The Strategic Role of AI in Business Transformation

How AI shifts business models, decision-making and value creation

Introduction

In today’s fast-moving landscape, artificial intelligence (AI) no longer sits at the fringes of business innovation—it’s fundamentally rewriting how organisations create value, make decisions and evolve their models.

For senior leaders, brand strategists and service-based organisations, the challenge is not simply to adopt AI, but to strategically embed it into the architecture of their business so that transformation becomes sustainable, differentiating and aligned with purpose.

This pillar page explores how AI is reshaping business models, decision-making and value creation, what organisations must get right, and how you can think of AI as a lever for strategic advantage rather than simply a cost-cutting tool.

1. Why AI is becoming a strategic business lever

There was a time when AI was discussed mainly in terms of narrow automation tasks: chatbots, predictive analytics, image recognition. But today we are witnessing a leap: generative AI (Gen AI), increasingly sophisticated machine-learning models, automated decision engines and more. According to a recent survey by McKinsey & Company, organisations using Gen AI are more likely to report revenue increases and cost reductions in specific business units. McKinsey & Company

Yet, the same survey also shows that relatively few firms have seen enterprise-wide bottom-line impact, underscoring that transformation is not automatic. McKinsey & Company

What does this tell us? That AI is moving from project-mode to strategy-mode: the real opportunity is re-imagining what the business does (value creation) and how it makes decisions—not just applying a tool here and there.

1.1 From support-function to core business model

Under strategic scrutiny, AI shifts from being an enabler of incremental efficiency to becoming a driver of business model innovation. In fact, research shows that AI enables new revenue streams, new value propositions and new ways to capture value. ResearchGate+1

Take the Business Model Canvas (value proposition, channels, customer relationships, revenue streams, resources, activities, partnerships, cost structure). Studies find that AI-driven organisations configure these components differently. For example, in one comparative study of AI- start-ups vs traditional firms, AI-led firms changed their value proposition, channels and revenue streams more radically. managementdynamics.ro

1.2 Decision-making upgraded

AI is also rewriting executive decision-making. Instead of intuition + historic trend data, leaders now have access to models that can simulate scenarios, detect patterns in real time, predict outcomes and surface insights from large data sets. This empowers strategic agility.

But—and this is key—the value lies not only in the model, but in the decision architecture. If the organisation hasn’t adapted its workflows, governance, and human-machine interplay, the strategic promise will fall short.

1.3 Value creation and capture

It’s often said that “data is the new oil”. In the AI era, data becomes the fuel for value creation—but only if the organisation can capture and monetise that value. A 2022 study by J. Åström and colleagues argues that AI providers need to align value-creation mechanisms (what new value is generated) with value-capture mechanisms (how the company monetises or benefits) in order to develop commercially viable AI business models. SpringerLink

In short: you can create value with AI, but if you can’t capture it (or embed it in the business), the strategic payoff will be limited.

2. What strategic transformation looks like in practice

Let’s break down what a strategic, AI-enabled transformation looks like in three main dimensions: business model shift, decision-making architecture, and organisational design.

2.1 Business model shift

Here are key areas where AI enables business model change:

New value propositions: AI enables service-based organisations (think coaches, consultants, wellness practitioners) to bundle insight, personalisation and automation into new offerings—e.g., AI-augmented coaching, predictive wellbeing triggers, or generative content at scale.
New revenue streams: By leveraging AI, organisations can migrate from one-to-many services to “platform” or “subscription + AI insight” models. Academic work shows AI-driven business model innovation (BMI) is emerging as a frontier of change. ResearchGate+1
Changed cost structure and resources: AI can significantly reduce manual labour in repetitive tasks, free up human talent for high-value tasks, and shift resources toward data, algorithmic assets and human-AI collaboration.
Ecosystem and partnerships: AI often requires partnerships—data providers, model vendors, integration partners—and this can shift roles from “we build everything in-house” to “we orchestrate a network”.
Customer engagement and channels: AI allows hyper-personalised experiences, automation of service delivery, and new channels (voice agents, chatbots, generative interfaces) that change how the organisation interacts with clients.

2.2 Decision‐making architecture

Strategy, marketing, operations, HR—all these functions are being transformed by AI-powered decision architectures.

