2. AI Strategy in 2025 - Developing your Strategic AI Plan.

AI Strategy 2025: From Readiness to Real-World Results

In the first segment of our AI Strategy series, we discussed conducting an AI Maturity Assessment to understand your organisation's readiness. This initial step helps you establish a clear baseline, providing the insights you need to move from aspiration to implementation with purpose and clarity.

But assessment is only the beginning. The real value comes from turning that understanding into a tailored AI strategy—a structured plan to guide your organisation from experimentation into action. At Beyond, we believe a well-designed AI strategy isn't just about adopting technology; it's about embedding intelligence across your operations and decision-making. That's what we mean by Putting Data to Work.

Without a robust strategy, AI projects risk stalling or becoming siloed experiments. With a pragmatic, action-oriented roadmap, AI can evolve from idea to implementation to impact—aligned to your business objectives, culture, and operating model. Most failed AI initiatives don’t falter because the technology doesn’t work—they struggle because the business isn’t ready to absorb and scale the change. That’s why strategic planning is as much about people and processes as it is about algorithms.

Your AI strategy must anticipate change, inspire confidence, and build capacity—not just tech infrastructure but human capability and operational maturity. The goal? To create synergy between AI and business goals that enables adoption, agility, and long-term impact.

Setting AI Objectives

Purpose-Driven Goals

AI objectives should be tightly aligned with your organisation's strategic priorities. Ask: How can AI enhance what matters most to us—customer experience, operational efficiency, innovation, or risk mitigation? AI should never be a solution in search of a problem. It must be a lever for progress on goals you already care about.

If your organisation has a broader data strategy—or departmental data initiatives—fold those in. They’re often overlooked but are crucial to forming a coherent and connected AI strategy.

Strategic Alignment Framework

1. Identify Strategic Areas: Where can AI make the biggest difference?

2. Assess Capabilities: Do we have the data, skills, and readiness to pursue this?

3. Define Objectives: Make them measurable and time-bound.

4. Integrate Into Planning: Ensure AI goals are part of business-as-usual strategy.

5. Close the Gaps: Upskill, invest, and adapt to get there.

Sample Objectives

• Personalise customer experiences to increase lifetime value by 20%

• Reduce operational costs by 15% through process automation

• Shorten product development cycles by 25% using AI-enhanced R&D

• Improve risk detection by 30% through predictive modelling

• Increase use of data-driven decisions by 40% across business units

Questions to Help Define Your AI Objectives

To help stakeholders articulate their AI goals:

1. What are the top 3–5 strategic priorities for your business?

2. How do you use data and analytics today?

3. What challenges could AI help address?

4. Where are customer or operational pain points?

5. What risks or concerns do you have about AI?

6. What AI/data projects have worked—or failed—so far?

7. What’s your long-term vision for AI?

8. How would you define success?

9. Is your tech and talent ready to support AI?

10. How will AI fit into existing strategies?

These questions spark honest conversations that go beyond the hype—and help uncover real opportunities, blockers, and priorities.

Identifying and Prioritising AI Use Cases

Use your AI maturity assessment to shortlist opportunities with real potential. Then prioritise using a simple two-axis matrix:

	Low Business Value	Medium Business Value	High Business Value
High Feasibility	Quick Wins	Strategic Bets	Transformational
Medium Feasibility	Cautious Picks	Good Bets	Long-Term Investments
Low Feasibility	Not Now	Reassess	Big Future Potential

This helps you balance early wins with longer-term plays.

Sample Use Cases

• Customer Service Automation (Chatbots, virtual agents)

• Predictive Maintenance (Avoid unplanned downtime)

• Personalised Marketing (Segment-level recommendations)

• Fraud Detection (Real-time anomaly detection)

• Supply Chain Optimisation (Forecasting and dynamic planning)

Building an AI Roadmap

Short-Term Initiatives

• Pilot in high-feasibility, high-value areas

• Track with clear KPIs

• Build proof points and momentum

Long-Term Planning

• Define a future vision for AI’s role

• Design for scalability, flexibility, and cross-functional collaboration

• Set a rolling 12–24 month roadmap with room for iteration

Milestones & KPIs

Set milestones such as:

• AI pilot completed

• Business case validated

• AI embedded in operational workflow

Track both technical performance (accuracy, latency, etc.) and business impact (cost saved, revenue uplift, satisfaction improved).

Do's and Don’ts

Do:

• Engage the business

• Stay agile

• Think ecosystem, not just use cases

Don’t:

• Overcommit to hype

• Ignore operating model impacts

• Assume tech alone delivers value

Resource Allocation

Technology Investment

Choose tools that support your goals:

• AI/ML platforms (e.g. Dataiku, AWS SageMaker)

• Data infrastructure (cloud, pipelines)

• RPA and NLP tools

Decide: build vs. buy vs. partner?

Talent Planning

Assess roles and gaps:

Role	AI Capability
BI Analyst	Limited (visualisation, reporting)
Data Analyst	Moderate (can adapt models, explore insight)
Data Scientist	Advanced (builds, trains, deploys models)

Invest in training and new hiring where needed. Embed AI fluency across the business.

Infrastructure

• Assess current tech stack

• Identify upgrades needed (compute, storage, security)

• Budget for both CapEx and OpEx

Operationalising Your AI Strategy

Integration

• Map AI into real-world workflows

• Encourage collaboration across departments

• Make AI part of daily business, not a separate stream

Change Management

• Build a change plan that includes training, support, communication

• Expect resistance—and plan to address it

• Champion adoption from the top

Conclusion

AI strategy is not a project plan—it’s a transformation journey.

You’ll need:

• Clear, business-aligned goals

• Prioritised, realistic use cases

• Flexible planning and agile execution

• Human change leadership

At Beyond, we help clients move from AI interest to AI impact by grounding strategy in reality, delivering early wins, and building long-term value.

Ready to get started? Let’s talk about your AI strategy. https://www.puttingdatatowork.com/contact-us