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With more enterprises deploying gen AI across business functions, a first-of-its-kind McKinsey survey provides insight into how leaders are increasingly turning to open source AI to build out their technology stacks.

Since the previous edition, we've reviewed hundreds of the latest agentic and gen AI best practices, case studies, and innovation insights. Here’s the top 1%...

In today’s edition:

• Mckinsey: The state of open source AI.

• BCG unpacks AI agents, MCP and agentic abundance.

• Deutsche Telekom scales hyper-personalized training.

• 2025: The year the Frontier Firm is born.

• LangChain CEO responds to OpenAI on agents.

• Transformation and technology in the news.

• Career opportunities & events.

Read time: 4 minutes.

MARKET INSIGHT

Mckinsey: The state of open source AI

Brief: McKinsey’s first-of-its-kind report informed by a survey of 700+ technology leaders and senior developers across 41 countries, explores how enterprises are increasingly turning to open source AI to build out their technology stacks.

Breakdown:

• Over 50% of firms use open source AI like Meta’s Llama or Google’s Gemma, often alongside proprietary options from OpenAI, Anthropic, and others.

• Firms that see AI as important to their competitive advantage are more than 40% more likely to report leveraging open source AI.

• Open source AI use is highest in tech, media, and telecom (70%). Experienced AI developers are 40% more likely to use it than their peers.

• 60% find open source AI has lower implementation costs, 46% cite lower maintenance costs, but proprietary AI has faster time to value.

• Top open source AI concerns include cybersecurity (62%), regulatory compliance (54%), and IP infringement (50%).

Why it’s important: Leading enterprises are well-positioned to harness open source AI where aligned to their unique context. As with cloud and software, a multimodel approach will be prevalent for many companies, with open source and proprietary AI coexisting across multiple layers of the technology stack.

BEST PRACTICE & MARKET INSIGHT

BCG unpacks AI agents, MCP and agentic abundance

Brief: Boston Consulting Group (BCG) published a 37-slide report on AI agents, covering how they are evolving, where they have product-market fit, how reliable and effective they can be, MCP’s role in agentic workflows, and building at scale.

Breakdown:

• BCG explores how agents are moving beyond simple 'if-statements' toward more autonomous agents and multi-agent systems.

• It outlines how coding agents are the first to reach product-market fit, with organizations realizing significant value from agentic workflows.

• Bloomberg’s compliance agents rigorously check facts and identify edge-case risks, reducing time-to-decision by 30–50%.

• BCG details six key dimensions for tracking agent performance, including reasoning and planning, task autonomy and execution, and more.

• It explains how MCP help unlock agentic workflows through one unified protocol and highlights the emerging role of agent-to-agent protocols.

Why it’s important: In less than half a year since its launch by Anthropic, the Model Context Protocol (MCP) has been rapidly adopted by OpenAI, Microsoft, and others. BCG's effort to unpack MCP’s role and its significance as a meaningful step towards broad applications of agentic systems in production is a valuable read.

CASE STUDY

Deutsche Telekom scales hyper-personalized training

Brief: Deutsche Telekom partnered with McKinsey to develop a gen AI-powered learning and coaching engine, helping to upskill 8,000 human agents in the field and call centers to better meet customer needs.

Breakdown:

• Deutsche Telekom saw that traditional learning programs were resulting in substantial variation in performance across agents.

• They sought to shift from reliance on individual coaching to an AI engine that would power hyper-personalized learning at scale.

• The team spent six weeks diagnosing agent needs with millions of data points, then four months building, testing, and refining the MVP solution.

• It’s built into agent workflows. For example, if an agent struggles with eSIM activation, they’re prompted to watch a quick training video.

• Operational efficiency has improved, and the likelihood of customers recommending the company has increased by 14%.

Why it’s important: Deutsche Telekom demonstrates how enterprises can quickly evolve to deliver scalable, efficient outcomes with AI. Deutsche Telekom SVP Peter Meier van Esch said, “The impact of this work has been profound,” with employees now better equipped to serve customers.

MARKET INSIGHT

2025: The year the Frontier Firm is born

Brief: Microsoft analyzed survey data from 31,000 workers in 31 countries, LinkedIn labor market trends, and trillions of Microsoft 365 productivity signals, revealing the emergence of an entirely new organization: the Frontier Firm.

