What Decades in Technology Reinforced Operational Discipline About Character

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AI Can Only Be As Effective As The Culture It's Constructed Into
The conversation about artificial intelligence in the business world has a problem The issue isn't one of technical. The technological capabilities of current AI and machine-learning systems are remarkable, growing with a speed that makes most predictions on how they will perform in just 18 months obsolete long before the period of eighteen months has expired. The problem is the gap between the capabilities of AI and what AI can do in controlled conditions - in a properly-funded research environment, with pure data, with clarified problem statement, and engineers that have the privilege of continually testing until the system can be used as designed - versus what it delivers when it is used in authentic organizations with real cultural norms, real organisational politics, and people with their own established views on whether the new system is something to be engaged with or something to reroute around but still presenting as compliance. I've been developing with Machine Learning since well before the flurry of AI enthusiasm became fashionable and commonplace for companies to declare their proficiency in the area. When I founded 1Touch with my partner, AI-driven matches and recommendation systems were not a distinctive feature we added to make the platform more appealing to investors. They were at the heart to the design of our product, it was the basis on which it created value for the users, and the thing that had to be reliable and operate at sufficient scale to allow the company to succeed. Also, I've gained direct real-time experience of what happens when you are trying to build something truly intelligent into a service and an organization simultaneously, and the lesson I have been reiterating each and every circumstance in which I've had to face this issue, is that the technology is almost never the most important factor. It is always the culture.
What I do by that is specific and concrete, not abstract. AI systems require data in order to perform - clear, consistent properly-structured data which describes the situation the system is trying to identify and make forecasts about. Companies with a strong culture of data produce that kind from the beginning, as a result of how they already operate. They have clear and consistently applied definitions of what they are collecting and the purpose for which they're doing it. They have a set of conventions that they agree to for the way data is collected, recorded, and stored. They have accountability frameworks that provide data quality as an explicit task rather than the general motives. Companies that lack strong data cultures produce something that appears like data. It's in systems and, if it's able to be accessed, it can be used to produce charts - but does not have a consistent definition as well as in its quality, and so full of mistakes in structure and unmapped errors that any AI application built on the top of it will mirror and magnify the mess rather than extracting genuine signals from it. The companies in that segment often don't realise they exist until they're well into an AI implementation and the outputs don't match the vendor's promises, at which point the temptation is to blame the technology. However, they are actually causing the problem by ignoring the culture and operational framework the technology was built on.

The second aspect of culture that influences AI outcomes is openness within the organisation - the extent to which people within the company are willing to let systems inform or change how they work, rather than treating it as an obstacle to their professional expertise, their institution's authority or even their job security. This is a cultural and leadership problem that is not technical which is a matter that begins at the highest level. When senior leaders are able to engage with AI outputs selectively, embracing the results that affirm what they believed before and not focusing on those that do not - their actions send the impression to everyone who watches that the commitment of the organisation to data-driven decision-making is conditional rather than true, and this conditionality will be passed throughout the organization faster than any training programme or change management initiative can neutralize. If senior management models an authentic, consistent approach to AI outputs as well as the discipline of changing their choices when evidence suggests they must, the whole organization's capability to utilize AI effectively increases dramatically and surprisingly quickly.

This isn't an abstract description of the behavior of organizations in the context of theory. It is a description of the pattern I have watched happen repeatedly in companies with significant funding, a true strategic commitment to AI implementation, and leadership teams who were truly enthusiastic about the possibilities of AI technology. The pattern is consistent enough so that I've adopted the practices of data governance as a fundamental diagnostic factor when evaluating an organization's AI potential. Before I inquire concerning the technological stack before I ask questions about the specific applications the company is considering, I ask about data governance. What is the definition of its most important metrics? Who's responsible when performance of the data isn't enough? When two different groups have contradicting data about the same situation in business and how can the conflicts resolved? Answers to those questions will reveal more about the probability of AI achievement more than any other discussion about algorithms, platforms or the timeframe for implementation.

I am convinced that the companies that will generate the most durable value from AI over the next decade are not the ones that embrace the latest technology first, nor the ones who invest the most massively in AI infrastructure or talent over the next few years. They are the ones that establish the organizational and cultural foundations to be able to use this technology effectively. This includes the data governance practices that generate reliable results, the decision-making frameworks that allow proof that actually influences outcomes and the leadership actions that signal to everyone in your organization that the dedication to data-driven operation is real rather than merely functional. The technology itself will be becoming more readily available and less expensive. The culture to use it well will remain scarce, as it demands a constant efforts and commitment from people in leadership for a long time rather than a single strategic decision or an investment in technology. This scarcity is where the most competitive advantage will be and it's an advantage that once created can be built upon in a way unlike the advantages of technology alone will. View James Deller for more advice including how developing people at scale confirmed what i suspected about lasting impact.