Decision-support systems: Rather than assuming human decisions, organisations are layering AI systems that recommend actions, rank options, or even execute decisions under governance.
Real-time monitoring and scenario planning: Models can simulate business model shifts, identify emerging risks, and surface strategic insights faster than traditional business intelligence.
Human-in-the-loop governance: It’s not about replacing humans; it’s about redesigning the interplay between humans and machines. The “human + machine” paradigm becomes central.
Ethics and trust built into decisions: Decision architecture must include transparency, auditability, and ethical guard-rails—not just to meet regulatory demands but to safeguard brand equity and stakeholder trust.

2.3 Organisational design & capability

For transformation to succeed, organisations must adapt their structure, culture and capabilities.

Leadership and mindset: Top executives need to champion AI as a strategic lever, not just an IT project.
Data & technology platform: Without a reliable data foundation and modular tech architecture, AI will become fragmented.
Skills and workflows: Staff must evolve from manually executing tasks to orchestrating and partnering with AI systems.
Governance and risk: AI introduces new risks (algorithmic bias, data privacy, model drift). Strategic governance frameworks are essential.
Change management: Since AI changes roles, workflows and operating rhythms, the human side of change must be managed intentionally.

3. Five strategic steps to lead AI-enabled transformation

Here is a practical roadmap for senior decision-makers and brand strategists to move from intention to impactful transformation.

Step 1: Clarify your strategic objective

Ask: What is the strategic purpose of AI within your organisation? Is it growth-oriented (new revenue), customer-experience driven (personalisation at scale), or operationally driven (efficiency + scalability)? Having clarity aligns stakeholders.

Step 2: Map the value chain and decision architecture

Analyse your current business model: value proposition, channels, resources, workflows, cost and revenue streams. Identify where AI could disrupt or enhance. Use the Business Model Canvas or similar frameworks. Prioritise where AI can bring unique strategic advantage.

Step 3: Build your data and technology foundation

Without usable, high-quality data and an architectural backbone, AI will under-deliver. Invest in data governance, integration, model management, and the technology stack (cloud, APIs, model frameworks).

Step 4: Reframe roles, processes and culture

AI changes who does what, how decisions are made and what value humans bring. Define the human-machine boundaries, redesign workflows, and foster a culture of experimentation, continuous learning and agility.

Step 5: Monitor, measure and iterate

Success isn’t “installing AI” but embedding it and capturing value. Use metrics aligned with business strategy (revenue growth, cost savings, decision-time reduction, customer-engagement uplift). Iterate your model: pilot, scale, refine.

4. Key strategic challenges and how to address them

Any transformation of this magnitude brings complexity. Understanding the hurdles ahead is as important as knowing the opportunities.

4.1 Gap between experimentation and enterprise-scale value

Many organisations pilot AI initiatives that never scale across the business. For example, McKinsey found that while many business units report value from Gen AI, more than 80 % of organisations do not yet see enterprise-wide EBIT impact. McKinsey & Company

Address it by: aligning pilots with strategic objectives, securing C-suite sponsorship, ensuring integration into core workflows rather than side projects.

4.2 Value capture vs value creation misalignment

It is possible to create value but fail to capture it effectively. The study by Åström (2022) emphasises how aligning value-creation and value-capture is vital for commercially viable AI business models. SpringerLink

Address it by: explicitly defining revenue models and capture mechanisms at the outset, not just the technical model output.

4.3 Data and technical infrastructure challenges

Poor data quality, fragmented systems, lack of integration, and legacy technology hamper strategic AI.

Address it by: conducting a data-readiness audit, securing clean and accessible datasets, building modular architecture, and ensuring scalability.

4.4 Organisational resistance and skill gaps

Change-resistant cultures, unclear roles, and lack of AI literacy can slow or derail transformation.

Address it by: investing in up-skilling, change management programmes, and creating cross-functional teams to embed AI adoption.

4.5 Ethical, governance and regulatory risks

AI introduces risks of bias, opacity, privacy breaches and regulatory non-compliance—all of which can negatively impact the brand.

Address it by: establishing governance frameworks early, defining accountability, creating transparency and embedding ethical considerations into the strategy.

5. Spotlight on sectors: how organisations are rethinking their models

Service-based professionals (coaches, consultants, wellness)

For organisations like yours (solo entrepreneurs, consultants, wellness professionals) the strategic role of AI means moving beyond “website plus marketing automation” to crafting differentiated value: for example, AI-augmented personalisation, predictive insights for clients, intelligent lead-nurture flows and content generation that supports deep client relationships.

