AI-Powered Business Optimization: The Future of Decision-Making

Guest: Avrom Gilbert (CEO, SparkBeyond)
Host: The Digital Executive (Coruzant Technologies)
Episode: 1033 – March 25, 2025
Duration: ~14 minutes

Episode Overview

This episode features Avrom Gilbert, CEO of SparkBeyond, who discusses how AI, particularly generative and agentic AI, is revolutionizing business optimization and decision-making. Drawing on Avrom's extensive executive experience in high-growth tech firms, the conversation explores how AI evolved from a supporting tool to a potential autonomous decision-maker, what “always optimized” businesses look like with AI, and why agentic AI is poised to transform the industry.

Key Discussion Points & Insights

1. The Evolution of AI in Business Decision-Making

[01:39–05:29]

• Two Eras: Avrom distinguishes “pre-gen AI” and “post-gen AI” periods in business.

  • Pre-gen AI: Early AI was at work in the background at large enterprises, providing insights from structured data (CRM, ERP, IoT), but not widely recognized or trusted at the operational level.
  • Post-gen AI: Since the emergence of tools like ChatGPT, AI’s capabilities have accelerated—especially in rapid research and world knowledge access.

• Decision Support Shifts: Tasks that once took days or weeks can now be handled in minutes, leveling the playing field for smaller businesses.

• Next Leap: The future lies in AI agents with a “Jarvis-like” (from Iron Man) understanding of not just global knowledge, but the unique drivers of a specific business. The challenge is that LLMs excel in general reasoning but lack company-specific operational data.

• SparkBeyond’s Role: Their “Always Optimized” platform aims to bridge this gap—educating AI on specific business intricacies for tailored insight and decision-making.

“If you were to ask ChatGPT or Perplexity why my costs are going up for a specific product and what should I do to fix it, … I probably get generic advice from the Internet and I certainly wouldn't hire an employee to give me generic advice. … The next big leap is when you give LLMs that knowledge that they need, which is really up-to-date information that makes them experts on your business.”
— Avrom Gilbert [04:40–05:20]

2. The Next Evolution: Deeply-Integrated, Always-Optimized AI

[06:07–07:17]

• Business-Specific Knowledge: AI agents will be able to understand highly specific, multi-variate details—e.g., machine-level data in a factory, or purchasing patterns across micro-segments of customers.
• Real-Time Optimization: AI will proactively prevent problems (like equipment failure) or optimize opportunities (customized promotions at scale), reacting not just quickly but autonomously.

“That LLM having not just world knowledge, but actually the capabilities and understanding of what drives the real world and specifically my business, that's going to be a big deal.”
— Avrom Gilbert [06:39–06:51]

3. Why “Agentic” AI Is Gaining Traction

[07:44–10:02]

• Agentic AI Defined: Agents are autonomous AI systems capable of performing specific tasks based on instructions, requiring little to no human intervention once set.
• Lower Barriers: The quality of large language models (LLMs) has improved rapidly, making it increasingly easy—soon even for non-experts—to deploy custom agents for business and personal use.
• Historical Analogy: The surge in agent development is likened to the explosive growth of mobile app ecosystems post-App Store launch—going from a few specialized applications to an everyday, multifaceted necessity.

“What we've now got is the ubiquitous availability of LLMs to the entire world. Which means there's a very low bar for creating agencies. … Pretty soon, a simple natural language instruction to an LLM will likely allow anyone to create an agent which is then usable.”
— Avrom Gilbert [08:16–08:44]

4. Real-World Example: Always Optimized AI in Action

[10:30–13:18]

• Traditional Approach vs. Always Optimized:

  • Traditionally, identifying issues like customer churn involved time-consuming, manual analysis and multiple internal handoffs.
  • With always optimized AI, insights and solutions are continuously generated and deployed in real time—AI segments customers, diagnoses causes for churn, drafts personalized responses, and (eventually) can take direct action without human approval.

• Scalability: This approach is not limited to churn reduction, but extends to fraud detection, cost analysis, energy optimization, and more—essentially, any domain with actionable operational data.

• The Key: Turning LLMs into true business experts by feeding them relevant, up-to-date business data, making AI’s reasoning both precise and contextually relevant.

“Always optimized means that AI does the hard work. And really the key to all of this is that we figured out the way to educate an LLM how to become an expert in the relevant parts of your business. … Once you've done that, it's much easier because LLMs are awesome.”
— Avrom Gilbert [12:35–12:58]

Notable Quotes & Memorable Moments

• On the “Jarvis” Analogy:

  “Have you seen the Marvel movies Iron Man? … Tony Stark has an AI called Jarvis which deeply understands everything including his business and operations. … We can all imagine maybe we'll be able to have an AI which ... achieves my business goals or make decisions for me.”
  — Avrom Gilbert [03:28–04:02]

• On Barriers to Entry for AI Agents:

  “If you have an idea for an agent, some basic technical skills and some time, you can create an agent fairly easily. And pretty soon a natural language instruction to an LLM will ... allow anyone to create an agent which is then usable.”
  — Avrom Gilbert [08:19–08:44]

• On Always Optimized AI:

  “This entire process can happen as frequently as we see the data changing—without delays, no waiting for people to be available or to have the time to do the work. … This whole always optimized capability is going to be perfectly natural for organizations in the coming years as we grow to trust LLMs and generative AI much more.”
  — Avrom Gilbert [12:58–13:18]

Key Timestamps

| Timestamp | Topic | |------------|------------------------------------------------------------------------------------------------------| | 01:39 | What is pre-gen AI vs. post-gen AI era in business decision-making | | 04:00 | The “Jarvis” analogy and the need for business-specific AI | | 05:29 | The impact of gen AI and the analogy of personal AI assistants | | 06:07 | What’s next: LLMs deeply understanding your business | | 07:44 | Why agentic AI is rising: historical context and technological progress | | 08:44 | Analogy: Agent ecosystem compared with the rise of mobile app stores | | 10:30 | Real-world scenario: Always optimized AI for churn reduction and other business needs | | 12:35 | Educating LLMs: Creating business experts from language models |

Conclusion

Avrom Gilbert paints a compelling vision of a future where AI is no longer a passive analytical assistant but an active, always-on agent deeply attuned to every operational nuance of a business. As agentic AI continues to advance and “always optimized” platforms become mainstream, organizations are poised to shift from reactive, labor-intensive decision-making to real-time, proactive optimization—enabled by AI that truly understands not just what your business needs, but why, and how to deliver it at scale.