While 71% of organizations have deployed generative AI in at least one business function, fewer than 5% of AI pilots achieve meaningful revenue impact. This gap between experimentation and production success represents both the greatest challenge and opportunity for technical leaders in 2025.
Read moreAs AI-native transformation reshapes the technology landscape, organizations must rethink not just their tools but their entire structures and processes. Simply adopting AI technologies is not enough. True transformation requires organizational change, guided by a deep understanding of both technical feasibility and business value. Our AI consulting team helps organizations navigate this complex transformation journey.
Read moreEnterprise AI Assistants unify disparate data sources, providing real-time insights and access control. Building domain-specific assistants and orchestrating them (hierarchical or federated) offers scalability, specialized features, and better performance than single-vendor solutions. Ultimately, organizations need a tailored approach that consolidates knowledge, fosters collaboration, and addresses evolving AI integration challenges.
Read moreIn this reflective podcast, the team at Winder.AI — Dr. Phil Winder, Charles Humble, and Jonathan Hunter — take a deep dive into their year, discussing trends, lessons learned, and their vision for 2025.
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Join Phil Winder and Charles Humble in this insightful interview as he delves into the critical components of an enterprise AI strategy, based upon his excellent article on the subject.
Gain actionable insights on aligning AI with your business goals, building robust data infrastructure, and prioritizing projects based on ROI. Learn about MLOps, AI security, and the role of generative AI and reinforcement learning across industries.
Read moreAI offers considerable benefits across almost every commercial enterprise, from efficiency gains to the potential for brand new products. But it also represents a threat, opening up possibilities for new entrants to disrupt established markets.
In business environments, company executives look to produce an ‘AI Strategy’ but can lose sight of their objectives, rather like a politician thinking, “Something needs to be done and this is something, so let’s do this.”
Read moreWinder.AI helped Neste develop their data science strategy to nudge their data scientists to produce more secure, more robust, production ready products. The results of this work were:
Neste is an energy company that focuses on renewables. The efficiency and optimization savings that machine learning, artificial intelligence and data science can provide play a key role in their strategy.
Read moreData Science has become an important part of any business because it provides a competitive advantage. Very early on, Amazon’s data on book purchases allowed them to deliver personalised recommendations whilst customers were browsing their site. Their main competitor in the US at the time was Borders, who mainly operated in physical stores. This physicality prevented them from seamlessly providing customers with personalised recommendations [1]. This example highlights how strategic business decisions and data science are inextricably linked.
Read moreWhen I think about my business, I often end up at one fundamental business truth. The goal of any business is to make profit. This is achieved by either making more money or reducing costs. Often, business activities are outsourced to experts in order to achieve one of these. For example, I outsource my accounting to an expert, in order to a) ensure that I’m being tax efficient (save costs) and b) because I lack the expertise and the time/desire to obtain it (save costs).
Most traditional outsourcing routes (e.g. accountancy, recruiting, etc.) affect either making more money (e.g. marketing) or reducing costs (e.g. recruiting). Outsourcing research and development is different, in that it can benefit your business in both of these ways.
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