The future of AI: From data to action

By The Coffee Shops. 

The shift from copilots to teammates: unlocking the era of systems of action. 

Editor’s note: This article was written by ChatGPT based on Karol Weymans’ notes from summit presentations. 

Amit Shah, founder and CEO of InstaLILY, delivered a thought-provoking session on the accelerating adoption of AI in the enterprise and its implications for the future of work at the recent SRS Distribution x InstaLILY AI Innovation Summit. 

The acceleration of enterprise AI 

Amit emphasized that AI is no longer an experimental technology, it is rapidly becoming a core part of enterprise operations. Both incumbents and start-ups are driving adoption at scale with massive investments fueling the shift. He pointed out that $200 million will be spent in the coming year on data centers dedicated to AI, noting, “We spent less putting people on the moon.” 

So far, he explained, 90% of AI has focused on words, but 90% of human work is centered on actions. That gap represents both the challenge and the opportunity ahead. “We are still in the opening act,” Amit said, stressing that the pace of change will have dire consequences for organizations that fail to participate. 

From artificial intelligence to action intelligence 

The industry is now shifting focus from we’ve known AI to stand for, Artificial Intelligence, to what Amit called Action Intelligence, the ability for AI to move beyond processing information and into executing tasks. With advances in models like ChatGPT, which can now work independently for more than two hours, AI is no longer just a co-pilot. It is becoming a teammate. 

To define a “good teammate,” Amit outlined three AI superpowers: 

1. Memory in context and long-term retention – agents never forget. 
2. Reasoning, inference and logic – including advanced math capabilities that once eluded AI. 
3. Communication – agents that collaborate seamlessly with humans. 

The compounding effect of memory, reasoning and communication makes agents increasingly potent, creating what Amit described as an “infinity of information.” 

AI copilots versus AI agents 

Amit distinguished between two roles AI plays in the enterprise today: 

1. AI Copilots support humans by providing suggestions and insights but still require human oversight. 
2. AI Agents operate independently, exercising high autonomy to make decisions, complete tasks and finalize outcomes. 

It is the rise of AI agents, Amit argued, that will unlock unprecedented enterprise value. 

The stages of enterprise AI adoption 

According to Amit, adoption follows a clear trajectory: 

1. Search and retrieval 
2. Decision guidance 
3. Tribal codification (knowledge capture) 
4. Augmented action by AI agents 

This progression is happening faster than most anticipated. In fact, Amit noted that the timeline from $1 million to $100 million in ARR for AI-based businesses is compressing dramatically. Both the cost and speed of building AI agents are improving, with multi-capable processors (MCPs) enabling them to communicate across applications and even self-train. 

“This is not just a software change,” Amit warned. “It is a human knowledge marker. The highest expense in business is human capital, and without AI, you will be left behind.” 

From systems of record to systems of action 

Amit placed today’s AI revolution in historical context: 

• 1994–2004: Digital era – Systems of Record 
• 2004–2014: Automation era – Robotic Process Automation 
• 2024: Intelligence era – LLM apps, Systems of Intelligence 
• Now: Agent AI era – Systems of Action 

Unlike past systems, these new systems do not just record or automate processes, they act, learn and adapt. Enterprises are already deploying systems of action, creating powerful competitive moats. 

InstaLILY and the rise of InstaWorkers 

Amit closed by sharing how InstaLILY is pioneering this future with its platform for hiring vertical AI teammates, called InstaWorkers. These AI-powered teammates prioritize key accounts, drive sales and serve as an all-knowing resource within enterprises. 

A practical use case he highlighted was diagnosing equipment issues in real time, where AI agents analyze images or data, identify the problem and generate actionable solutions on the spot. 

Amit’s formula for growth: 

Build agents fast in the enterprise + build agents that run fast in the enterprise = build your enterprise fast 

By giving AI agents all the resources they need, enterprises can accelerate performance, reduce reliance on human capital and position themselves to thrive in an era where systems of action replace systems of record.