In the words of tech entrepreneur Matt Mullenweg: \u201cTechnology is best when it brings people together.\u201d As AI reshapes the business landscape, the challenge is not just building smarter systems, but using them to serve people better.<\/p>\n\n\n\n
Despite all the buzz, most organizations are still struggling to extract real, sustainable value from artificial intelligence. Every boardroom is asking the same question: Will AI revolutionize our business, or simply distract it? As the noise grows louder, clarity has never been more important. <\/p>\n\n\n\n
According to McKinsey<\/a> and Deloitte<\/a data-aos=\"fade-up\">, many companies are hitting roadblocks when trying to scale and realize the benefits of AI. Deloitte\u2019s study describes a complex landscape where organizations are beginning to move beyond early-stage experimentation toward more strategic, enterprise-level adoption of generative AI. However, regulatory compliance has emerged as a major barrier to progress, rising from 28% to 38% in just a few months. Furthermore, 69% of organizations expect it will take more than a year to implement a comprehensive AI governance framework. <\/p>\n\n\n\n McKinsey\u2019s research, published in January 2025 and based on interviews with global leaders and employees, indicates that almost all companies invest in AI, but just 1% believe these investments are at maturity. The report shows that the biggest barrier to scaling is not employees, who are ready, but leaders, who aren\u2019t piloting progress fast enough. <\/p>\n\n\n\n The study also found that measurable value in profit and loss from AI initiatives is promising, but continues to lag. <\/p>\n\n\n\n So, is AI just the latest pipe dream sold by tech evangelists, or is there genuine value waiting to be unlocked? If so, how can companies get there? In short, how can organizations achieve faster and better RoAI <\/em data-aos=\"fade-up\">\u2013 Return on Artificial (intelligence) Investment? <\/p>\n\n\n\n Jim Rowan, Applied AI Leader at Deloitte, notes: “Amid the promise of AI agents and the evolution of foundational models, future-thinking organizations are as bullish as ever in building bridges to ROI, all while understanding the need for nuance \u2013 and patience \u2013 as we embrace this next wave of GenAI. Anticipation is high, and now is the time for leaders to take the long view of their GenAI investments, with a focus on governance, collaboration, and continued iteration as key accelerators in the race for sustainable value.” <\/p>\n\n\n\n For all the excitement around AI, few organizations are seeing consistent success. Michael de Kare-Silver<\/a><\/strong>, Managing Partner at Signium UK<\/a data-aos=\"fade-up\">, comments, \u201cThere doesn\u2019t seem to be a single company that has discovered the silver bullet for achieving significant ROI out of AI. There\u2019s a lot of investment, a flurry of activity and learning, and plenty of use cases being explored. Yet still, very few efforts are genuinely resulting in measurable returns.\u201d\u00a0\u00a0<\/p>\n\n\n\n The collective research points to a handful of recurring obstacles that continue to get in the way. <\/p>\n\n\n\n 1. Multiple random tests and pilots <\/p>\n\n\n\n Most companies have been allowing their staff to experiment with AI. In many cases, this means using tools like CoPilot to summarize a Teams or Zoom meeting and send out notes, or to generate a first draft of a document. However, these efforts are scattered \u2013 each team, department, or region is trialling different tools and use cases. It\u2019s random, opportunistic, and individual, with vastly different levels of adoption and effectiveness. <\/p>\n\n\n\n 2. No unified strategy or approach <\/p>\n\n\n\n In this \u201crandom\u201d environment, there\u2019s a clear lack of unified thinking. There\u2019s no overarching plan, no guiding view on who should use AI and in what context, and no rules about when it\u2019s appropriate or when it\u2019s not. As a result, the opportunity to incorporate tried-and-tested AI tools into routine processes across an organization is lost. <\/p>\n\n\n\n 3. Fear of the unknown <\/p>\n\n\n\n AI is still new. It\u2019s uncertain. It\u2019s not yet proven that it can drive sustainable revenue or profit, so the prevailing attitude in many companies is: let others go first. <\/p>\n\n\n\n \u201cNo leader wants to risk throwing money in the water,\u201d says de Kare-Silver. \u201cThey prefer calculated risks, but in the world of AI, where so much is unknown, calculations are estimates, at best. Leaders want to see proven use cases, and only then can they be persuaded to try it themselves.