Low-code tools are going mainstream
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Multilingual NLP will grow
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Combining supervised and unsupervised machine learning methods
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Automating customer service: Tagging tickets and new era of chatbots
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Detecting fake news and cyber-bullying
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For years, we've dreamed of automating our most tedious workflows. Yet complex processes with contextual nuances remained hard to automate. Until now.
The Traditional Way
The 'if-this-then-that' logic has served as our automation backbone for decades. Very powerful with straightforward processes, but it crumbles when facing the almost infinite nuances of reality.
- You've build a customer service automation? It fails on the first edge case.
- Your competitor updates their interface? Your scraper breaks overnight.
- A scenario requires contextual judgment? Traditional automation simply can't handle it.
Don't get me wrong - stacking these automations is powerful and in many cases still the best solution. But they've also kept entire categories of work firmly in human hands.
The Leap From 'Task' to 'Goal' Automation
Unlike this rigid approach, LLM's are remarkably capable of understanding context and nuances.
This allows us to move from task-oriented automation to goal-oriented automation, and that is not a detail. It's a paradigm shift.
Your automation can now adapt to unexpected changes and make contextual decisions without human intervention. Rather than meticulously programming every possible path and exception, we can now:
1. Define the desired outcome
2. Provide necessary access to context and tools
3. Set appropriate guardrails
4. Let the agent navigate the nuanced landscape towards a goal.
Processes that were impossible to automate not to long ago are now prime candidates. For businesses, this means scaling operations without scaling headcount while freeing human talent for truly creative work. The definition of a competitive advantage.
The Double-Edge Sword
Exciting for sure, but also a bit scary if you ask me.
Because when introducing agentic AI in workflows, we're handing some of the judgment to the LLM.
A huge leverage with great responsibility. When an agent has the autonomy to determine its path to a goal, critical questions emerge:
- Who's accountable when an AI makes an ethically questionable decision to achieve its target?
- How do we ensure our agents' methods align with our values, not just our metrics?
- Where does human oversight belong in increasingly autonomous systems?
Consider a sales-oriented AI that discovers misleading messaging drives better results, or a content agent that mimics competitors' work too closely. The goal was achieved, but at what cost?
Navigating the New Frontier
These are important questions to answer when building these solutions.
Despite these challenges, the move towards agentic AI in automation is practically inevitable. The advantages are simply too significant.
I truly believe AI has the potential to improve life in ways we cannot yet imagine. With responsible implementation, and small experiments as the way forward.
It's like a metaphorical narrow bridge. You're by bike and want to cross, but how?
Don't stand still or overthink it. Instead, keep moving forward at a steady pace. Maintain balance so you can correct little deviations on the path to your destination.
The Next Step?
If there was a genie in a bottle that could automate any process in your business, what would you choose?
Those who answer this question thoughtfully and take action now will write the history of the future.
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