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Oct 21, 2025 · 30m · big-technology

How Enterprises Actually Get ROI From AI — With Globant CEO Martin Migoya

Martin Migoya · 23m spoken Alex Kantrowitz · 3m spoken
0:00 / 0:00
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gold bands on the timeline = statements, start to end. Hover to read, click to jump. CC turns on captions

In this interview, Globant CEO Martin Migoya speaks with Alex Kantrowitz about the realities of enterprise AI adoption, highlighting multi-model architectures, context engineering, human supervision, and the transition toward consumption-based AI Pods to deliver measurable business ROI.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Alex holds 11.6% of the talking time here. How this is scored →

Alex as informed peer 3.5 Guest teaching 3.5 Guest disagreement 1.0 Alex pushing back 0.5
05100:0010:0020:0030:004:54–9:44 · Alex as informed peer 1/10 Globant Enterprise AI Platform and Multi-Model Architecture Alex asks a broad opening question allowing Martin to lay out Globant's multi-model architecture. Martin delivers a lengthy, uninterrupted monologue about integrating 140 LLMs and creating agentic workflows.9:45–13:11 · Alex as informed peer 2/10 Enterprise Use Cases in E-Commerce, Procurement, and Coding Alex prompts Martin for tangible enterprise case studies. Martin walks through detailed examples across e-commerce returns, energy sector procurement, and automated legacy code migration.13:12–17:36 · Alex as informed peer 6/10 The Critical Role of Context Engineering and Human Supervision Alex pushes on how enterprises trust probabilistic automation, prompting Martin to explain context generation and supervisory guardrails. Alex then demonstrates strong technical insight by connecting this to conversations with an Amazon executive regarding Alexa Plus and Apple Intelligence context limits.17:37–26:40 · Alex as informed peer 5/10 AI Pods, Agentic Commerce, and the Evolution of Pricing Models Alex conceptualizes the shift to humans acting as auditors within bot workflows and presses Martin on how this changes traditional agency pricing models. Martin details Globant's supervised token pricing and compares it to AWS cloud adoption.4:54–9:44 · Guest teaching 3/10 Globant Enterprise AI Platform and Multi-Model Architecture Alex asks a broad opening question allowing Martin to lay out Globant's multi-model architecture. Martin delivers a lengthy, uninterrupted monologue about integrating 140 LLMs and creating agentic workflows.9:45–13:11 · Guest teaching 3/10 Enterprise Use Cases in E-Commerce, Procurement, and Coding Alex prompts Martin for tangible enterprise case studies. Martin walks through detailed examples across e-commerce returns, energy sector procurement, and automated legacy code migration.13:12–17:36 · Guest teaching 4/10 The Critical Role of Context Engineering and Human Supervision Alex pushes on how enterprises trust probabilistic automation, prompting Martin to explain context generation and supervisory guardrails. Alex then demonstrates strong technical insight by connecting this to conversations with an Amazon executive regarding Alexa Plus and Apple Intelligence context limits.17:37–26:40 · Guest teaching 4/10 AI Pods, Agentic Commerce, and the Evolution of Pricing Models Alex conceptualizes the shift to humans acting as auditors within bot workflows and presses Martin on how this changes traditional agency pricing models. Martin details Globant's supervised token pricing and compares it to AWS cloud adoption.4:54–9:44 · Guest disagreement 1/10 Globant Enterprise AI Platform and Multi-Model Architecture Alex asks a broad opening question allowing Martin to lay out Globant's multi-model architecture. Martin delivers a lengthy, uninterrupted monologue about integrating 140 LLMs and creating agentic workflows.9:45–13:11 · Guest disagreement 1/10 Enterprise Use Cases in E-Commerce, Procurement, and Coding Alex prompts Martin for tangible enterprise case studies. Martin walks through detailed examples across e-commerce returns, energy sector procurement, and automated legacy code migration.13:12–17:36 · Guest disagreement 1/10 The Critical Role of Context Engineering and Human Supervision Alex pushes on how enterprises trust probabilistic automation, prompting Martin to explain context generation and supervisory guardrails. Alex then demonstrates strong technical insight by connecting this to conversations with an Amazon executive regarding Alexa Plus and Apple Intelligence context limits.17:37–26:40 · Guest disagreement 1/10 AI Pods, Agentic Commerce, and the Evolution of Pricing Models Alex conceptualizes the shift to humans acting as auditors within bot workflows and presses Martin on how this changes traditional agency pricing models. Martin details Globant's supervised token pricing and compares it to AWS cloud adoption.4:54–9:44 · Alex pushing back 0/10 Globant Enterprise AI Platform and Multi-Model Architecture Alex asks a broad opening question allowing Martin to lay out Globant's multi-model architecture. Martin delivers a lengthy, uninterrupted monologue about integrating 140 LLMs and creating agentic workflows.9:45–13:11 · Alex pushing back 0/10 Enterprise Use Cases in E-Commerce, Procurement, and Coding Alex prompts Martin for tangible enterprise case studies. Martin walks through detailed examples across e-commerce returns, energy sector procurement, and automated legacy code migration.13:12–17:36 · Alex pushing back 1/10 The Critical Role of Context Engineering and Human Supervision Alex pushes on how enterprises trust probabilistic automation, prompting Martin to explain context generation and supervisory guardrails. Alex then demonstrates strong technical insight by connecting this to conversations with an Amazon executive regarding Alexa Plus and Apple Intelligence context limits.17:37–26:40 · Alex pushing back 1/10 AI Pods, Agentic Commerce, and the Evolution of Pricing Models Alex conceptualizes the shift to humans acting as auditors within bot workflows and presses Martin on how this changes traditional agency pricing models. Martin details Globant's supervised token pricing and compares it to AWS cloud adoption.

