AI transformation is the next phase of digital transformation. Businesses are willing to invest in AI technologies to stay ahead of competitors.
Digital transformation is required before companies can start their AI transformation because digital data is necessary for AI training and digital processes are necessary to roll-out AI solutions in most cases. Feel free to read about digital transformation frameworks and our digital sustainability solutions if you believe that your company has not yet progressed on its digital transformation journey.
What is AI Transformation?
AI transformation is the next step after digital transformation. After a company adopts digital processes, the next step is to improve its intelligence. This would increase the level of automation and effectiveness of those processes.
AI transformation touches all aspects of the modern enterprise, including both commercial and operational activities. Tech giants are integrating AI into their processes and products. For example, Google is calling itself an “AI-first” organization. Besides tech giants, IDC estimates that at least 90% of new organizations will insert AI technology into their processes and products.
What are the steps to AI transformation?
We have listed below a set of the top 6 steps for Fortune 500 firms. Smaller firms could skip having in-house teams and strive for less risky and less investment heavy approaches such as relying on consultants for targeted projects.
1. Defining a clear vision and strategic roadmap for AI adoption
A successful AI transformation begins with identifying and prioritizing the use cases where generative AI (GenAI) and large language models (LLMs) can impact business outcomes most. Organizations should start by assessing which operational workflows are most ripe for automation and where human expertise can be effectively amplified through AI. This could include automating repetitive tasks, streamlining data analysis, or synthesizing insights from vast, unstructured data sets. The key is to align these use cases with overall strategic objectives so that every AI initiative drives tangible results and contributes to a higher return on investment.
Case study: JPMorgan Chase’s DocLLM is an example of leveraging GenAI to transform the way contracts are analyzed. By automating the review process, the bank has reportedly reduced manual review time by up to 85% and significantly minimized errors. Such high-impact initiatives free up critical resources and allow experts to focus on strategic decisions rather than getting bogged down in routine tasks. 1
2. Build a hybrid AI expertise network
Organizations looking to drive AI transformation in 2025 must ensure they have access to cutting-edge technical talent and domain-specific knowledge. Building a hybrid AI network means combining the expertise of external AI labs and vendors, such as OpenAI, with the upskilling of internal teams. This combination is essential because it infuses the organization with state-of-the-art AI capabilities and fosters a deep understanding of how these technologies can be tailored to unique business challenges.
Case study: Airbus invested in training approximately 10,000 engineers in tools like GitHub Copilot. This effort accelerated their aircraft design simulations by an impressive 40%, demonstrating that internal upskilling and external partnerships can yield significant efficiency gains. 2
Companies can create a culture of continuous learning and innovation by investing in comprehensive training programs tailored to various roles, from executives to junior engineers.
Also, implementing process mining is one of those easy-to-achieve and impactful projects. With a process mining tool, your business can identify existing inefficiencies and automate or improve those processes to achieve savings or customer experience improvement. Thus, some process mining tools generate a digital twin of an organization (DTO), which provides an end-to-end overview of the company’s processes and offers simulation capabilities to compare actual and hypothetical scenarios.
3. Deploy agentic AI for end-to-end automation
The concept of agentic AI revolves around deploying autonomous systems that can handle multi-step workflows without constant human intervention. By integrating AI agents into business processes, companies can automate complex chains of decision-making and execution. This strategy optimizes operational efficiency and enables employees to redirect their focus to higher-level tasks that require creative and strategic insight.
Case study: Unilever’s deployment of an AI procurement agent illustrates how autonomous systems can revolutionize supply chain management. The AI agent can negotiate with suppliers, leading to annual savings of up to $250 million. This case study underscores the immense potential of AI agents to streamline operations and optimize cost efficiencies across various functions. 3
4. Embed responsible AI safeguards
With AI’s increasing integration into every facet of business operations, ensuring ethical use and preventing bias have never been more important. Embedding responsible AI means establishing robust oversight frameworks that monitor AI outputs for accuracy, fairness, and regulatory compliance. This proactive approach is vital to maintaining public trust and ensuring that AI systems operate transparently and ethically.
A case study in responsible AI implementation is CVS Health’s use of AWS’s Guardrails for Amazon Bedrock. By integrating critical models and auditing mechanisms, CVS Health ensures that its pharmacy chatbots consistently adhere to strict FDA guidelines while mitigating the risks of biased outcomes. Such practices are critical in healthcare and other sensitive industries where the stakes are high and any deviation can have serious repercussions. 4
5. Master data-centric AI
The success of AI initiatives is rooted in the quality and management of data. A master data-centric strategy involves investing in superior data lifecycle management practices to ensure that AI models are trained on high-quality, relevant, and well-curated datasets. Without such a foundation, even the most advanced AI systems can underperform and produce unreliable outputs.
