How Working Across Industries Deepened My Conviction About People About People

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AI Is Only As Effective As The Society It's Created In
The discussion around artificial Intelligence in the field of business is not without a problem and the cause is not technical. The capabilities of modern AI and machine learning technologies are impressive. They are developing rapidly, making most forecasts about what they'll look like in about 18 months obsolete well before the eighteen-month period has ended. The problem is the gap between the capabilities of AI and what AI can achieve under controlled conditions – in a properly-funded research environment, with good data and a clearly defined problem, and with engineers who have the luxury of testing the system until it works as expected - and the results it can provide when it is implemented within real organisations with real cultures which are real, with real organisational political systems, and people with an established view of how a new program is something to take seriously or something that can be managed but still presenting as conformity. I've been working with algorithms since just before the present wave of AI interest made it fashionable for every business to proclaim their fluency in this field. When I co-founded 1Touch with my partner, AI-driven matches and recommendation systems weren't a differentiating feature we added to make our product more attractive to investors. They were at the heart of the architecture of the product, being the primary mechanism that the platform produced value and needed to work reliably and at size for the business to function. This means I've got direct, real-time experience of what happens when you are trying to build something truly intelligent into business and a product at the same time And the point I continue to revisit each and every circumstance in which I have encountered this issue, is the technology will never be the most important factor. The main factor that limits the possibilities is almost everything else, including culture.
What I say is concrete and not abstract. AI systems need data to perform their functions - clean, consistent well-structured and structured data that is the thing the system is trying to analyze and make predictions about. Businesses with strong data culture produce this type of data automatically, as a result of how they operate. They have clearly defined and consistently implemented definitions of what they're collecting and the purpose for which they're doing it. They've negotiated conventions regarding how data is collected, recorded, and stored. They have accountability structures that allow data quality to be a distinct duty, not merely a vague intent. Organisations without strong data cultures produce something that appears as if it is data - it's in systems which can be searched, and it is used to produce charts, but is so inconsistant in definition, and therefore variable in quality, and so full of problems with structure and non-mapped exceptions that any AI application built on the top of it will reflect and amplify the underlying mess instead of drawing a real signals from it. Companies in this category usually don't realize they exist until they're already well into the process of implementing an AI implementation and find that the results don't meet the vendor's promises. At this point, it is tempting to blame technology, but the real issue is the organizational and cultural foundation the technology was built on.

The second dimension of culture that decides AI outcomes is openness within the organisation - the extent to which the people inside the organisation are truly open to letting any system or process inform the way they operate and not view it as the threat to their own professional know-how, their institutional authority or their security at work. This is a moral and leadership issue which is not a technical problem and one that begins at the highest level. When senior leaders are able to engage with AI outputs only in a selective way - embracing the results that affirm the assumptions they had previously made and disregarding those that do not – their behavior sends an indication to anyone who is watching that the company's commitment to decision-making based on data is a conditional rather than genuine and this can spread throughout the company more quickly than any training programme or change management initiative could help to stop. If senior leaders exhibit genuine, consistent engagement with AI outputs, as well as the ability to make changes to their decisions when the evidence suggests they need to, then the company's ability to use AI effectively increases significantly and is able to be done so quickly.

This isn't the abstract way to think about how organizations should be conducted in the context of theory. It's a description of the pattern I've witnessed be played out in a variety of organizations that had substantial funds, genuine strategic commitment to AI adoption, as well as leadership groups that were fully enthusiastic about the potential of the technology. The pattern is so consistent that I've decided to treat practice of governing data as a first-line diagnostic when evaluating an company's AI preparedness. Before I ask what the current technology stack is, before I inquire about the exact usage cases the company is looking at, I ask about data governance. How does the organisation define its most important metrics? Who is responsible if the performance of the data isn't enough? What happens when two processes have conflicting data regarding the same reality in business, and how is that conflict resolved? The answers to these questions provide more information about the potential for AI successful in comparison to any discussion about platforms, algorithms, or implementation timelines.

I believe that the businesses which will benefit the most long-lasting value from AI in the coming decade will not be those who implement the most advanced technology first, or the ones that are investing most heavily in AI infrastructure and talent over the next few years. They will be the ones who create the operational and cultural bases to effectively use the technology correctly - the information governance practices that give reliable information, the decision-making frameworks that create space for the evidence to truly influence outcomes, and the leadership behaviours that tell everyone within the company that commitment to a data-driven business is genuine instead of merely a matter of performance. The technology itself will be becoming more readily available and less expensive. However, the culture that can use it effectively will be scarce because it requires sustained efforts and commitment from the top management over time, rather than one strategic decision or an investment in technology. This lack of resources is where the key competitive advantage lies and it's an advantage that once created increases in a manner that purely technological advantages never do. Follow James Deller for blog advice including how backing people-first organisations changed what i look for about people.