\u201d <\/p>\n\n\n\n 4. Poor data quality <\/p>\n\n\n\n To really take advantage of an AI application, it needs to be able to leverage the company\u2019s data. That is the bedrock of AI efficiency, after all. However, in many companies, data quality is poor. There\u2019s often no uniform, up-to-date, hygienic database, and without clean data as a foundation, it becomes impossible to get sensible insights from AI analysis. This could lead to incorrect outputs, misinformation, and AI hallucinations. <\/p>\n\n\n\n 5. Absence of experienced AI leadership <\/p>\n\n\n\n In most companies, responsibility for AI has simply been added to the job portfolio of an already-busy CTO or Head of Data. De Kare-Silver advises against this, saying, \u201cA company\u2019s CTO or Head of Data is likely already at their limit. They\u2019re not going to be able to find the time to explore what the true advantages of AI could be. Testing AI and aligning its application with an organization\u2019s goals, as well as industry regulations, is meticulous and time-consuming work. For a busy CTO, that\u2019s simply not a priority.\u201d <\/p>\n\n\n\n De Kare-Silver applauds organizations like BT, Schneider Electric, and ING, that have appointed senior, dedicated AI champions: \u201cConsidering the potential impact of AI \u2013 both good and bad \u2013 having an executive role dedicated to driving it is surely what\u2019s needed to have any real shot at delivering RoAI.\u201d <\/p>\n\n\n\n At the heart of the AI conversation lies a critical issue: how to move down this path without fundamentally undermining our people. If AI is to deliver the much-vaunted value it promises, then a significant part of that will presumably come through automation, process change, and the replacement of people with bots. <\/p>\n\n\n\n As we begin to explore that path, what will the impact be on our people? What are the consequences of fuelling further fear in the workforce, as the spectre of job cuts and reduced staffing looms larger? Do we even want to replace people with technology? After all, aren\u2019t our employees the very core of what gives a business meaning? <\/p>\n\n\n\n Richard Branson\u2019s well-known mantra comes to mind: <\/p>\n\n\n\n Happy employees = happy customers = market success\u00a0<\/strong data-aos=\"fade-up\"><\/p>\n\n\n\n De Kare-Silver urges businesses to approach the human cost thoughtfully: \u201cBranson\u2019s business equation has proven itself, time and again. Do we really want to replace \u2018happy employees\u2019 with \u2018efficient bots\u2019? When AI and bots become the face of a business, how do we preserve the human connection consumers are looking for?\u201d <\/p>\n\n\n\n Many leaders are becoming acutely aware of this dilemma, and it\u2019s fast becoming one of the defining challenges beneath the AI hype. More organizations are looking for real evidence that replacing people with AI tools will truly deliver tangible, measurable business benefits. <\/p>\n\n\n\n In Deloitte\u2019s study, only 16% of executives said they had been able to present a clear report to their CFO demonstrating value creation. Most admitted they struggled to define and measure the real impact of their AI efforts. <\/p>\n\n\n\n For many executives, the question now is how to turn early exploration into real impact. While the path forward isn\u2019t without obstacles, emerging research and early success stories suggest there are six critical steps that can help organizations shift from scattered pilots to scalable, strategic outcomes: <\/p>\n\n\n\n 1. Appoint a dedicated AI Champion <\/p>\n\n\n\n\n
Five key challenges blocking AI progress <\/h3>\n\n\n\n
The human cost: What’s the real impact on people? <\/h3>\n\n\n\n
Six steps to unlocking RoAI <\/h3>\n\n\n\n