{"slug": "discipline-the-secret-ingredient-of-successful-ai-projects", "title": "Discipline – the secret ingredient of successful AI projects", "summary": "Colibri Digital, a strategic adviser and implementation partner, warns that FOMO-driven, performative AI deployments rarely deliver return on investment, while disciplined, foundation-first strategies do. The company advocates for three-to-five-year roadmaps translated into discrete use cases, emphasizing that robust data platforms are essential for agility in the fast-paced AI market. Simon Bateman, CEO at Recognise Bank, notes that boards must accept failures and pivots as inevitable for long-term transformation success.", "body_md": "# Discipline – the secret ingredient of successful AI projects\n\nTHE ARTICLES ON THESE PAGES ARE PRODUCED BY BUSINESS REPORTER, WHICH TAKES SOLE RESPONSIBILITY FOR THE CONTENTS\n\n- Bookmark\n\n*Colibri Digital is a Business Reporter client*\n\n**Why performative deployments rarely bring RoI, while the ones with a strategy and a foundation-first approach often do.**\n\nAlthough the ticking-the-box approach is often mentioned in relation to compliance, it’s an attitude that extends far beyond the perfunctory meeting of rules and requirements.\n\nDriven by the FOMO effect surrounding generative AI and amplified by a range of competing AI assistants and the emergence of agentic AI, a multitude of businesses are rushing to make sporadic, performative AI deployments to try and stay in the game.\n\nThe investments perceived as low-hanging fruit are typically widgets and chatbots – AI applications that rarely deliver meaningful RoI in isolation and without a coherent transformation strategy behind them.\n\nFOMO-driven hasty investments in shiny new AI toys often lead to stop-start technology programmes – recurring cycles of excitement followed by stalled projects and, ultimately, disillusionment with the technology itself.\n\nAlthough some companies have the vision and discipline to carry out entire AI roadmaps without losing direction or getting discouraged by delayed RoI, most of them require some guidance to take these projects to fruition.\n\nAs both a strategic adviser and an implementation partner, [Colibri Digital](https://www.colibridigital.io/) helps businesses navigate the planning and proof-of-concept process.\n\nOnce a three-to-five-year roadmap has been developed with the client, the strategy gets translated into a dozen discrete use cases with tangible impacts. These are then prioritised, with the top two or three implemented sequentially. Their outcomes are used to demonstrate value to the board and secure ongoing support.\n\nBut the partnership doesn’t end here. Colibri also ensures that, by the end of the pilot phase, clients have developed the capabilities required to scale successful use cases across multiple functions in parallel. As Cross explained, Colibri’s ultimate goal is to “get its clients to the point where they feel confident enough to embrace change and carry on with the transformation on their own.”\n\n**The imperative of a strong foundation**\n\nDiscussions about why a unified enterprise data base is key to digitalisation started decades ago. However, for many companies, data silos remain a persistent reality. But while patchy data creates a setback for other digital initiatives, an AI project without a repository of robust, resilient and trustworthy data is a recipe for disaster.\n\nGiven the breakneck speed of AI innovation, creating an AI-enabled enterprise is rarely straightforward. Making the right procurement decisions amid an abundance of technologies can be challenging enough, but difficulties are further compounded by the fact that cutting-edge tools can become outdated within months.\n\nOnly companies with a robust data platform foundation can demonstrate the agility required for navigating the fast-paced AI market by pivoting to new tools and switching to alternative tactics. As Simon Bateman, CEO at [Recognise Bank](https://bit.ly/4bFaFe1), points out, boards must also understand that failures and pivots in technology decision-making are an inevitable part of achieving long-term transformation success.\n\nBut data silos are not the only factor that can undermine the RoI of AI projects. Technology and finance leaders often underestimate the significant operational and maintenance costs associated with AI systems.\n\nParadoxically, companies often boast about the level of their AI-enablement in what has become a token race, as though higher token consumption without verified outcomes was a meaningful measure of effectiveness or productivity gains.\n\nColibri’s approach, by contrast, has a focus on outcomes and delivery. As Cross explains, Colibri’s ultimate goal is to “give clients the confidence that they will get to the outcome they desire when they first engaged with Colibri.”\n\nWith more than a decade of experience in planning and delivering data, AI and cloud projects, Colibri has already seen a number of its clients reap the benefits of the partnership with the company through improved productivity, increased revenue or boosted share value.\n\nRecognise Bank, a returning customer, has recently engaged Colibri with building a central data repository with data lineage capabilities, which can serve as a solid foundation for future data analytics and AI projects.\n\nHowever, Colibri’s support extended beyond technology deployment. The company also provided guidance on the governance and control frameworks required to operate a unified data platform effectively.\n\nAs the platform is getting increasingly embedded in the bank’s day-to-day operations, its added value across the whole company is becoming apparent.\n\nFirst and foremost, the project has achieved its primary objective of speeding up the loan assessment process. But efficiencies are being realised across other functions too.\n\nAs teams continue to populate the platform with data, they are experiencing growing productivity gains. Meanwhile, self-service analytics capabilities are empowering employees with direct access to data, reducing reliance on IT and business intelligence teams while increasing confidence in decision-making.\n\nThrough its transition journey, Recognise has clearly understood why sporadic, performative AI investments in chatbots and widgets rarely succeed and how a multi-phase transformation project aligned with business strategy can create long-term value.\n\nThe bank has now reached a stage where the data platform developed with Colibri enables it to make informed decisions about which emerging opportunities to pursue and how they support its broader strategic objectives.\n\nColibri has guided Recognise through strategy development, data foundation building and the creation of a robust decision-making framework. Now, Recognise is in a position to apply the methodology independently, identifying new use cases and scaling technology deployments with confidence.", "url": "https://wpnews.pro/news/discipline-the-secret-ingredient-of-successful-ai-projects", "canonical_source": "https://www.independent.co.uk/news/business/business-reporter/ai-projects-data-digitalisation-compliance-b3022465.html", "published_at": "2026-07-29 07:00:00+00:00", "updated_at": "2026-07-29 07:04:34.076908+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-infrastructure", "ai-startups"], "entities": ["Colibri Digital", "Recognise Bank", "Simon Bateman"], "alternates": {"html": "https://wpnews.pro/news/discipline-the-secret-ingredient-of-successful-ai-projects", "markdown": "https://wpnews.pro/news/discipline-the-secret-ingredient-of-successful-ai-projects.md", "text": "https://wpnews.pro/news/discipline-the-secret-ingredient-of-successful-ai-projects.txt", "jsonld": "https://wpnews.pro/news/discipline-the-secret-ingredient-of-successful-ai-projects.jsonld"}}