Trends in AI Consulting

Stop calling yourself a “consulting firm.” That title is going extinct. We are also changing it :O If you’re still selling decks, manual analysis, and time-based retainers... You’re building a 2010s business in a 2030s world. Let me break this down based on what we’re seeing right now: Top firms are already shifting: BCG is piloting internal agent ecosystems for research + benchmarking McKinsey is training junior consultants to become prompt engineers Deloitte is building LLM-based decision simulators for client strategy They’re not replacing consultants. They’re augmenting them — turning smart people into insight orchestrators. The new consulting model looks like this: 1. Agent-powered discovery Research, insight summaries, opportunity analysis Tools: Perplexity AI, ChatGPT 2. Prompt-native consultants Deep domain expertise + ability to guide LLMs Trained on frameworks, not just templates 3. Simulated decision-making Predictive agents stress-test strategy recommendations Open-source projects like AutoSimulate are just the start 4. Productized service layers Playbooks turn into micro-platforms Deliverables evolve into dashboards, agents, automations Real example from our world: We helped a boutique consultancy deploy HR AI to: ✅ Analyze 5 years of hiring and retention data ✅ Build a talent intelligence dashboard ✅ Simulate the impact of hybrid policy shifts Result? - 40% drop in attrition - New $400K+ recurring revenue stream from productized delivery The uncomfortable truth? - Insight alone won’t be enough. - It’s insight orchestration that will win. Don’t fight the AI shift — build on it. You’re not just a consultant anymore. You’re a strategist, a systems thinker, and a conductor of agents. And the firms who lean in? They’re not going extinct. They’re becoming the platforms of the next decade. #AgenticAI #Consulting #LLMs #FutureOfWork #AIConsulting #LiquidTechnologies #Strategy

7 Enterprise AI Trends I’ve Been Seeing Over the past 30 days, I’ve had a bunch of conversations with teams across enterprises, MSPs, SIs, and ISVs. These aren’t headlines — they’re the themes that keep surfacing when people talk honestly about where AI is heading. Here’s what I’m hearing again and again: 1. Shadow AI is showing up Consumer employees are bringing tools like ChatGPT to work without looping in IT. Just like with Shadow IT, it’ is not-stoppable… but also risky — especially when it comes to data and compliance. 2. Big consulting firms are getting in early Top-tier consultants are already leading many enterprise AI initiatives. Everyone else is now trying to catch up and get in on the action. 3. Internal AI projects are getting stuck Lots of pilots aren’t making it past the experiment stage. Lack of security, observability, and governance is slowing things down. AIOps and trust layers are becoming essential. 4. AI budgets are starting to show up Many forward looking companies are budgeting for security in 2025, not just models. I’m also seeing more interest (and noise) for AI agents and MCP servers. 5. Under-performing Public companies want the AI premium Companies not viewed as “AI plays” are waking up — buying AI startups, hiring AI-native execs, partnering with AI startups and doing whatever they can to reframe their story. 6. Adoption is uneven across industries Tech, financial services, healthcare, and legal are way ahead of others. It’s a mix of urgency, regulation, and clearer ROI. Some verticals just have more at stake. 7. AI needs to fit how people work GenAI only works when it actually fits into real workflows. If it doesn’t connect to existing systems and roles, it becomes shelfware pretty fast. — My takeaway: We’re past the phase of just “playing with LLMs.” What matters now is execution — trust, scale, architecture, and making AI work where the business actually happens. What are you seeing in your world? Curious how this compares. P.S. Not one person I spoke with mentioned “machine learning.” That shift says a lot. — #EnterpriseAI #GenAI #AITrends #DigitalTransformation #MSP #SystemIntegrators #ISV #AIinBusiness #AIstrategy #AITrustScore Tumeryk

O'Reilly's Technology Trends for 2025 report, published today, is based on analyzed data from 2.8 million users on its learning platform, and giving insights into the most popular technology topics consumed - identifying emerging trends that could influence business decisions in the year ahead. The outlook for AI technologies is marked by dramatic growth in key areas. The percentages describe the growth in interest or usage of specific areas within the field: Prompt Engineering surged by 456%, AI Principles by 386%, and Generative AI by 289%. Additionally, the use of GitHub Copilot skyrocketed by 471%, highlighting a robust interest in tools that boost productivity. In terms of security, there was a significant 44% increase in interest in governance, risk, and compliance, accompanied by heightened attention to application security and the zero trust model. While traditional programming languages such as Python and Java experienced declines, data engineering skills witnessed a 29% increase, underscoring their essential role in powering AI applications. * * * Based on these numbers, the report analyses the Technology Trends for 2025 in the field of AI: I. Diverse AI Models: Unlike previous years when ChatGPT dominated, the field now includes a variety of strong contenders like Claude, Google’s Gemini, and Llama. These models have broadened the AI landscape and are each finding their niches within different user bases. II. Skill Growth: There has been a significant increase in interest and development in AI skills, notably in Machine Learning, Artificial Intelligence, Natural Language Processing, Generative AI, AI Principles, and Prompt Engineering. These skills are seeing varying levels of growth, with Prompt Engineering experiencing the most substantial surge. III. Shift in Platform Focus: Interest in GPT has declined as the industry moves away from platform-specific knowledge towards more generalized, foundational AI understanding. This shift reflects a maturation in the industry as developers seek capabilities that are applicable across various models. IV. Future Trends: The report anticipates potential disillusionment with AI, a phenomenon more sociological than technical, often due to overhyped expectations. Nonetheless, advancements continue, particularly in making AI interactions more intuitive and reducing the need for complex prompts. V. Development Tools and Data Engineering: Tools like LangChain and retrieval-augmented generation (RAG) are highlighted as key to building more sophisticated AI applications that can handle private data more securely and efficiently. Moreover, the importance of data engineering skills is underscored, supporting AI applications with robust data infrastructure. * * * The insights of the report can guide strategic planning, investment decisions, and curriculum development, and overall, offer a valuable snapshot of the technology landscape.