By Sarthak Rohal, Senior Vice President at In2IT Technologies.
A few years ago, Artificial Intelligence (AI) was mostly an experiment. Companies saw it as futuristic, expensive, and typically only accessible to big tech firms. Then Generative AI (GenAI) arrived and quickly shifted the conversation. Suddenly, executives in various industries began asking, “How quickly can we implement this?”
This led to a flurry of pilot projects, internal demos, AI-generated presentations, chatbot tests, and bold claims about productivity improvements. However, behind the excitement, a more challenging reality emerged. Many organisations found that while GenAI generates interest, producing measurable business value is often complex.
Today, the discussion is shifting from novelty to practicality. Companies no longer question whether to explore GenAI. Instead, they are focusing on where it can truly improve operations, reduce costs, enhance decision-making, or provide a competitive edge. This is where strategic guidance becomes crucial.
From ‘cool technology’ to operational necessity
One major misconception about GenAI is that it functions as a standalone solution. In fact, its success relies heavily on how well it integrates with existing business processes.
Many early adopters treated GenAI in a tactical way rather than a strategic one. Departments rolled out individual tools without considering governance, security, integration, or long-term business results. Some businesses invested significantly in AI platforms that employees barely used once the initial excitement wore off.
Organisations that see the best returns take a different approach. Instead of chasing trends, they identify high-friction areas in their operations and ask where GenAI can boost efficiency or enhance customer experience.
In financial services, for instance, GenAI speeds up report drafting, automates routine administrative tasks, and assists with data analysis. In healthcare, AI systems help summarise patient information and lessen the workload of manual documentation. In manufacturing and logistics, GenAI supports teams with predictive maintenance insights, knowledge management, and operational troubleshooting.
The key takeaway is that successful implementations address specific business problems instead of deploying AI just to seem innovative.
The hidden challenge: too many possibilities
Ironically, one of the biggest challenges in adopting GenAI is the overwhelming number of possible use cases. Executives face a barrage of vendor promises, industry predictions, and demos claiming transformative results.
The issue is that not every use case offers a meaningful return on investment.
A customer service chatbot might sound impressive, but if the underlying business processes are flawed, it only automates frustration. Likewise, using GenAI tools with poorly structured data can lead to inconsistent or unreliable results.
This is where experienced IT consultants are increasingly vital. They provide not just technical expertise but also help organisations distinguish truly impactful opportunities from costly distractions.
Rather than starting with technology, consultants guide businesses to focus on operational realities. Which processes take up the most time? Where are employees bogged down by repetitive tasks? Which areas struggle with knowledge bottlenecks or slow decision-making?
By answering these questions first, businesses can prioritise AI initiatives that are measurable, scalable, and aligned with broader organisational goals.
Build it yourself, or buy it off the shelf?
Another important decision for organisations is whether to develop customised GenAI solutions internally or use existing platforms.
On paper, building custom systems seems appealing. Tailored solutions can allow for greater control, stronger differentiation, and better integration into business workflows. However, creating and maintaining AI systems in-house requires significant expertise, governance structures, infrastructure investment, and ongoing management.
For many organisations, especially in South Africa where there are skill shortages in advanced tech fields, building everything from scratch may not be feasible.
At the same time, relying solely on off-the-shelf solutions comes with its own risks. Companies may face limitations on customisation, data privacy, integration, or vendor dependency.
The answer is often not clear-cut. More organisations are adopting hybrid solutions, combining commercial AI tools with custom workflows and internal governance frameworks. IT consultants are helping businesses navigate these choices by evaluating long-term scalability, operational needs, and total costs rather than focusing only on quick implementation.
Governance is no longer optional
As GenAI adoption speeds up, it’s becoming clear that unmanaged AI poses significant risks.
Concerns about data security, compliance, intellectual property, misinformation, and bias are now real business risks with potential reputational and legal consequences.
An employee uploading sensitive internal data to a public AI tool could expose confidential information. AI-generated outputs that seem convincing may still contain inaccuracies or biases. Without clear governance policies, organisations risk creating operational vulnerabilities faster than they generate efficiencies.
This is why responsible AI deployment is as important as innovation.
Proactive businesses are establishing governance frameworks early, defining clear policies around data usage, human oversight, accountability, and ethical use. They are also recognising that GenAI should enhance human expertise rather than replace critical decision-making.
Beyond experimentation lies competitive advantage
The companies that gain real value from GenAI are not necessarily the fastest movers. They are the ones who move with clarity and end with measurable results.
The initial surge of enthusiasm for GenAI created understandable pressure for companies to act quickly. However, the next phase of adoption will belong to businesses that link AI investment directly to operational outcomes and measurable business goals.
This requires more than implementing technology. It involves aligning strategy, redesigning processes, ensuring governance, developing skills, and continuously evaluating results.
GenAI clearly marks one of the most significant technological shifts in recent business history. But its long-term value will depend on whether those tools address meaningful problems, enhance resilience, and provide a sustainable advantage in increasingly competitive markets.
The hype cycle may have brought GenAI into the boardroom, but measurable business value will keep it there.
