A strategic approach as the key to AI transformation: How insurers can successfully shape change

AI transformation in insurance: Why strategic thinking is a game-changer

AI is being driven by major technology companies, investing billions of dollars into the new technologies, and AI consequently continues to evolve rapidly. After large language models (LLMs), the next evolutionary stage, Agentic AI, is already on the horizon. Companies have to rethink their approach and their business strategies, and ask the right questions to successfully integrate AI into their core value creation processes.

Currently, many insurers only invest in individual AI applications — from chatbots in customer service to AI-based automation of administrative tasks and fraud detection in claims management — but they often lack a clear strategic direction or a defined understanding of how the implementation supports their overall corporate strategy.

However, the real potential of AI, namely, to create substantial value and sustainable differentiation, will only be fully realised when AI is no longer seen as a collection of single use cases, but as an integrated part of the strategic orientation and management of the company, supported by a closely aligned AI investment and implementation roadmap.

Leading insurers integrate AI into their corporate strategy and pursue a clear, top-down defined AI strategy derived directly from the overall business strategy and backed by sponsorship at top management level.
Our industry and project experience shows that in many insurance companies, there is still a gap between “AI is being used” and “AI is part of our business strategy.” At present, no more than 25% of all insurers have a dedicated AI strategy derived from their business strategy and are focused on their journey toward becoming an AI-transformed enterprise.

How insurers can successfully shape this transformation

The journey towards an AI-transformed organisation typically does not follow a linear path, but rather develops as a J-shaped curve.

In the early phase, pilot projects deliver initial (often isolated) successes — as well as failures. This is followed by a consolidation phase that varies in length, during which it becomes essential to structure the growing complexity of different, loosely connected initiatives and establish a solid foundation for scaling and for the next level of AI maturity.

This phase is not only about strengthening the strategic connection between initiatives but also about building core enablers such as scalable data quality, compliance, and governance. Equally critical are increased investments in skills and cultural readiness — otherwise companies risk that, even though they have access to a technology, their people are not yet ready to leverage it, letting new opportunities pass them by.

This foundational phase often comes with friction losses, temporarily reduced productivity, and declining engagement among employees and management. Amid the jungle of initiatives, promises, and early disappointments, it is easy to underestimate that strategic foresight and structured execution are particularly crucial at this point.

To reach a higher level of AI maturity, organisations must be willing to tolerate productivity losses in the short-to-medium term and invest deliberately in AI foundations. Those who accept this “pain of change” as a necessary part of evolution will be well-prepared for broader AI adoption and will position themselves favorably on the exponentially rising value curve (see Figure 1 – Deloitte J-Curve in AI Transformations).

While the general shape of the J-curve cannot be changed, the length and intensity of each phase can certainly be influenced. Organisations that think strategically about AI early on — that is, those who develop a top-down view of AI’s contribution to strategic ambitions alongside initial bottom-up use cases — can shorten learning phases and accelerate their transition to value creation.

From business strategy to AI strategy

At a group level, insurers generally have clear strategic targets — such as growth in specific business lines or customer segments, improving customer centricity, or increasing efficiency and financial resilience in a volatile environment.

The time has come to stop viewing AI as an experiment or a CTO topic and instead embed it top-down into existing strategy programs — using it systematically to achieve strategic objectives: Where can AI help me reach my strategic ambitions faster, with more precision, or in entirely new ways?
Integrating AI into corporate strategy and deriving an AI strategy — similar to how a product or sales strategy is derived — requires openness and willingness from the CEO and leadership team to embrace the disruptive potential of AI-enabled value creation and close collaboration between business and IT. With a structured approach, however, this is by no means an unsolvable task.

As in classical strategy development, the process begins with identifying the central AI levers that support the achievement of strategic business goals. The next step is to turn these insights into concrete AI initiatives within an AI roadmap, enabling the practical application of these levers and their prioritisation through an enterprise-wide AI use case portfolio management. Through this process, AI gradually becomes an integral part of business development and management — not as a standalone topic, but as an indispensable tool that directly contributes to executing strategic ambitions and maximissing the value of AI for business success.