Breakdown:

• Frontier Firms run on on-demand intelligence and hybrid teams of humans and agents scaling fast, staying agile, and delivering value faster.

• These firms are already taking shape, and within 2–5 years, most organizations are expected to be on their path to becoming Frontier Firms.

• 82% of leaders say this is the year to rethink strategy, and 81% expect agents to be integrated into AI plans within 12–18 months.

• Microsoft sees three Frontier Firm phases: first, every employee gets an AI assistant that helps them work better and faster.

• Second, agents join teams as "digital coworkers" taking on specific tasks. Thirdly, agents execute entire business processes, checking in as needed.

Why it’s important: The time for pilots alone has passed. Real change requires full-scale adoption. Define roles where automation adds value and treat digital employees like teammates. Identify processes for full automation and those best suited for human-AI collaboration.

BEST PRACTICE INSIGHT

LangChain CEO responds to OpenAI on agents

Brief: Harrison Chase, CEO of LangChain, the popular framework for building agents, responded to OpenAI’s new agent guide and Anthropic’s earlier release, sharing his perspective on agentic systems and how frameworks support them.

Breakdown:

• Chase critiques OpenAI’s vague agent definition, favoring Anthropic’s precise framing of agentic systems as workflows, agents, or both.

• OpenAI and Anthropic both note that agents aren't always needed; workflows are often faster, cheaper, more reliable, and simpler to implement.

• Chase discusses the spectrum of “agentic” behavior, where systems vary in how agent-like they are, depending on their use of workflows and agents.

• Agentic systems, whether workflows or agents, share many common features that can be provided by a framework or built from scratch.

• Chase also shared a comparison of 14 agentic frameworks, evaluating capabilities in AutoGen, OpenAI’s Agents SDK, CrewAI, and others.

Why it’s important: Amid the market hype, posturing and noise, even leaders like OpenAI, Anthropic, and LangChain offer nuanced views, in part reflecting the pace of change in the space. With little precise analysis on agents and frameworks, this contribution offers timely and valuable insight.

Sequoia explores the Always-On Economy, where AI removes temporal constraints, enabling transformation over the next 5-7 years.

Bain examines how AI winners develop the right tech stack, data foundation, and use cases, strategically integrating AI and co-creating with the front line.

BCG outlines how gen AI can transform ERP implementation, saving time on testing, training, documentation, and also delivering a better system.

Gartner answers key market questions, including whether to prepare for AGI, what MCP is, why DeepSeek is innovative, and what is Manus.

KPMG discusses boards’ roles in accelerating gen AI exploration, and helping management to implement policies and guardrails for responsible AI.

Deloitte shared a brief on demonstrating market readiness of AI systems to help comply with global regulatory requirements.

IBM claims a $3.5 billion productivity boost through AI agent use and releases a video outlining the key differences between generative and agentic AI.

Genpact shares details on its Agentic Insourcing solution, leveraging agents to improve business processes in response to uncertainties such as tariff changes.

OpenAI launched its GPT-Image-1 model via API, enabling developers to integrate ChatGPT's image capabilities into third-party apps and platforms.

Huawei is reportedly preparing shipments of its new AI chip, the 910C, rivaling Nvidia’s H100 and aiming to fill the void left by U.S. export restrictions.

Meta partnered with Booz Allen to deploy a fine-tuned Llama 3.2 model aboard the International Space Station (ISS) National Laboratory.

NVIDIA's NeMo microservices are now generally available, serving as building blocks for AI agents leveraging reasoning models like Llama Nemotron.

OpenAI introduced Flex processing, halving API costs for o3 and o4-mini models in exchange for slower response times.

Adobe expanded its Firefly AI platform, introducing two new image generation models, third-party integrations, and an upcoming mobile app.

The Washington Post partnered with OpenAI to bring summaries and links from its reporting directly into ChatGPT responses.

BMW plans to integrate Chinese startup DeepSeek’s AI models into its new vehicles in China starting later this year.

CAREER OPPORTUNITIES

Databricks - AI Industry GTM Lead

CrowdStrike - Enterprise AI Senior Director

C3 AI - Group Vice President

EVENTS

Infosys - AI Omnichannel Marketing - April 30, 2025

AIAI - LLMOps Summit - May 29, 2025

Gartner - AI Customer Experience - June 10, 2025

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