Think of: “What could our business model look like in 3-5 years if we embraced AI as a core value driver rather than an add-on?”

Manufacturing, fintech and advanced industries

Research shows that industries that embed AI in core functions and business models (for example manufacturing value-chain re-design, fintech platform models) are more likely to gain strategic advantage. For example, a systematic review by Jorzik (2024) found two key contributions: structured analysis of AI-driven business model innovation in 180 articles. sciencedirect.com

Start-ups versus incumbents

Start-ups that are AI-native can build radically different models from scratch, whereas incumbents often take iterative approaches. The study by Figura (2025) shows that AI-startups changed value proposition, channels and revenue streams more radically than traditional firms. managementdynamics.ro

6. Future-looking trends and implications for brand strategy

As a brand strategist thinking ahead, here are five trends to keep on your radar:

Trend 1: Generative AI & hyper-personalisation

The rise of Gen AI means brands can scale unique, personalised experiences in ways previously impossible. This opens new value propositions: “AI-powered brand voice”, “dynamic micro-content at scale”, “real-time personalisation across customer journeys”.

Trend 2: Platform and ecosystem-based business models

AI increasingly enables platform business models (connecting humans, machines, data and communities). As noted by thought-leader Sangeet Paul Choudary, a shift from pipeline to platform logic is central to modern business strategy. Wikipedia

Trend 3: Decision-intelligence as a discipline

Rather than just “AI projects”, the discipline of “decision-intelligence” (designing decision-making systems with humans and machines) will become critical. Brands that master this will outpace others in agility and insight.

Trend 4: Hybrid human-machine workforce

The future workforce is hybrid: humans empowered by AI, focusing on empathy, creativity, judgement; machines handling scale, repetition, pattern recognition. This demands a rethinking of roles, culture and brand promise as employer.

Trend 5: Responsible AI as a brand differentiator

Ethics, transparency and governance will increasingly feed into brand reputation. Organisations that embed responsible AI into their brand story stand to gain trust, loyalty and differentiation.

7. How to embed AI into your brand strategy

Here are practical steps for brand strategists and organisational leaders:

Align AI to your brand promise: What your brand stands for should shape how you adopt AI. For example, if your brand emphasises “personal human connection”, ensure AI supports rather than replaces that experience.
Map client journeys & identify AI-inflection points: Map how your clients engage with your services, and identify where AI can enhance insight, personalisation, speed or scale.
Design human-machine collaboration in your value delivery: Decide which tasks humans will retain and which will shift to AI. Clarify transitions and guardrails.
Monitor and communicate the impact: Use metrics that matter (client outcomes, decision latency, satisfaction, cost to serve). Communicate how AI enhances your brand experience—not just automates it.
Tell a transparent story: Be open about how you use AI—what data you collect, how decisions are made, and how you protect privacy and fairness. This builds trust.
Iterate the model: AI strategy is not “set and forget”. Establish feedback loops, refine models, and align your brand offering as the business model evolves.

8. Summary

The strategic role of AI in business transformation is not about simply automating existing tasks—it’s about re-imagining how value is created, how decisions are made, and how the organisation operates in an AI-augmented world.

Now is the time to think strategically: what will your business model become when AI is fully embedded? How will your brand promise evolve? How will your decision-making, workflows and client experience change?

If you’d like to explore how to map your current state, identify strategic AI opportunities and craft a transformation roadmap aligned with your brand strategy, let’s begin the conversation.

References

J. Åström et al., “Value creation and value capture for AI business model innovation”, Journal of Innovation & Knowledge, vol. 7, 2022. SpringerLink
P. Jorzik, “AI-driven business model innovation: A systematic review”, Journal of Business Research, 2024. sciencedirect.com
C. Cei, “Conceptualisation of the relationship between AI and value creation in manufacturing”, Master’s thesis, 2025. diva-portal.org
M. Figura, “From idea to impact: The role of artificial intelligence in business model evolution”, Management Dynamics, 2025. managementdynamics.ro
McKinsey & Company, “The State of AI: Global survey”, March 2025. McKinsey & Company
Teng D., “Generative-AI’s effects on new value propositions in business model innovation”, 2025. sciencedirect.com