Aaron Levie, CEO of Box, shares observations from a week of meetings with IT and AI leaders across major enterprise sectors including banking, media, retail, healthcare, consulting, tech, and sports. The focus of these conversations was on agents in the enterprise — the next phase of AI adoption.

Levie signals a key industry shift: enterprises are moving from the chat era of AI to an agentic model, though the full thread of takeaways is truncated in the archived content. The post reflects growing momentum around AI agents as the central topic among large enterprise decision-makers, suggesting this transition is becoming a strategic priority across industries.

This article from SQORUS, a digital transformation consulting firm, explores how the labor market is evolving as employees, consultants, freelancers, and AI agents increasingly collaborate within the same organizations. Drawing on a conversation with Frédéric Brousse, Managing Director of Enterprise EMEA at Malt, and data from Malt's Tech Trends 2026 study, the piece highlights that freelancing has matured well beyond a stopgap solution — 94% of European freelancers previously held permanent positions, and large corporations are now structuring formal relationships with independent talent.

The article identifies a critical shift: companies no longer prioritize the type of service provider but rather the ability to solve problems by combining business, technological, and organizational expertise. The rise of "super-connector" profiles — professionals who blend domain knowledge (marketing, finance, HR) with automation and AI skills — reflects this trend. Expertise alone is losing its standalone value; what matters increasingly is the ability to bridge disciplines.

Looking toward 2027, three key expectations emerge: mastering AI within one's own field, the ability to coordinate multiple areas of expertise (evidenced by rising demand for part-time CTO/CDO roles), and a sharp focus on impact and operationalization rather than prototypes and demos. AI has become the second most in-demand skill on Malt's platform, with AI agent-related postings growing 60-fold since 2024. The article concludes that the central question for companies is shifting from "who will do this task?" to "what combination of human and technological expertise will drive this transformation?"

At the 2023 MIT Digital Technology and Strategy Conference, ten startups showcased how they are leveraging artificial intelligence to drive digital transformation across diverse industries. The companies span a wide range of applications — from Cleanlab, which uses AI to find and fix errors in datasets, to Zapata, which focuses on industrial generative AI for solving enterprise-scale problems using both language and numerical models. Other notable startups include CoCoPIE, streamlining AI deployment on mobile and IoT devices; Hopara, creating affordable digital twins; and MontBlancAI, applying anomaly-detection algorithms to manufacturing.

The lineup highlights the breadth of AI's current impact: Covariance builds competitive intelligence profiles by combining first-party, third-party, and public data; Einblick uses generative AI to help users write code and build models collaboratively; serviceMob provides AI-driven analytics for customer service operations; TechNext uses machine learning to assess emerging technology investment opportunities; and Wise Systems improves last-mile delivery routing. Together, these startups illustrate how small, agile companies are tackling significant challenges in data quality, logistics, manufacturing, and business intelligence by building on the latest advances in AI and machine learning.

Jim Lecinski, a marketing professor at Northwestern University's Kellogg School of Management, presents a four-quadrant framework for how CMOs should think about AI's role in marketing strategy. He argues that most organizations limit their AI efforts to internal productivity (automated reporting, content creation) and external productivity (chatbots, customer service automation), which deliver quick wins but cap AI's strategic potential. The more transformative use cases lie in internal growth — where AI powers advanced forecasting, scenario planning, and resource allocation — and external growth, where predictive personalization and data-driven product innovation create new demand and strengthen customer lifetime value.

Lecinski's core message is that treating AI solely as a cost-saving mechanism is a strategic mistake. Leading CMOs use productivity gains to fund and build toward growth initiatives, asking bigger questions about how AI can improve strategic judgment, unlock new business models, and create long-term enterprise value. He encourages marketing leaders to audit where their AI resources are concentrated and deliberately rebalance toward growth-oriented applications. The distinction he draws is sharp: productivity builds momentum, but growth builds competitive advantage.

Julian Harris shares a reflection on the quote "Automation is the last step, not the first," calling it profoundly relevant to why companies are slow to embrace AI. He highlights that the process begins with understanding how things are done today — a phase so critical that consultants dedicate entire discovery phases to it, which alone can be costly and time-consuming.

The post underscores a key insight for organizations pursuing AI adoption: before automating any workflow, companies must first thoroughly map and comprehend their existing processes. This sequential approach — understand first, automate later — is presented as a fundamental reason why enterprise AI transformation takes longer than many expect.