Case study: Mayo Clinic’s Medical-GPT is an exemplary case study in data-centric AI. By training on anonymized patient interactions and domain-specific data, the Medical-GPT system has outperformed general models, delivering more accurate and contextually relevant insights in the medical field. This success underscores the importance of mastering data curation and management to harness the full potential of AI. 5
6. AI-driven innovation
Innovation in AI is not a one-time effort but a continuous process that benefits from iterative testing and rapid prototyping. AI-driven innovation sprints offer a strategic approach to quickly test and validate new ideas before scaling them across the organization. These sprints enable companies to experiment with GenAI applications in areas such as marketing content generation, predictive maintenance, and customer service enhancements.
Case study: L’Oréal provides a compelling example of this strategy. By conducting targeted AI innovation sprints, L’Oréal could reduce product development cycles from 18 months to just 4 weeks using tools like ChatGPT-4 for trend analysis and product ideation. This approach accelerates the innovation process and drives faster time-to-market for new products and services. 6
7. Scale with modular AI
A modular AI architecture allows organizations to integrate multiple AI models—ranging from OpenAI’s suite of tools to open-source solutions—into a scalable system. This ensures that businesses are not dependent on a single vendor and are well-positioned to incorporate new advancements as they become available.
Case study: Samsung’s Gauss LLM is an example of a modular architecture in action. By integrating a variety of AI models, Samsung has managed to optimize tasks from code generation to customer support. This integrated approach not only enhances the overall performance of the system but also ensures that the organization can swiftly pivot to new models or technologies without significant rework. 7
What are the obstacles to AI transformation?
The top obstacles facing AI transformation are:
- Insufficient AI talent and experience in the organization.
- Data quality issues and a lack of sufficient data.
- Difficulties in identifying applicable business use cases.
- Company culture does not recognize the value of AI.
| Challenge | Solution | Case Study |
|---|---|---|
| Talent Shortages | Partner with platforms like Coursera for GenAI certifications; hire “AI translators” to bridge tech-business gaps. | ANZ Bank upskilled 70% of its workforce on Copilot tools in 6 months. 8 |
| Data Quality Issues | Use synthetic data to fill gaps; deploy tools like IBM’s Watsonx for data harmonization. | Urban Company improved chatbot accuracy to 85% by refining training data. 9 |
| Shadow AI Risks | Centralize LLM access via platforms like AWS Bedrock; monitor usage with tools like Cyberhaven. | Barnsley Council reduced data leaks by 40% with integrated governance. |
| Cultural Resistance | Link AI adoption to employee empowerment. | Asahi Europe saved 15% time on admin tasks. 10 |
What are the best practices?
Based on our review of existing research and interviews:
- Define clear objectives: Identify specific business challenges that AI can solve and ensure these initiatives align with your strategic goals.
- Build a robust integration framework: Set clear guidelines for data governance, model training, IT integration, performance monitoring, and regulatory compliance.
- Start with pilot projects: Launch small-scale pilots to evaluate AI effectiveness, gather insights, and minimize risks before scaling.
- Implement continuous iteration: Regularly assess AI performance, collect user feedback, and refine models to adapt to evolving business needs.
- Partner with experts & develop internal skills: Collaborate with experienced LLM vendors while investing in upskilling your team to ensure sustainable transformation.
- Prioritize security & ethical practices: Address biases, ensure transparency, and enforce strong data privacy measures throughout the AI lifecycle.
- Foster cross-functional collaboration: Encourage communication and teamwork across departments to align AI initiatives with broader business strategies.
- Focus on user experience: Design intuitive tools that easily integrate with existing workflows and actively promote user adoption.
- Adopt a future-proof strategy: Build flexible architectures that allow for continuous learning, adaptation to new technologies, and independence from single vendors.
For more on AI
Feel free to check our other AI articles to learn more about how AI can transform your business:
- AI in Automation: Which tasks can we automate?
- Top AI Use Cases / Applications
- State of AI technology: Comprehensive Guide
If you have a data source that can be used to build a machine learning model and improve your business’s performance, don’t hesitate to contact us:
If you want to learn more about custom AI solutions, feel free to read our whitepaper on the topic:
We can also help if you have questions about how AI transformation can impact your business and how you can get started:
External Links
- 1. Copilot included and unlimited usage | Intercom Help.
- 2. Copilot included and unlimited usage | Intercom Help.
- 3. Copilot included and unlimited usage | Intercom Help.
- 4. Copilot included and unlimited usage | Intercom Help.
- 5. Copilot included and unlimited usage | Intercom Help.
- 6. Copilot included and unlimited usage | Intercom Help.
- 7. Copilot included and unlimited usage | Intercom Help.
- 8. https://www.foreveryscale.com/p/anz-bank-github-copilot-breakthrough
- 9. https://blogs.microsoft.com/blog/2025/03/10/https-blogs-microsoft-com-blog-2024-11-12-how-real-world-businesses-are-transforming-with-ai/
- 10. https://www.microsoft.com/en/customers/story/1794442287816192713-asahi-europe-and-international-microsoft-copilot-for-microsoft-365-consumer-goods-en-czechia
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