Embracing AI with Dr. Gillian Hammah: Six AI trends that will shape 2026
Tag: General news
Published On: January 15, 2026
Artificial intelligence is no longer a distant future technology; it is already changing how people work across the world. This first part of a two‑part series explains three key AI trends for 2026 and what they mean for workers, businesses, and policymakers in Ghana.
Trend 1: Models Are Becoming Commodities
For the last few years, every new AI release triggered arguments about which model was “the best.” That debate matters less in 2026 because leading models are now tightly clustered in performance on most benchmarks. Research tracking model quality shows that the gap between top closed models (like OpenAI or Gemini) and open‑weight models (like Llama and others) has narrowed to just a few percentage points on major tests.
Open weight models allow developers to host, fine-tune and inspect the models, while closed models do not allow anyone to see or control the underlying model. At the same time, the cost of using powerful models has dropped dramatically, with reports of more than 100‑fold cost reductions over the last few years as hardware and optimization improve.
When things become cheaper and similar in quality, they start to look like commodities – like electricity or mobile data. The real competition shifts from “whose engine is better?” to “who offers the best experience and integration?” In AI, that means value is moving from the raw model layer to the application layer: tools that integrate AI into email, documents, ERP systems, school portals, or farming apps.
What this means for Ghana
Trend 1: Models Are Becoming Commodities
For the last few years, every new AI release triggered arguments about which model was “the best.” That debate matters less in 2026 because leading models are now tightly clustered in performance on most benchmarks. Research tracking model quality shows that the gap between top closed models (like OpenAI or Gemini) and open‑weight models (like Llama and others) has narrowed to just a few percentage points on major tests.
Open weight models allow developers to host, fine-tune and inspect the models, while closed models do not allow anyone to see or control the underlying model. At the same time, the cost of using powerful models has dropped dramatically, with reports of more than 100‑fold cost reductions over the last few years as hardware and optimization improve.
When things become cheaper and similar in quality, they start to look like commodities – like electricity or mobile data. The real competition shifts from “whose engine is better?” to “who offers the best experience and integration?” In AI, that means value is moving from the raw model layer to the application layer: tools that integrate AI into email, documents, ERP systems, school portals, or farming apps.
What this means for Ghana
- For Ghanaian startups, the barrier to using strong AI models is lower than ever, because open‑weight models with near‑frontier performance can be deployed locally or via regional cloud providers.
- For government and regulators, it becomes more important to set rules for how AI is used (data protection, fairness, safety) than to worry about a single dominant model.
- For universities and training providers, the focus should be on teaching students to design AI‑powered workflows and products, not just prompting a single branded model.
A practical Ghana‑specific example is fintech: rather than spending years trying to train a local large model, a Ghanaian fintech can combine an off‑the‑shelf open model with local data on fraud patterns and mobile money behaviour to build a credit‑risk or fraud‑detection assistant tailored to MTN MoMo, Telecel Cash, or banks.
Similarly, an agritech startup in Tamale could download a compact open‑weight model (like a small Llama or DeepSeek variant), run it directly on robust field hardware, and give farmers recommendations even when connectivity is poor or expensive.
Trend 2: 2026 Is About AI Workflows, Not “Magic Agents”
There is a lot of hype around fully autonomous “AI agents” that can do everything on their own. In practice, most organizations globally are not there yet: surveys show that only a small fraction – around 10% in many functions – report scaling truly autonomous agents, while a much larger share of AI use already happens through workflow‑specific tools that keep humans in the loop.
Similarly, an agritech startup in Tamale could download a compact open‑weight model (like a small Llama or DeepSeek variant), run it directly on robust field hardware, and give farmers recommendations even when connectivity is poor or expensive.
Trend 2: 2026 Is About AI Workflows, Not “Magic Agents”
There is a lot of hype around fully autonomous “AI agents” that can do everything on their own. In practice, most organizations globally are not there yet: surveys show that only a small fraction – around 10% in many functions – report scaling truly autonomous agents, while a much larger share of AI use already happens through workflow‑specific tools that keep humans in the loop.
Enterprise reports also show that a significant portion of AI use (often around 20% of enterprise usage) is happening via configured workflows such as custom assistants, templates, and embedded tools, not free‑roaming agents.
Several real‑world deployments show this pattern clearly: for example, hospitals now use AI to draft radiology or lab report summaries that doctors then review and sign off. Airlines use AI to suggest responses in customer‑support chats while human agents choose and edit the final message.
Several real‑world deployments show this pattern clearly: for example, hospitals now use AI to draft radiology or lab report summaries that doctors then review and sign off. Airlines use AI to suggest responses in customer‑support chats while human agents choose and edit the final message.
Many software teams also rely on AI coding assistants that generate proposed code or test cases, but developers still decide what to accept and how to integrate it. These are structured workflows, not fully autonomous systems, yet they routinely deliver large gains – often cutting preparation or development time by 50 to 60% while maintaining or improving quality and satisfaction.
Analysts suggest that 2026 is about agent‑light workflows, which are clear processes where AI does the predictable pieces and people still make the final calls. Consulting studies estimate that redesigning workflows around AI could unlock trillions of dollars in value globally by 2030, if organisations invest in process redesign rather than shiny demos.