speaking balance: gold is Alex, purple is the guest (3 minute bins)

0:00 · Alex 16.7% · guest 83.3%0:00 · Alex 16.7% · guest 83.3%3:00 · Alex 2.8% · guest 97.2%3:00 · Alex 2.8% · guest 97.2%6:00 · Alex 0% · guest 100%6:00 · Alex 0% · guest 100%9:00 · Alex 8.7% · guest 91.3%9:00 · Alex 8.7% · guest 91.3%12:00 · Alex 7% · guest 93%12:00 · Alex 7% · guest 93%15:00 · Alex 41% · guest 59%15:00 · Alex 41% · guest 59%18:00 · Alex 8.1% · guest 91.9%18:00 · Alex 8.1% · guest 91.9%21:00 · Alex 7.9% · guest 92.1%21:00 · Alex 7.9% · guest 92.1%24:00 · Alex 10.7% · guest 89.3%24:00 · Alex 10.7% · guest 89.3%27:00 · Alex 12.1% · guest 87.9%27:00 · Alex 12.1% · guest 87.9%30:00 · Alex 93.5% · guest 6.5%30:00 · Alex 93.5% · guest 6.5%
Sharpest disagreement ▶ 20:10 Critique of Naive AI Code Generation

Martin sharply dismisses competitors' approaches to code generation, arguing they naively reinvent the wheel instead of integrating into massive existing code repositories.

 Hardest push from Alex ▶ 17:37 Challenging Workflow Integration Framing

Alex questions whether AI adoption means humans are merely becoming peripheral auditors inside automated bot workflows rather than active users.

 Biggest teaching moment ▶ 13:25 Explaining Context Engineering Over Deterministic Fixes

Martin educates the host on why probabilistic enterprise systems cannot be debugged like traditional software and must rely on precise context generation and human guardrails.

 Alex holds their own ▶ 16:01 Amazon and Apple Intelligence Industry Analysis

Alex displays his reporting depth by citing private discussions with Amazon executives on Alexa Plus to validate the technical bottlenecks of data context overload.
the scores for every segment, with the reasoning behind each
ChapterTopicAlex as informed peerGuest teachingGuest disagreementAlex pushing backWhy
Globant Enterprise AI Platform and Multi-Model Architecture 1310 Alex asks a broad opening question allowing Martin to lay out Globant's multi-model architecture. Martin delivers a lengthy, uninterrupted monologue about integrating 140 LLMs and creating agentic workflows.
Enterprise Use Cases in E-Commerce, Procurement, and Coding 2310 Alex prompts Martin for tangible enterprise case studies. Martin walks through detailed examples across e-commerce returns, energy sector procurement, and automated legacy code migration.
The Critical Role of Context Engineering and Human Supervision 6411 Alex pushes on how enterprises trust probabilistic automation, prompting Martin to explain context generation and supervisory guardrails. Alex then demonstrates strong technical insight by connecting this to conversations with an Amazon executive regarding Alexa Plus and Apple Intelligence context limits.
AI Pods, Agentic Commerce, and the Evolution of Pricing Models 5411 Alex conceptualizes the shift to humans acting as auditors within bot workflows and presses Martin on how this changes traditional agency pricing models. Martin details Globant's supervised token pricing and compares it to AWS cloud adoption.

Statements from this episode (7)

Insight
Migoya: Probabilistic AI breaks traditional deterministic debugging in enterprise software
“We're used to in the software industry, you know, when we have a problem, there's a bag associated with that problem. We go and fix the bag, and then the problem gets solved. Well, this is not the case any longer with, when you're talking with a probabilistic …”
Martin Migoya Oct 21, 2025 ▶ 3:25
Prediction Not checkable as stated
Migoya: Complex enterprise AI adoption will be slower than expected
“I think that this next generation wave of change will come. It will take a while, and it will be a slow, a slower than expected adoption on this complex side while the easy cases will be solved quite fast, very fast.”
Martin Migoya Oct 21, 2025 ▶ 4:32
Assertion Supported
Migoya: Globant platform integrates about 140 AI models
“Today we counted about 140 different models plus versions of the model that are integrated into our platforms.”
Martin Migoya Oct 21, 2025 ▶ 6:14
Insight
Migoya: Current AI models are already sufficient if given the right context
“The current systems we have, the current technology we have, if you put the right context, it is the right answer. And you can decrease a lot hallucination percentages just by creating the right context.”
Martin Migoya Oct 21, 2025 ▶ 17:12
Insight
Migoya: Next-generation AI flips workflow to AI supervised by humans
“The initial version of AI was humans accelerated by AI. The next generation is AI supervised by humans.”
Martin Migoya Oct 21, 2025 ▶ 21:23
Assertion Supported
Globant patented Copilot-like AI coding technology in 2020 but lacked funding
“And we have been using AI since the last 10 years. We got a patent in twenty-twenty very similar to Copilot. We didn't have enough money to be able to scale it up, but we knew the technology.”
Martin Migoya Oct 21, 2025 ▶ 25:12
Insight
Migoya: Only 5% to 6% of companies can implement AI independently
“There must be five or six percent of companies that are technically savvy to implement these technologies by itself, but therefore the rest of the people is like a jungle that is growing every day and doubling every day, in which you need to understand where t…”
Martin Migoya Oct 21, 2025 ▶ 27:23
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