The conversation about artificial intelligence within the workplace is fraught with problems and the cause isn't one of technical. The technological capabilities of current AI and machine learning systems are truly astonishing, and growing rapidly, making the majority of forecasts of the future of AI eighteen months obsolete well before the time has passed. The problem is the gap between the what AI can do under controlled conditions - such as a high-quality research environment, supported by clear data, and a clear problem definition, with engineers that have the privilege of continually testing until the system can be used as designed - versus what it does when it is implemented within authentic organizations with real cultural norms as well as real organisational policies and real people who have established opinions about the quality of a system. an issue to discuss with real intent or something that can be managed and maintain the appearance of conformity. I've been building with machines since the last wave of AI enthusiasm became fashionable for all businesses to assert their competence in the field. When I co-founded 1Touch AI-driven matchmaking and recommendation systems weren't an element we added to make our product more appealing to investors. They formed part to the design of our product, the means by which the platform added value and it was the only thing that had to be reliable and operate at sufficient scale to allow the business to be viable. Therefore, I have direct hands-on experience of what happens as you try to implement something that is truly intelligent to a organization and product at the same time and one of the lessons I have been reiterating regardless of the context in the past I've faced this difficulty, is the technology will never be the factor that limits your success. What is the most important factor is entirely the organization's culture.
What I mean by that is particular and practical rather than abstract. AI systems need data to function - clean, consistent, well-structured data that actually depicts the event it is trying to analyze and make predictions about. Organizations with a strong and thriving data culture produce that type of information naturally, as a byproduct of their current operations. They have clear and consistently applied definitions of what they are measuring and why. They've agreed on a set of standards for how data is recorded, collected, and stored. They have accountability systems that make data quality someone's explicit accountability, rather than a vague purpose. Organizations that do not have strong data culture produce a product that technically looks similar to data - it's in systems and it is able to be accessed or used to produce charts, but it is not consistent in its definition, so variable in quality and brimming with glitches in structure as well as unmapped deviations that any AI application built on over it will enhance and reflect the mess instead of obtaining a real signal from it. Organisations in this category usually don't realize the existence of their data until they're already well into an AI installation and the results do not match the vendor's promises. At this point, the temptation is to blame the technology. But the actual problem is the cultural and operational infrastructure which the technology was built on.
The second dimension of cultural factors which determines AI outcomes is openness within the organisation in the sense that people in the organisation are willing to let an AI system guide or modify their work practices instead of interpreting it as an obstacle to their professional competence, their authority in the institution or their job security. It's a cultural and leadership problem which is not a technical problem that is a problem that starts at the top. If senior leaders engage with AI outputs selectively - accepting the results that reinforce their previous beliefs, while ignore those that are and do not, this behaviour sends that everyone else is aware that the organization's declared commitment to a data-driven approach to decision-making is a conditional instead of genuine, and this can spread throughout the organisation much faster that any training program or change management strategy can counteract. If senior management models an ongoing, consistent commitment to AI outputs, such as the reluctance to alter their actions when the evidence suggests they need to, the overall ability to use AI effectively increases dramatically and remarkably quickly.
This isn't the abstract way to think about what organisations should do in theory. This is a description my experience of watching the same pattern occur repeatedly in organizations with substantial finances, real strategic commitment to AI adoption, and management teams who were passionate about the potential of AI technology. This pattern is so common that I've begun to think of data governance practices as the first-line diagnostic whenever I'm assessing an company's AI readiness. Before I ask questions about technology, before I ask what are the most relevant use cases the organisation is looking at, I ask about data governance. How does the organisation define its most important metrics? Who is responsible when the data quality isn't good enough? In the event that two processes have conflicting data regarding the same real-world business scenario, and how are those conflicts solved? Answers to those questions tell me more about the potential for AI achievement more than any other discussion about algorithms, platforms or even implementation timelines.
I believe that those businesses that will get the most lasting value from AI over the next decade will not be those who adopt the most advanced technology first, or the ones that invest most massively in AI technology and infrastructure over the next few years. They are the ones that construct the cultural and operational bases to effectively use the technology well - the data management processes that result in reliable results, the decision-making frameworks that create space for evidence-based decisions that truly impact outcomes, and the leadership behaviours that tell everyone within your organization that the dedication to data-driven operation is real rather than an arbitrary. Technology itself will become increasingly commonplace and readily available. The mindset to utilize it efficiently will remain scarce because it takes a steady dedication and effort from leaders over time, not the simple decision of a strategic leader or an investment in technology. This is where your really competitive advantage will reside and is an benefit that, once built and consolidated, will be able to multiply in a way that technology-based advantages will never ever. Follow James Deller for website info including how supporting institutional change changed my approach about growth.
