The rise of AI-native companies underscores the opportunity – and the urgency for action. AI has lowered barriers to entry, allowing agile AI-first scale-ups and tech-savvy companies to disrupt established industries with innovative business models and frictionless customer experience. Technology companies are moving into multiple domains, from financial services to automative, healthcare, food delivery, and more.
Combined with ongoing disruption, we’re seeing the lines of traditional industries and sectors blur. Our Value in Motion report recently revealed the extent to which AI, climate change and geopolitical shifts are reconfiguring the global economy. It highlighted just how significantly sectors and industries will converge over the next decade, as well as where the value will move. These ‘domains of growth’ will shape opportunities that require a bold response, as businesses look beyond linear growth, to find opportunities outside their traditional core offerings. But it also means organisations must recognise their biggest competitors may no longer be their traditional rivals.
Established businesses will need to look towards greater innovation and a proactive mindset. It is time for reinvention rather than adaption. Get it right and the rewards are significant. Get it wrong and risk irrelevance.
It’s a message echoed in the Gartner® article Over 100 Data, Analytics and AI Predictions Through 2030 - an annual look at “the continued impact and influence of data, analytics and increasingly AI across an ever-broadening range of industries and initiatives.”
A dual-track imperative: balancing business as usual with AI-driven transformation
Although mature businesses in certain sectors have the benefits of existing client bases, specific IP and proprietary domain knowledge, they will have to navigate a more complex path to reinventing business models than tech-first entrants. Reinvention will hinge on a ‘dual-track approach’: optimising existing operations with AI while exploring transformative new business models.
A manufacturer might use AI to optimise its supply chain (business as usual) while also exploring AI-powered product customisation (transformation). A retailer might use AI for personalised recommendations (business as usual) while building a virtual shopping platform (transformation). This balanced approach is crucial. It allows businesses to deliver immediate value while simultaneously building the target operating models needed to be market leaders in the mid to long term.
According to Gartner®, “planning for possible alternative future scenarios is a vital aspect of modern leadership and required operating practices given the hopes pinned on AI and the conflicted economic situation we face.”
To succeed, organisations must consider three key lenses: creating an AI-native workforce (embedding AI fluency into daily operations), optimising back-office functions with AI, and, most crucially, driving business model innovation. This third lens distinguishes those who merely adapt from those who will shape the future.
A collaborative approach to disruption
At PwC, we're already helping businesses seize these opportunities. We've partnered with a UK health club on an AI-driven transformation to enhance member experiences and operational efficiency. We’re helping organisations in the financial services sector redesign entire customer services and sales workflows with AI agentic frameworks. We’re helping organisations across sectors redesign customer onboarding. We’ve helped banks rigorously test GenAI tools, building trust and boost productivity. And we’ve streamlined internal audit processes with GenAI, freeing up teams for more strategic, value-add work.
Transformation requires assessing risk, capital, stakeholder alignment, and business models. It demands choosing the right transformation model – building an internal AI innovation hub or forging strategic partnerships. It requires cross-functional AI task forces, investment in ‘AI fluency,’ and a workforce equipped with technical skills and critical thinking.
We believe upskilling our people is a crucial element of future AI success. That’s why we’re investing US$1.5 billion globally into scaling our capabilities, upskilling our people and rolling out AI securely across our network, adding new teams, skills, tools and training. However, we’re also aware that for others, the AI learning and employment landscape can be fragmented and costly. Working with Innovate UK, we’ve helped launch AI Skills Hub - a learning platform to drive UK-wide AI upskilling, allowing individuals, employers, training providers and AI tech partners to work together to advance AI adoption, job creation and growth.
Managing the cost of AI adoption
But AI transformation has a cost. As revealed in our last article, Gartner predicts that by 2028, over 50% of enterprises building their own generative AI models will abandon efforts because of cost overruns and technical debt. We believe this highlights the need for a strategic, measured approach. Leaders must champion a paced approach, fostering organisation-wide understanding of AI's complexities and robust governance frameworks.
This journey is about shifting mindsets. It's about viewing AI not just as a disruptor, but as a strategic ally. Transformative potential requires a top to bottom cultural change to ensure that the existing organisational structures don’t become a blocker to the change required to move to new operating models. Many organisations will need to think about R&D, experimentation and innovation in fundamentally different ways if they are to thrive in the future.
We are also likely to see ROI metrics having to evolve. While cost savings matter, 'soft’ metrics will be equally as importantly – enhanced employee engagement, stronger brand loyalty, accelerated innovation cycles. Employee satisfaction surveys can track engagement. Brand sentiment analysis can gauge loyalty. Innovation can be measured by the number of new products or services launched.
Elsewhere, this new world of work will demand rethinking organisational structures. Hierarchical models will need to give way to fluid, network-based organisations, more like agile squads than functions and siloed teams. It will also require significantly more human-AI collaboration, creating new roles focused on AI oversight, data specialists and governance roles, particularly as we start to see the greater roll out of agentic AI.
This needs to be a long-term journey for all organisations. We recognise this ourselves and continue to invest in our own capabilities. Our newly launched Tech Catalyst capability brings together human centric design and technical capabilities in AI, cloud, data and engineering. With end-to-end support from prototyping to deployment, and a focus on emerging technologies, it empowers clients to solve complex problems and unlock value through tailored, scalable AI solutions.
This is how we all unlock the true potential of AI together – to drive unprecedented growth while also protecting business as usual.
Gartner, Over 100 Data, Analytics and AI Predictions Through 2030, By Sarah James, Alan D. Duncan, 2 May 2025.
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