, most modern AI systems, including large language models (LLMs), follow a simple pattern:​

• Data: examples from the past (transactions, images, text, conversations).
• Model: a mathematical structure that finds patterns in that data.
• Training: adjusting the model until it can make good predictions or generate useful outputs.
• Evaluation: testing it on new data to see how well it performs.

If your data is biased, messy or incomplete, your model will quietly learn those problems and repeat them. If you apply a model trained on one context (say, U.S. credit data) directly to a different one (say, Ghanaian micro‑loans), you can get unfair or simply wrong outcomes.​

For non‑technical professionals, useful ML/LLM fundamentals include:​

• Knowing the difference between a simple rule‑based system and a learned model.
• Understanding that models have limits: a probability is not a guarantee.
• Asking about the data: “Where did it come from? Does it represent our customers or communities?”
• Recognising when a problem is predictable enough for AI, and when it needs deep human judgment.

This level of understanding helps you collaborate with technical teams, evaluate vendor promises, and spot when someone is “AI‑washing” a basic feature.​

You can later decide you want to go deeper into Python, statistics or model building. But even without coding, ML fundamentals make you a much sharper decision‑maker in an AI‑driven organisation.​

 AI governance and building trust

As AI touches more decisions (e.g., hiring, lending, customer service), governance is moving from an IT issue to a core business skill.​

AI governance is about setting rules, guardrails and habits for how AI is used in your organisation so that it is:​

• Accountable – someone is clearly responsible for outcomes.
• Fair – systems don’t systematically disadvantage certain groups.
• Transparent – people understand when and how AI is involved.
• Compliant – use respects laws, regulations and company values.

Global analysts already rank AI governance and compliance among the most in‑demand AI skills for 2026. One expert put it this way: “The most valuable AI skill in 2026 isn’t coding, it’s building trust.”​

In Africa, the governance opportunity is even larger because:​

• Data protection laws and AI regulations are still maturing.
• Many organisations have informal data practices and limited documentation.
• Imported tools may not be designed with African languages, names, or cultural patterns in mind.

Professionals who understand both their local context and AI risks can shape how AI is adopted—instead of silently accepting whatever is shipped.​

Practically, AI governance skills include:

• Drafting simple internal policies: what data can be fed to public models, which tools are approved, where human review is mandatory.​​
• Participating in or initiating AI councils or working groups, where staff can propose tools and raise concerns.​​
• Asking governance questions in projects: “How will we monitor this system? What’s our plan if it fails? How will users challenge a wrong decision?”​​

These skills sit at the intersection of ethics, risk, law and culture, making them accessible to people from HR, legal, operations, finance and beyond.​

Domain‑specific AI skills: where the real value is

AI skills only become powerful when they are applied to specific problems, with specific people, in specific places. That’s why domain‑specific AI skills — combining AI fluency with deep knowledge of your field — are some of the most valuable in 2026. Here are a few examples:

• Finance and fintech
  • Using AI for credit scoring in informal economies where customers may lack formal credit histories but have rich mobile‑money or transaction data.​​
  • Designing fraud models that recognise local scam patterns instead of relying only on global templates.
• Healthcare
  • Supporting diagnostics with AI tools that flag anomalies in scans or lab results, while clinicians keep the final say.​
  • Using AI to triage cases in busy clinics, so the most urgent patients are seen faster.
• HR and people development
  • Analysing internal skills data to design better training, promotion pathways and succession plans.​
  • Using AI to personalise learning content for large, diverse workforces, without letting algorithms reinforce bias.
• Agriculture and climate
  • Combining satellite, weather and soil data to give smallholder farmers better guidance on planting and fertilizer use.​
  • Predicting pest outbreaks and yield risks so cooperatives and buyers can plan ahead.

In each of these, the technology alone is not enough. You need people who:​

• Understand the sector’s realities, incentives and constraints.
• Can translate messy, local problems into clear AI‑friendly use cases.
• Know when to trust the model and when to override it.

For African professionals, this is a major advantage. You live in the context global models often struggle with. If you build AI fluency on top of that, you become the bridge between powerful technology and local impact.​

Bringing it together (and looking ahead)

The advanced AI skills that matter most in 2026 are less about writing algorithms and more about designing, supervising and applying AI intelligently:

• Understanding and shaping agentic AI and workflows
• Grasping enough ML/LLM fundamentals to ask good questions
• Leading on AI governance and trust
• Combining AI fluency with deep domain knowledge

For now, the most important step is choosing one of these advanced skills and deciding where you’ll start applying it in your own corner of Africa’s AI future.​

Dr. Gillian Hammah is the Founder of GradePoint AI, an AI-Powered Grading Assistant for African University Lecturers and Chief Marketing Officer at Aya Data, a UK & Ghana-based AI consulting firm. Connect with her at [email protected] or www.gradepoint.ai.

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