Agentic AI is the new way businesses will represent themselves in the next five years, and now is the time to invest. As happened before, like in the early days of the internet, those who move first will lead.
Before digital:
- Businesses relied on physical stores and newspaper or billboard ads.
- In 1995, websites became the digital storefronts, reshaping how companies connected with customers.
- By 2010, social media made brands active participants in online communities.
- By 2015, mobile came, businesses had to meet customers right in their pockets.
Now, were on the cusp of the next shift. IDC forecasts global spending on AI, including applications, infrastructure, and services, will exceed $632 billion by 2028, more than doubling from current levels.
But this isnt just about investing in mere automation. The true potential lies in integrating AI as teammates within your organization.
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These agents arent just advanced chatbots. The real opportunity lies in internal deployment: AI teammates embedded within your organization. A sales agent summarizes pipeline reports. An HR agent fields policy questions. A product agent surfaces customer insights.
They reduce grunt work, highlight what matters, and start making real decisions. As they connect across tools, they become as core as your customer relationship management system or cloud storage.
This isnt distant. McKinsey reports 78 percent of companies now use AI in at least one core function, a sharp jump from last year.
These agents will represent your brand while driving operational efficiency across functions. For small and mid-size businesses (SMBs), theyll automate the frontlines and quietly scale.
So, how can you begin this transformation?
Your first step into AI
You can begin by building retrieval-augmented generation (RAG) agents, simple yet powerful tools that pull the right information from your systems to support teams instantly.
According to Gartner, 80 percent of enterprises now favor RAG over fine-tuning as their go-to method for implementing GenAI, highlighting its rise as the leading architecture for real-world business applications.
At Tkxel, for example, we have a people buddy HR agent that fetches company policies, answers employee queries about leave balances, and provides quick HR support without manual intervention.
Once you see traction there, move towards creating AI workflows. For example, a simple yet effective marketing workflow might look like this:
- The AI agent drafts a campaign brief using the latest product updates and goals.
- It analyses audience insights and past engagement data to identify whats resonating.
- 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 driving better outcomes.
Finally, theres the idea of autonomous AI agents, systems that make and execute decisions independently. In my opinion, we havent reached that level just yet. Full autonomy requires more maturity in reasoning, safety, and governance. Well get there, but todays real opportunity lies in AI workflows that assist, accelerate, and extend your teams capabilities.
Where AI efforts stall
Yet for all the hype, theres a disconnect. According to Boston Consulting Group, despite widespread AI adoption, only 26 percent of companies have built the capabilities to go beyond proofs of concept and deliver real value.
After the first pilot, most leaders hit a wall. They dont know what comes next, how to iterate, scale, or move from the test phase to transformation.
Weve spoken with founders about the challenges in scaling AI initiatives, and several themes emerge consistently:
- Limited technical bandwidth
- Scattered data
- Unclear use cases
- Internal resistance
But beyond what theyre saying, what were seeing is even more telling:
- Solving the hardest problems first
- Stalling in endless tool evaluation
- Expecting instant perfection
Either way, traction slips before results appear.
A Proven Framework for Success: PoC � PoV � Scale
To avoid the stall, follow a phased, outcome-driven approach:
- Proof of Concept:Begin by exploring basic AI use cases that are relevant to your organization. At this stage, the focus is on validating whether these use cases are feasible and effective within your context.
- Proof of Value:Once feasibility is confirmed, the next step is to apply AI to core business processes, uncovering measurable impacts. This helps demonstrate how AI can drive real value for the business.
- Value Expansion:
Finally, scale AI across multiple business functions. This phase is about embedding AI more deeply into the organization to sustain performance and ensure AI continues to provide long-term value.
This staged framework keeps AI projects grounded in outcomes, not just experimentation, and turns early traction into long-term transformation.
Start simple, evolve fast
If theres one thing Ive learned, its this: AI workflows are where the real leverage is for SMBs. Not big bang deployments, not theoretical strategy decks, just picking one workflow that slows your team down today and fixing it with AI. Start with something repeatable, tie it to a clear outcome, and move fast. You dont need to solve everything at once. You need proof that this can work in your context. Once you have that, scaling isnt a question; its the next step.
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