Urgent.News

What's breaking now, across thousands of outlets.

AI

Consulting's race to become AI native

How much is the drive to be "AI-native" changing the core of traditional consulting?

The consulting industry is in a race to become AI-native, as artificial intelligence reshapes clients' expectations and the way work gets done. Traditional consulting firms, once known for crunching numbers and providing generalist support, are now expected to deliver tangible tools, systems, and ongoing assistance. Major players like KPMG, PwC, EY, and McKinsey have rewritten their training agendas to focus on AI-centric skills while embedding AI across their traditional offerings.

Consultants are increasingly being hired for their technical expertise, and companies are partnering with AI leaders to build internal tools and AI-enhanced platforms for clients. The lines between technology and consulting are blurring, with AI now accounting for over 40% of the revenue at firms like BCG and McKinsey.

Written by urgent.news from Business Insider's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at businessinsider.com →

More in AI

OpenAI Cuts GPT-5.6 Luna and Terra Costs, Reshaping API Budget Planning

OpenAI has reduced usage costs for two GPT-5.6 model variants, cutting GPT-5.6 Luna pricing by about 80% and GPT-5.6 Terra pricing by about 20% .

  • GPT-5.6 Luna costs cut by 80%, Terra costs cut by 20%
  • Introduces Sol Fast mode for GPT-5.6 Sol, 2.5x faster at double cost
  • Encourages reevaluation of AI workload assumptions and model routing

Agentic AI That Survives the Enterprise, Part 1: Probabilistic Engines, Deterministic Businesses

Enterprises run on workflows that must be auditable, explainable, predictable, and correct. A single arithmetic error is not a quirk. It's a financial loss.

  • Enterprises demand auditability, explainability, and accuracy in workflows.
  • Probabilistic LLMs struggle with guaranteeing outcomes, unlike traditional machines.
  • Architectural issues, not model limitations, cause most enterprise AI failures.

More from Saturday 22 August →