Ghanaian use cases for AI workflows
For Ghana, AI workflows may be more realistic and impactful than jumping straight to full autonomy. Here are a few examples:
Ghanaian use cases for AI workflows
For Ghana, AI workflows may be more realistic and impactful than jumping straight to full autonomy. Here are a few examples:
- Public sector services. Passport or DVLA offices could use AI to pre‑check forms, flag missing information, and route cases, while staff do final approvals – reducing long queues without handing full control to a black‑box system.
- Education and assessment. A university in Accra could use a tool like GradePoint AI, a Ghana‑based grading assistant, to draft rubric‑aligned feedback on student essays while lecturers review and edit the comments before releasing them, significantly reducing marking time while still safeguarding their academic judgment.
- Customer service in banks and telcos. Ghanaian banks or telcos can let AI handle routine FAQs, SIM registration status checks, or balance queries, while human agents handle complex complaints or fraud cases; the workflow is human‑supervised but AI‑accelerated.
The key is to pick a recurring deliverable, such as monthly reports, help‑desk tickets, social‑media replies or underwriting memos, and break it into steps. For example, AI can be used for drafting, summarising, classifying; humans for exceptions, approvals, and sensitive decisions. For Ghanaian organisations that are just starting with AI, this workflow approach reduces risk, is easier to audit, and fits existing HR and governance structures.
Trend 3: The End of the Technical Divide
In many organisations, “technical people” traditionally acted as gatekeepers. For example, if a sales or marketing team wanted a dashboard or automation, they had to wait for IT or data teams. That pattern is weakening. Studies from MIT and others show that generative AI disproportionately boosts the productivity of less‑experienced or less‑technical workers, compressing the performance gap between “experts” and “novices.” One widely cited experiment found that lower‑skill workers improved much more when given access to tools like ChatGPT, reducing inequality in output quality.
Enterprise usage data shows a sharp rise in coding‑related activity from non‑technical staff, as salespeople, marketers, and operations managers increasingly use AI to write scripts, automate spreadsheets, and build simple internal tools. Recent surveys report that around three‑quarters of enterprise users say they now rely on AI to complete tasks they literally could not do before, not just to speed up work they were already doing.
AI is acting as an equaliser, turning technical execution into something many different roles can access directly rather than a privilege reserved for specialists.
Trend 3: The End of the Technical Divide
In many organisations, “technical people” traditionally acted as gatekeepers. For example, if a sales or marketing team wanted a dashboard or automation, they had to wait for IT or data teams. That pattern is weakening. Studies from MIT and others show that generative AI disproportionately boosts the productivity of less‑experienced or less‑technical workers, compressing the performance gap between “experts” and “novices.” One widely cited experiment found that lower‑skill workers improved much more when given access to tools like ChatGPT, reducing inequality in output quality.
Enterprise usage data shows a sharp rise in coding‑related activity from non‑technical staff, as salespeople, marketers, and operations managers increasingly use AI to write scripts, automate spreadsheets, and build simple internal tools. Recent surveys report that around three‑quarters of enterprise users say they now rely on AI to complete tasks they literally could not do before, not just to speed up work they were already doing.
AI is acting as an equaliser, turning technical execution into something many different roles can access directly rather than a privilege reserved for specialists.
Implications for Ghana’s workforce
Ghana has already signalled its ambition to use AI to boost productivity, with a National AI Strategy and ongoing work on ethical AI policy and an Emerging Technologies Bill. If implemented well, these tools could help workers at all education levels upgrade their capabilities:
Ghana has already signalled its ambition to use AI to boost productivity, with a National AI Strategy and ongoing work on ethical AI policy and an Emerging Technologies Bill. If implemented well, these tools could help workers at all education levels upgrade their capabilities:
- A teacher in Cape Coast who is not a programmer can use AI to generate quizzes, lesson plans aligned to the new curriculum, or basic data dashboards showing student performance over the term.
- A small business owner in Accra’s Makola market can ask AI to draft invoices, write marketing copy, or generate simple sales‑tracking sheets in Excel or Google Sheets, without hiring a consultant.
- Nurses and health officers can use AI to draft patient education materials in English and Ghanaian languages, or summarise clinical guidelines into simpler steps, even if they have no background in data science.
- But the same trend is a warning: if your value in the organisation is “I am the only one who knows Excel, SQL, or how to build dashboards,” that advantage will shrink. The workers who will thrive are those who combine domain expertise (i.e., knowing Ghanaian customers, local regulations, or on‑the‑ground realities) with the ability to use AI tools directly.
How Ghanaians can respond in 2026
- Attempt one “impossible” project.
Choose a task you usually outsource – like building a dashboard of sales by region, cleaning a messy dataset of customer records, or automating a weekly status email – and try to build it using an AI assistant, step by step. - Shift from tools to systems.
Instead of learning just one app, think in terms of systems: how can AI plus Google Workspace, Microsoft 365, or local platforms like Hubtel or ExpressPay work together to support your role end‑to‑end? - Align with Ghana’s AI strategy.
As Ghana rolls out its AI strategy and emerging tech regulations, workers and businesses that can show they use AI responsibly – protecting data, avoiding bias, documenting processes – will be better positioned for partnerships and funding.
The “end of the technical divide” does not mean everyone must become a programmer; it means everyone has the capability, using AI, to turn ideas into working prototypes without waiting months for IT. In a context like Ghana, where there is both high youth unemployment and high demand for digital skills, this shift could be a powerful lever.
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.
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.