The New Face of Business is AI Agents
The future of business is being shaped by artificial intelligence, and the next five years will see a dramatic shift in how companies operate. This is not just about adopting new technology—it’s about redefining how businesses engage with customers, manage internal operations, and drive growth. Just as the internet transformed commerce in the 1990s, AI agents are now poised to become the backbone of modern enterprises.
From Physical to Digital: A Historical Perspective
In the early days of business, companies relied on physical stores and traditional advertising methods like newspapers and billboards. By 1995, websites became the digital storefronts that allowed brands to connect with customers online. The rise of social media in the 2010s turned brands into active participants in online communities, while mobile technology in 2015 forced businesses to meet customers wherever they were—right in their pockets.
Now, we stand at the threshold of another major transformation, one driven by AI. According to IDC, global spending on AI—including applications, infrastructure, and services—is expected to exceed $632 billion by 2028, more than doubling current levels. But this isn’t just about automation; it’s about integrating AI as a strategic partner within your organization.
AI as Your Teammate
AI agents are no longer just advanced chatbots. They are intelligent systems designed to work alongside employees, enhancing productivity and decision-making. These agents can handle tasks such as summarizing pipeline reports for sales teams, answering HR policy questions, or identifying customer insights for product development. They reduce repetitive tasks, highlight key information, and even make decisions based on data.
As these agents integrate across different tools and platforms, they become as essential as cloud storage or customer relationship management (CRM) systems. This integration is already happening. McKinsey reports that 78% of companies now use AI in at least one core function, a significant increase from previous years.
For small and mid-size businesses (SMBs), AI agents offer a way to automate front-line operations and scale efficiently without needing large teams. The potential is vast, but the question remains: How do you begin?
Your First Step into AI
The journey starts with building retrieval-augmented generation (RAG) agents—simple yet powerful tools that pull relevant information from your systems to support teams instantly. Gartner notes that 80% of enterprises now favor RAG over fine-tuning as their go-to method for implementing generative AI, highlighting its growing importance in real-world applications.
At Tkxel, for example, an “HR buddy” agent handles employee queries about leave balances and company policies, providing quick support without manual intervention. Once these initial efforts show traction, the next step is to create AI workflows that streamline processes.
Building Effective AI Workflows
A simple marketing workflow might look like this:
- An AI agent drafts a campaign brief using the latest product updates and goals.
- It analyzes audience insights and past engagement data to identify what works.
- It generates multiple copy variations tailored to different segments or platforms.
- Finally, it recommends the top-performing versions based on A/B testing feedback.
These workflows blend automation with creativity, saving hours of manual effort while improving outcomes. However, the ultimate goal—autonomous AI agents that make and execute decisions independently—is still a ways off. Full autonomy requires more maturity in reasoning, safety, and governance. For now, the real opportunity lies in AI workflows that assist, accelerate, and extend your team’s capabilities.
Where AI Efforts Stall
Despite widespread adoption, many companies struggle to move beyond proof-of-concept stages. According to the Boston Consulting Group, only 26% of companies have built the capabilities needed to deliver real value from AI. After the first pilot, leaders often hit a wall, unsure how to iterate, scale, or transition from testing to full implementation.
Common challenges include limited technical resources, scattered data, unclear use cases, and internal resistance. Some companies also get stuck in endless tool evaluations or expect instant perfection, which leads to stalled progress.
A Proven Framework for Success
To avoid these pitfalls, adopt a phased approach:
- Proof of Concept (PoC): Start by exploring basic AI use cases relevant to your business. Focus on validating feasibility and effectiveness within your context.
- Proof of Value (PoV): Once feasibility is confirmed, apply AI to core business processes and measure its impact. This helps demonstrate how AI can drive tangible value.
- Scale: Finally, expand AI across multiple functions, embedding it deeply into your organization to sustain performance and ensure long-term value.
This structured framework keeps AI projects grounded in outcomes rather than experimentation, turning early success into long-term transformation.
Start Simple, Evolve Fast
The key lesson here is that AI workflows are where SMBs can gain the most leverage. Not through big-bang deployments or theoretical strategies, but by tackling one specific workflow that slows down your team. Pick something repeatable, tie it to a clear outcome, and move fast. You don’t need to solve everything at once—you just need proof that AI can work in your context. Once you have that, scaling becomes inevitable.