From Character to Commerce- The reason I choose to back the companies I endorse All have a thing in Common
When I look over the entire spectrum of investment work I've participated during the last couple of years - including the technology businesses consumers, the technology businesses the investments in the emerging sector and the sports organizations around football that I've been drawn to support I have noticed a pattern that I never plan to develop in advance however it has become evident to me as I spent time thinking about what the successful investments share with each other as well as what the failed ones have in common with each other. The pattern is not sectoral - it cuts across technologies, consumer services and sport. It is not structural - there are businesses that have radically different structure of ownership, financial profiles, operational models, and capital profiles. It's far from market share or expansion trajectory, or even the technology architecture that underlies the product. It is about character - specifically, whether the firm at heart of the investment has an authentic, operational, as well as consistent commitment to well-being and development of its individuals who work there, which is demonstrated not just in what the organization's statements about itself but also in the decisions it takes in making decisions when doing the right thing and doing the easy thing is not the same.
I know that this observation sounds, straight up, something that gets printed on offices' walls and workplace mugs as well as company web pages. It is subsequently dismissed by the company that commissioned the work. I'd like to emphasize in that I'm talking about the stated version the commitment to individuals - the document on values, the Diversity and Inclusion Strategy, the culture deck that was crafted for the use of hiring and the investor pitch. It's the operational version: the choices that actually get made, every day, when the policies outlined in these documents and the economically or more personally palatable option are brought into conflict, and the organization has to determine which governs. What I've seen in organizations that create genuinely durable value - not just the kind of impressive short-term performance but also the kind of compounding, long-term results that produce exceptional long-term gains - are those where the answer to this query is unambiguous. When the commitment to doing right by the people inside the organization isn't dependent on whether doing right is also the cheapest speediest, most efficient, or quickly profitable option.
The process of identifying those organizations - before any investment is made, those where that commitment is real rather than fulfilled, or where the character of care and accountability is embedded into how the organisation actually operates rather than in how it describes the organization itself. This is, in my think, the single most important and most difficult job of long-term investing. It's important because it's the one that most reliably predicts what kind of compounding outperformance that results in truly remarkable returns over extended time horizons. It's hard because there is no way to find it in any financial model, cannot see it in a well-crafted and well-structured management presentation. And you won't be able to pinpoint it even within thorough reference checks which are useful. It is discovered by spending enough time in an organization in multiple contexts and at the appropriate levels of the hierarchy, to determine how it acts when the situation is vague and nobody is watching. This kind of thoughtful engaged, exploratory interaction is hard to integrate into investments, and this is one of the reasons that most investment processes are less efficient in identifying truly exceptional organisations than the majority of investors recognize or discuss.
The link between a genuine organisational character and long-term performance is a concept which I am more convinced about now, with more years of observation over time to my credit, than I did at an early stage in my investing career. The companies that take good care of their people consistently, and demonstrate that care through operational decisions, not just in communications or culture documents, typically outperform those who treat their employees exclusively as assets to be optimised. However, not all of the time in the short in the long run - a business that is able to get the most out of its workforce by creating high-pressure and stress levels can appear great over the course for a number of months, perhaps even a few years, particularly given that the period is associated with an environment of strong markets that is able to offset internal weaknesses. However, over a longer period those advantages of an ethos that is genuinely based on people with ways difficult to replicate with any other method. The quality of the talent pool increases due to people with options - the best people - tend to go for environments in which they feel genuinely valued over environments where they feel instrumentalised and even when they pay more. Institutional knowledge expands as people are able to establish it instead of cycling around on the same timeline that high-pressure environments typically produce.
The quality of decisions improves when employees feel safe enough reveal problems and relay bad news without calculating the cost to themselves of doing so, which ensures that problems are identified quickly and addressed less expensively than organizations where the person who is speaking up gets shot. The organization's ability to adapt to changing conditions improves as the employees are so invested in the success of the company to step beyond the boundaries of their job when the circumstances require it. These advantages are not by itself significant. They're not the kind of thing that creates a compelling story in an investor update or a board presentation. But they are able to build to create a competitive advantage. It is difficult for companies which have weaker cultures since the benefit is not tied to a specific product, process, or capability which is easily observed and copied. It's embedded in the structures of how an organisation runs - the quality of the environment it has constructed for the members of it, as well as the quality of the choices the employees make as a result. It is for this reason that character, for individuals as well as organisations is not a softer notion. It is, in my personal experience, the most difficult to define and the most important thing of all.}