Within the derived AI strategy, data and technology capabilities (the tech foundation) play a central role alongside strategy, people, and processes. Scalable data platforms, consistent data governance, reusable AI components, and the introduction of data quality management form the backbone for developing high-value AI use cases efficiently and reliably — while also preparing the organisation for future technological advances. These investments in technology provide the foundation to enable the business to develop AI strategically and align it flexibly, at scale, and consistently with business goals.

Practical example — strategy and AI in claims management

Few areas illustrate the strategic power of AI for insurers as clearly as claims management. For many insurers, the claims process is one of the biggest cost drivers – while also being a key determinant of customer satisfaction. Insurers seeking to leverage AI strategically in claims should start by identifying, in line with their claims management strategy, the most effective levers for AI adoption. A common goal is the improvement of the combined ratio, something that can be achieved through targeted AI deployment. For example, AI-powered claims triage enables the fast and automated handling of simple claims, while immediately routing complex or sensitive cases to experienced claims handlers.

In addition, AI-driven prioritisation and dynamic assignment optimise processing order, workload, and depth, ensuring efficient resource allocation and improving claims ratios. AI-supported fraud detection and predictive models for claim amounts further enhance payment management and cash flow optimisation. At the same time, this deep integration of AI into claims handling will trigger broader transformation — in roles, skills, decision-making authority, governance, and risk management. Actively managing these changes in parallel is not a “nice-to-have,” but essential. Otherwise, two risks arise: First, past technology rollouts have shown that failing to address evolving skill and mindset requirements prevents the organisation from fully realising AI’s potential. Second, efficiency and growth gains will not materialise if structures are not adapted to the new division of work between humans and machines.

From fixed plans to dynamic steering — AI as an enabler of agile strategy

Looking ahead, the link between business strategy and AI will no longer be one-directional. While AI initiatives are derived from strategic goals, insights and feedback from AI-enhanced core processes will increasingly feed back into strategic management. Over time, this creates a more agile and data-driven approach to strategic planning and decision-making, guided by real-time data, simulations, and forecasts from core business processes — rather than historical metrics or fixed annual plans.

Conclusion — proactively combining strategic ambition, technological intelligence, and human enablement

True value and differentiation can be achieved when AI deployment is directly derived from corporate strategy and treated as a holistic transformation effort. At the same time, organisations must have a realistic view on how such transformations unfold: progress is not linear but also not random. The key is to actively manage individual phases — consciously tolerating temporary productivity declines and higher investment needs. Through clear strategic priorities, visible quick wins, and the tight integration of business and technology, organisations can accelerate their transition into the value creation phase of the J-curve and shorten the period of “tolerance”.

Where does your organisation stand in this transformation?

Answer the following questions for a quick check:

• How closely are your business and AI strategies linked today?
• To what extent is your current AI project portfolio aligned with your overarching business goals, and where do you see the strongest levers for AI to impact your bottom line?
• How well prepared is your data and technology architecture to efficiently integrate new AI use cases and profit from the next stage of development, such as Agentic AI?
• Do you have a clear understanding of how AI will change employee roles and requirements in your core business processes — and what this means for recruitment, training, leadership, and company culture?
• To what extent are you already using AI-supported decision-making at the strategic level?
• Do you view your organisation’s AI transformation as an actively managed, enterprise-wide transformation project — or still as a pilot initiative?  

Deloitte supports insurers in designing their AI transformation holistically

Deloitte helps insurers shape their AI transformation holistically and achieve tangible value that goes beyond single use cases. Our AI Framework makes AI a core part of your corporate strategy. By identifying targeted use cases and providing active guidance, we help you integrate AI into your operations in a sustainable way. This approach ensures that AI becomes not just a technology you use, but a key driver of sustainable business success and differentiation.

Interested? Contact us — we look forward to supporting your AI journey.

Authors

• Dr. Marcel Thom, Partner, Insurance Lead, Switzerland
• Dr. Madan Sathe, Partner, Forensic & Financial Crime
• Monika Tschudi, Director, Swiss Technology & Transformation
• Tom David, Director, Swiss Technology & Transformation