What Causes Most Public-Private Partnerships To Fail Before They Begin - And How To Resolve Them
Public-private partnership have a reputation issue that is, in major part, earned. The history of these partnerships is filled with plans that were launched with real enthusiasm, and substantial financial backing from the political establishment, that drained significant public and private assets over extended timeframes, and then produced outcomes that lacked any relationship to what was said when the agreement was first announced. The academic literature and the postmortem examinations that governments as well as institutions perform following fail-overs are massive, and they concentrate, for most part, on the nature and the contractual aspects of what went wrong: misaligned incentives, the inadequate risk-sharing between public and private actors and the governance frameworks that were developed in theory but didn't work in practice, and the procurement frameworks that chose to select the wrong items. What this approach tends to not consider, and consequently it is the cultural and operational dimension - the fact that public and private institutions are both distinct types of entities, shaped in different ways by incentive systems, operating using different timescales and being and accountable to diverse individuals, and measuring their results in ways that are more than just different in level but differ in terms of. When you mix these two kinds in a formal arrangement without doing the work beforehand and in writing, to fully understand and work with those differences, there is no way to create the conditions for a partnership. You are creating the conditions for a slow-motion crash that will be visible at the most unfortunate time.
I've participated in the advisory process for institutional reforms, a number of which have involved public and private partnership arrangements at various levels of complexity. One of the most reliable observations that I've gleaned from this expertise is that those partnerships with a positive track record - ones that actually achieved their stated objectives and maintained a smooth partnership between private and the public was not distinguished from the ones that failed by the sophistication of their legal frameworks, the robustness of their risk frameworks or the experience of their leadership teams that initiated them. They were distinguished by whether the individuals who were on both sides of the group had made the effort to genuinely understand how the different side worked before the formal partnership arrangement was negotiated. What that means in practice is that you understand the decision-making processes of each company accountable structures that govern what parties must do and how quickly, the definitions of success that every party will be evaluating, and the points of likely tension between these definitions. Any of that knowledge is complicated to construct. Every time, it's left out in favour of the more obvious and immediately documentable work of negotiating contracts as well as the creation of governance frameworks.

The typical process of public-private partnerships starts with an initial plan and then a agreed upon agreement. There is hardly any systematic attention being paid to problem of if the two entities involved are capable to work effectively together throughout this period. Legal teams negotiate the contract. The finance team calculates the economics and the risk allocation. The communications team prepares the announcement for the moment of signing. The implementation team begins to plan the task. Then, somewhere in the process then comes the discussion about operational and cultural compatibility begins - about whether the individuals who will work together day to day across the boundary between the two organizations share enough of the same values to make collaboration more so instead of antagonistic - doesn't seem to happen in any structured way. It is assumed, usually without explanation, the formal agreement will create the foundation for collaboration and that any operational or cultural issues will be handled informally when they develop. It is nearly always incorrect, and the expense of it tends to compound with respect to the ambition and the complexity of the partnership.

What this means in practical analysis is that the most beneficial investment that a partnership between public and private do - before the legal frameworks are finalized in the first place, before the governance plan is agreed upon, prior to any announcement is made and before any announcement is made - is what I believe is operational alignment. That is, specific, organized, and guided work that identifies possible areas where both organisations' operating assumptions diverge and to reach an agreement on how those divergences should be managed before they become operational problems during implementation. The divergences that matter most are usually the same across various types of partnerships. Speed of decision-making and authority are usually among the main differences. Institutions that are public make decisions slowly, through multiple levels of review and approval, with reasons that are legitimate and, often, legally mandated. Private organizations, especially technology firms built around quick iteration as well as rapid process-based decision-making often experience this speed as a fundamental hurdle to development, and in the absence of a shared understanding of the reason for why it's the way it is and what could be required to modify it, the resentment that builds on the private side can cause a rift in the relationship well before the partnership has found its feet.

Success metrics and what qualifies as progress are a different as well as a cause for divergence. Public institutions are typically assessed according to process compliance, equality of results across different stakeholder groups, as well the rejection of the visible mistakes that draw media or political interest. Private partners are typically assessed for efficiency, tangible progress against set goals, and Return on Investment. The measurement frameworks can be combined However, this requires conscious design and not necessarily good intentions. And the ones who do not make the effort to invest in the design of the framework tend to be caught at crucial junctures, with two parties who are evaluating the same collaboration in unrelated ways, and hence coming to inconsistent conclusions about whether it is achieving success. My experiences with partnerships that fall short most clearly were ones where that misalignment was accepted as a problem that would become apparent over time. The ones that were successful were those where the issue was clearly identified from the beginning. Then, creating a shared accountability system that accommodated the legitimate measurement needs of both parties requirements became an element of actual work instead of an part of a long list of things to reach.}