Leaders looking to introduce agentic AI in their organization this year should start where accountability is clear, design deliberately as complexity increases and make sure readiness keeps pace with capability.
Philadelphia-area tech leaders are excited about AI agents, and the enthusiasm is understandable.
But there’s a growing gap between what organizations want agents to do and how ready they are to use them well.
The good news is that most companies don’t need to jump straight to fully autonomous systems. There’s a more practical way to think about agentic AI that aligns with how real work gets done in Philly-area businesses.
Write down five tasks that you complete every week. Look for those that involve repetitive steps and are somewhat ambiguous, like researching market information or comparing data from different systems. Narrow it down to the ones that you spend most of your time on.
Here’s how to apply that to the work you’re doing every day.
The 3 categories of agentic AI
Agentic AI scenarios tend to fall into three broad categories, each with increasing complexity and risk. Understanding these is key to deciding where agentic AI will have a great impact for you and your teams, and how rigorously to test your agents.
First: personal productivity.
This is where most people start. Individuals use AI to draft content, summarize information, brainstorm ideas or prepare analyses. The scope is limited, the risk is manageable and accountability is clear: The person using the tool owns the result.
Second: team-delegated tasks.
When it’s time to level up, teams begin delegating recurring tasks to agents, including pipeline reviews, report preparation, data checks and coordination across tools. Here, AI starts to influence shared outcomes. Variation matters more, and review processes become essential.
Third: business-critical workflows.
At the highest level, agents support core processes such as procurement, customer support routing and financial analysis. These systems scale impact quickly, but they also scale mistakes if guardrails aren’t in place.
Problems arise when organizations treat all three categories the same.
The dual-lens principle
Several software vendors have been advertising AI agents as “digital employees” that are capable of handling tasks that are typically within the scope of customer service or business development reps.
This pins humans against agents instead of showing the potential of collaborating with AI. One way to avoid that trap is to apply the Dual-Lens Principle when designing and deploying agents.
Looking through only one lens creates problems. Treat agents only as software, and you miss how they reshape work. Treat them only as digital coworkers, and accountability starts to blur. IT and business leaders need to hold both views simultaneously.
Through one lens, agents behave like employees. They follow processes, need clear instructions and operate within defined procedures. This lens helps teams think about workflows, handoffs and quality standards.
Through the other lens, agents are software. They make decisions based on data, logic and configuration. This lens forces clarity around ownership, accountability, permissions and risk.
As you build your first agent, lean on common HR concepts. Create a job or role description that defines what the agent should do. Decide which data and systems the agent should use to complete their tasks and define what the expected result should look like.
The human is still responsible
No matter how advanced agents become, one principle doesn’t change: responsibility for delivering great work stays with the human. That means checking for bias, validating factual accuracy, assessing relevance and deciding whether an output is good enough to act on or share.
AI can accelerate work, but it doesn’t remove judgment from the equation. This is where many organizations stumble. They scale AI use faster than they scale standards, and productivity gains quietly turn into review overhead, rework and what I call “Draft Debt” — AI-generated output that looks finished but still needs human clean-up.
You can avoid Draft Debt by reviewing what your agent has created before you pass on the result. Look for biases in the output — for example, if everyone’s contributions in a meeting are captured in the AI-created summary or if genders, ethnic backgrounds and jobs are represented equitably.
If something seems off, ask your agent to update its result or correct it yourself.
Start smaller, design deliberately
Leaders looking to introduce agentic AI in their organization this year should start where accountability is clear, design deliberately as complexity increases and make sure readiness keeps pace with capability.
Begin by reviewing your software vendors’ portfolios. Most of them have recently added AI agents that you can customize for your company. This reduces complexity and increases time to value.
For example, agents can help your sales team identify new prospects and automate parts of the outreach or create or respond to requests for proposals more quickly. This reduces manual effort on otherwise time-consuming tasks.
When human judgment stays ahead of AI capabilities, organizations achieve better outcomes without sacrificing trust or quality.
In 2026, wanting agents is easy. Using them well is where the real work begins.