The Reason Why The Majority Of Public-Private Partnerships Fail Before They Even Begin - And How To Fix Them
Public-private partnerships are a perception problem that's, in large part of the time, earned. The history of these partnerships is filled with projects that were presented with enthusiasm and significant financial backing from the political establishment, took up a lot of public and private resources over a long period, which in the end produced outcomes with only a slight resemblance to what had been made clear when the alliance was launched. The academic literature and the postmortem examinations that governments as well as institutions undertake following the errors are comprehensive, and they concentrate on the predominant, on the structural and contractual aspects of what went wrong that resulted from the misaligned incentives an insufficient risk allocation between public and private parties along with the governance arrangements that were designed in the theory but never worked in practice, and the structures for procurement that decided to choose the wrong items. What this analysis tends to neglect, invariably and ultimately to the detriment of culture is the operational aspect - the fact that private and public organisations are genuinely different kinds of entities, formed with different reward structures that operate with different timescales, accountable to different stakeholders, and measuring results in ways that are not only different in extent but also different in nature. When you bring the two types of organisation together in a formal partnership, without undertaking the work upfront and clearly, to comprehend and manage those differences, you are not creating an agreement. It is creating the right conditions for a collision in slow-motion that is likely to be noticed at the worst possible time.
I've participated with advisory work in support of institution modernisation initiatives, some of which involve public-private partnerships at different levels of complexity. The most reliable conclusion I can make from that experiences is that the partnerships that performed well - that did indeed meet their declared objectives and maintained a functional working relationship between the private and public sectors throughout they were not distinguished from the ones that failed because of the sophistication of their legal structures, the strictness of their risk-management frameworks or the seniority of the leaders who initiated them. There was a distinct difference in how the participants from both sides of the table had been able to fully understand how the other side operated before the formal partnership framework was approved. What this means in actual practice is understanding the process of decision-making that each organisation operates under as well as the accountability structures that govern what parties must agree to and how quickly and efficiently they can do so, the criteria of success which both parties will be judged on, and the points of likely tension between those definitions. None of that understanding is difficult to build. All of it is overlooked in favor of the clearer and faster recorded work of negotiating contracts and designing governance frameworks.

The common public-private partnership model is a gradual process from concept to signed agreement with remarkably little focus on the issue of whether the two organizations involved are actually capable of working together effectively over the life of the agreement. Legal teams negotiate the contract. Finance teams model the economics and risk-adjustment. The communications team plans an announcement for the day of signing. The implementation team is beginning to plan the project. In that same sequence there is a discussion about compatibility between operational and cultural aspects - on whether the employees who will cooperate day-today across the borders between the two organizations have enough in common to ensure that work genuinely collaborative rather than adversarial - is not likely to take place in a structured way. It is usually assumed, with no explanation, that the formal agreement sets the prerequisites for effective collaboration and that any cultural or operational differences will be managed informally as they occur. That assumption is almost always wrong, and the financial cost of it can increase according to the ambition and complexity of the partnership.

The practical application of this analysis is that the best investment a public-private partnership could undertake - before the legal framework is finalized and before the governance model has been agreed upon, or before any announcements are made - is through what I would refer to as operational alignment. This is a specific, structured, facilitated work that identifies the points where the two organisations operate from different assumptions, and then to establish a clear understanding of how those divergences will be dealt with before they become operational issues upon implementation. The factors that are most crucial to consider generally are the same for different kinds of partnerships. In terms of speed and authority, they are often among them. Institutions of public administration are designed to make decisions in a slow manner, with multiple layers of analysis and approval, for reasons which are completely legal and are often mandated by law. Private organizations, especially technology businesses built on rapid iteration, and fast making decisions - often find the slow pace as a primary obstacle to progress, and without a mutual understanding of how the pace works it is and what truly be needed to change it, the anger that can be felt on the personal side can undermine the relationship before the partnership has established its own footing.

Success metrics and the factors that count as progress are an additional as well as a cause for divergence. Public institutions are usually judged by their compliance with processes, the equity of outcomes across stakeholder groups, as well as the reduction of the risk of failings that attract political or media attention. Private parties are usually assessed on efficiency, progress that can be measured towards goals, and performance. The measurement frameworks can be constructed to be compatible However, this requires carefully designed and thought-out intentions. Those partnerships that do not invest in that type of design will come across, at critical moments, with two different parties who measure the same collaboration in genuinely inconsistent ways and thereby coming to an incompatible conclusion about whether it is working. The partnerships that I have seen do not succeed the most ones where the issue was assumed to be resolved over time. They that worked were those in which the issue was clearly identified from the start, and when developing a shared accountability framework which accommodated both parties' legitimate measurement requirements turned into an actual work rather than an item on the list of things that one could eventually achieve.}