Develop a strategy for AI transformation that delivers tangible results
Most businesses are now investing in generative AI, but only those that move beyond static tools and embed AI into workflows will benefit from its full value.
The last few years have brought a surge of interest in artificial intelligence. A host of AI-enabled tools and applications are now available to enterprises, many of them driven by generative AI (genAI) technology.
Four cornerstones for a winning AI PC strategy
While businesses grasp the potential of generative AI (genAI) to spark a wave of game- changing innovation across departments, many are still not seeing the anticipated outcomes.
The road to AI success requires a holistic strategy that identifies the right technology, focuses on workflow integration, understands how to optimise data and recognises security challenges.
The race is on for businesses to get out ahead with AI deployments and use the technology to outperform competitors. According to McKinsey, 92% of companies plan to increase their AI investments over the next three years.1
Yet achieving success would appear to be about more than just spend. A recent study from MIT found that although enterprises have invested around $30-40 billion in genAI, 95% of organisations are getting zero return on those investments.2 McKinsey’s study, meanwhile, found that only 1% of leaders believe that their company is “mature” on the deployment spectrum, meaning that AI is fully integrated into workflows and drives substantial business outcomes.3
Broadly speaking, these failures can be attributed to one or more issues:
Workflow challenges. Businesses struggle to integrate AI seamlessly into existing workflows, resulting in fragmented processes that limit AI's ability to deliver value. This disconnect often creates inefficiencies, duplication of effort and a lack of end-to-end automation. Without proper orchestration, AI can become siloed, which limits its impact.
Lack of contextual learning. AI systems often lack the real-time contextual awareness needed to make decisions, which undermines their effectiveness and trustworthiness. Often, models are trained on static data and are therefore unable to adapt to dynamic business environments. This limits their ability to provide relevant insights, especially in scenarios requiring judgment or human-like understanding.
Misalignment with day-to-day operations. When AI initiatives are developed in isolation from the needs of users they will not gain a foothold into daily routines, which results in them being underused or even resisted by employees. Successful adoption depends on tight alignment with existing roles, tasks and pain points. Without user-centric design, AI can feel it is being imposed from above, stalling momentum.
To realise the full value of AI investments, enterprises should take a structured approach while working closely with trusted cloud partners throughout their transformation programme. There are five key steps that every business should take:
Move beyond static tools. By moving away from static, prompt-driven apps and connecting to foundational-model APIs through services like Amazon Bedrock, businesses can ensure that their AI models are fuelled by their own data and customised to their specific industry and workflows. This shift provides for dynamic, context-aware solutions that continuously evolve with business needs, enhancing responsiveness, reducing manual interventions and driving real-time innovation.
Prioritise workflow integration. By orchestrating genAI workflows using tools like Amazon Bedrock and AWS Step Functions, businesses can integrate their AI instances into their existing business processes right across the organisation. As well as breaking down siloes, orchestration enables automated decision-making, improved process efficiency and end-to-end visibility, key factors for achieving operational excellence.
Enrich models with contextual intelligence. As with any application, genAI is only as good as the data that support it. Businesses should invest in fine-tuning their models with domain-specific data. Where such data is lacking, businesses should consider synthetic data, using a larger language model to fine-tune a smaller model. This has the added benefit of a faster turnaround time. Combined with contextual signals, such as user behaviour or operational status, this approach can drastically enhance accuracy, relevance and the quality of decisions.
Scale securely and responsibly. Choose platforms that provide enterprise-grade security, compliance and monitoring. Responsible AI controls should be included from the outset to protect customer trust while scaling deployments. Cloud providers like AWS can help, providing a set of tools that enable organisations to design, build and operate AI systems responsibly. This includes data governance, auditability, access controls and bias detection. These tools ensure that ethics and accountability are prioritised every bit as much as innovation in AI deployments.
Empower employees. By upskilling employees and focusing on deploying intuitive tools and interfaces, organisations can democratise AI usage, helping to accelerate adoption and embed AI in day-to-day decision-making. Training programmes, low-code environments and embedded AI assistants can transform how teams interact with data. This drives productivity and fosters a culture of innovation and continuous improvement across the business. As Pat Brans says in CIO.com: “Technology may be the essential element, but culture is the catalyst. Successful AI programs are supported by organisational habits that promote experimentation, internal visibility, and cross-functional collaboration. A culture of curiosity and iteration is just as critical as a strong technology stack.”8
Businesses which realise the full value of genAI may achieve substantial gains and even transform their market position. However, they must do more than experiment with chatbots and static tools. For AI to deliver the wholesale transformation it is capable of, organisations should focus on building customised, workflow-integrated systems that learn from context and which are inextricably linked to day-to-day operations.
These systems must be adaptable and able to continuously improve as business needs evolve. By prioritising integration, data quality, security, governance and team empowerment, businesses can move beyond pilots to real, measurable transformation. The enterprises that embrace this approach will evolve into adaptive, resilient organisations ready to compete and grow in an increasingly AI-centric economy.
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Endnotes
1. McKinsey, Superagency in the workplace: Empowering people to unlock AI’s full potential, 2025, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work
2. MIT, The GenAI Divide State of AI in Business 2025, 2025, https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
3. McKinsey, Superagency in the workplace: Empowering people to unlock AI’s full potential, 2025, https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work
4. McKinsey, The state of AI: How organizations are rewiring to capture value, 2025, https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
5. MIT, The GenAI Divide State of AI in Business 2025, 2025, https://mlq.ai/media/quarterly_decks/v0.1_State_of_AI_in_Business_2025_Report.pdf
6. Qlik, “94% of Businesses Are Investing More in AI—Yet Only 21% Have Successfully Operationalized It,” 2025, https://www.qlik.com/us/news/company/press-room/press-releases/94-percent-of-businesses-are-investing-more-in-ai-yet-only-21-percent-have-successfully-operationalized-it
7. BCG, AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value, 2024, https://www.bcg.com/press/24october2024-ai-adoption-in-2024-74-of-companies-struggle-to-achieve-and-scale-value
8. CIO, 4 things that make an AI strategy work in the short and long term, July 2025, https://www.cio.com/article/4013209/4-things-that-make-an-ai-strategy-work-in-the-short-and-long-term.html
