Alex Lieberman, founder of Morning Brew, shares reflections on his latest venture — what he describes as "McKinsey for AI" — six months after launching. The business is 100% bootstrapped and, according to Lieberman, is the fastest-growing business he's ever started. He notes that it has pushed him harder than any previous endeavor.

The post teases a thread of lessons, musings, and reflections, opening with the observation that most engineering organizations are still 6 to 12 months behind in AI adoption. The full thread appears to explore the gap between AI's potential and how companies are actually implementing it, positioning his consultancy-style business as a bridge for that gap.

The Financial Times shares an opinion piece questioning the relevance of consultants in the age of AI. The article appears to explore how artificial intelligence is disrupting the traditional consulting industry, potentially replacing or diminishing the need for human consultants as AI tools become more capable of providing strategic advice, data analysis, and business recommendations that were once the exclusive domain of consulting firms.

Plainstack's guide targets independent consultants and freelancers — a group largely overlooked by mainstream AI tool coverage that tends to focus on enterprise buyers or venture-backed startups. The guide covers six specific high-impact jobs where AI tools can reduce unbilled time: proposal writing, scheduling, invoicing and bookkeeping, meeting transcription, email management, and data privacy. Each sub-guide follows a consistent structure with plain-language verdicts, vendor-sourced pricing, and honest assessments of fit, while explicitly disclosing that evaluations are desk-researched rather than based on hands-on daily use.

The guide grounds its relevance in concrete adoption data: 24% of small employers were using AI tools as of NFIB's March 2025 survey, while the OECD reports adoption jumped from 14.2% to 20.2% of firms between 2024 and 2025. A key insight is that solo consultants adopting AI for specific, genuinely useful tasks are still ahead of most peers, not behind them. The pricing landscape varies significantly by category — scheduling and transcription tools often have usable free tiers, while all-in-one business management platforms typically run $9–$29/month for solo-relevant features. The guide emphasizes transparency, with visible "as of" dates on pricing and quarterly review commitments.

Nathaniel Whittemore poses the question of how AI will transform consulting and professional services. He pushes back against the simplistic narrative that LLMs and AI agents will straightforwardly displace consultants and professional services firms. Instead, he argues the reality will be more nuanced than direct replacement, suggesting a more complex interplay between AI capabilities and the value that human consultants provide. The post appears to continue with a longer thread or linked content exploring this nuance further.

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?"

This article argues that the biggest mistake freelancers make with AI is using it merely to work faster without raising their rates. The real opportunity lies in leveraging AI for business development — particularly proposals, client research, and follow-ups — rather than just delivery acceleration. Citing Upwork's 2025 Freelancer Economy Report, the piece notes that frequent AI users sent 40% more proposals and reported 20–35% higher acceptance rates, driven by both increased volume and improved quality.

On the delivery side, AI handles what the author calls "scaffolding" — first drafts, slide deck structures, executive summaries, and research summaries — freeing freelancers to focus on the judgment and expertise that justify their rates. The article emphasizes that freelancers should price on value delivered, not time spent, so that when AI reduces a 20-hour project to 12 hours, the savings translate into more revenue capacity rather than lower fees. Administrative automation (meeting notes, SOWs, invoicing) can recover an estimated 5–8 hours per week, which at $100/hour represents $600 in weekly recovered capacity.

The core mental model is straightforward: AI replaces the commoditized parts of freelance work — not the relationships, specialized expertise, or original thinking that clients actually pay for. Freelancers who use AI to compete on proposal quality and deliver better work products are pulling ahead, while those using it to cut corners on delivery without raising their value proposition are falling behind.

Ashutosh Shrivastava shares an article about how AI is fundamentally disrupting the consulting business model. He highlights what he sees as the key insight: the traditional bottleneck in consulting was never intelligence itself, but rather context. Human consultants could only ever sample a small fraction of reality when analyzing problems and making recommendations.

With the emergence of AI agents and increasingly capable models, that constraint is being removed. These systems can process and synthesize far more information than human consultants, potentially undermining the core value proposition of traditional consulting firms. The post points to a broader shift in how knowledge work and advisory services may be restructured as AI capabilities continue to advance.

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.