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  • Writer's pictureBeyond Team

2. AI Strategy - Developing your Strategic AI Plan.

Updated: Apr 19

Introduction to AI Strategy

In the first segment of our AI Strategy series, we discussed the process of doing an AI maturity assessment, laying the groundwork to understand where your business stands in terms of its AI readiness. This assessment is a great first step, providing a clear baseline from which to start your AI journey. It acts as the guide to set out your plan for AI implementation, ensuring you're moving forward with purpose and clear goals.

As you switch from assessment mode into action, your focus needs to shift to developing blueprint—a tailored AI strategy which will support the identification and launching AI initiatives in an organised way as well as leading you and your teams through the critical thinking needed to operationalise them in a way that they integrate into your business and deliver on the promised value. Here at Beyond a well designed, robust AI strategy goes beyond mere adoption; it’s about embedding intelligence into your business processes and decision-making - hence our strap-line of Putting Data to Work.

The need for a strategy cannot be overstated. Without it, AI projects risk being merely experiments rather than drivers of transformation. A pragmatic, action orientated AI strategy becomes your plan for turning potential from creative idea all the way through into performance, ensuring that the AI capabilities you develop align with your business objectives, culture, and operational realities. We see many many AI projects falter after the first few hurdles, even after they have shown themselves to be effective, simply because the plan to opreationalise them has not been in place and working alongside the programme. Pretty much any AI related projected is going to impact ways of working, operating models etc and when these happens it means people enter the equation. This needs a strong plan to manage change in a positive way.

As such, this strategic planning is not just about technology—it's about people, processes, and the future state of your enterprise. It’s about anticipating the needs of your market and customers, reimagining the possibilities, and setting a course towards a future where AI is not an add-on but an integral engine of innovation and growth.

As we dive into the detail of the nitty gritty of the development of your AI strategy, remember that the ultimate goal is to create the right synergy between AI and your business goals that will allow adoption and use to happen. AI solutions, for all their apparent human-like qualities need to operate within a system and will need continuous service and maintenance. So, your strategy should be about laying a foundation today that will support the achievements of tomorrow, and ensuring that the business can take small incremental steps towards realising the transformative potential of AI for ultimately what we all hope to be large, sustained business impact.

abstract representation of ai strategy roadmap

Setting AI Objectives

Purpose-Driven Goals

At the heart of a successful AI strategy is the alignment of AI goals with your overarching business objectives. The purpose of AI in your organisation should resonate with the core mission, addressing real business challenges and opportunities. AI shouldn't be deployed just for the sake of innovation; rather, it must serve a defined purpose that advances your company's agenda.

This alignment means that AI initiatives should be designed to enhance customer experiences, streamline operations, or unlock new avenues for growth – whatever your business values most. To ensure AI supports these core business values, each objective needs to be underpinned by a clear understanding of how AI can enhance these areas. It's about matching AI's capabilities to your business's aspirations and challenges.

Bear in mind that your organisation will have probably done any number of strategic studies around data. There may already be a Data Strategy for the whole business, or individual departments for example such as Finance may have developed their own Finance Data Strategy. Don't miss a trick by thinking you can set off without folding these into your own strategic plan as they will become crucial cogs in your strategic wheel.

Strategic Alignment

The process of setting AI objectives must be woven into the fabric of your strategic planning. It involves a series of steps:

  • Identifying Strategic Areas: Determine which areas of your business can benefit most from AI. Whether it's improving customer service, increasing operational efficiency, or driving innovation, the key is to focus on areas that will have the most significant impact on your strategic goals.

  • Capability Assessment: Evaluate your current capabilities against the potential AI offers. This assessment should consider not just technological infrastructure but also skills, processes, culture and appetite for change.

  • Objective Definition: Develop clear and measurable objectives that reflect both the potential identified in the capability assessment and the strategic areas of focus. This might include increasing customer satisfaction scores, reducing operational costs, or achieving specific innovation milestones.

  • Integration into Strategy: Embed these objectives into your broader business strategy, ensuring they have the visibility and priority necessary for successful implementation. They might require adjustments to your business model or operational processes to accommodate and leverage AI effectively, so ensuring they are included as part of your broader strategy is important.

  • Closing the Gap: Identify any gaps between current capabilities and what is needed to achieve your AI objectives. Develop a plan to close these gaps, whether through technology investments, partnerships, training, or hiring.

Example Objectives

To give you a clearer idea of what AI objectives might look like, consider the following examples:

  • Customer Experience: Use AI to personalise customer interactions, aiming to increase customer lifetime value by 20% within the next fiscal year.

  • Operational Efficiency: Implement AI-driven process automation to reduce operational costs by 15% while maintaining or improving quality and compliance standards.

  • Innovation: Leverage AI to accelerate the product development cycle, with the goal of reducing time-to-market by 25%.

  • Risk Management: Deploy AI for predictive risk analytics to improve risk detection by 30%, thereby mitigating potential financial and reputational damage.

  • Data-Driven Decision-Making: Use AI to enhance business intelligence capabilities, aiming to increase the use of data-driven decision-making across the business by 40%.

By setting specific, aligned, and measurable AI objectives, you can direct AI initiatives to contribute meaningfully to your strategic goals, driving tangible business outcomes.


What to ask your stakeholders to help uncover and define your AI objectives

For one of our strategy consultant preparing to help a clients stakeholders identify their objectives for their AI strategy, asking the right questions is a really important tool to help frame the right conversations with the right people to get clarity. We've summarised our some of our interview toolkits to give you our top ten starter questions to help you start having these conversations in your business:

  1. What are the top strategic priorities for the business over the next 3-5 years? - This question helps understand the broader context within which AI must operate and ensures that AI initiatives are relevant to long-term business goals.

  2. How do you currently use data and analytics in your department/functional decision-making processes? - This question helps draw out existing capabilities and identifies potential areas where AI could enhance decision-making.

  3. What specific business challenges or opportunities do you believe AI could help address? - Encourage stakeholders to think about specific problems ensures that potential AI solutions have a purpose and are tailored to real business needs.

  4. Which customer or operational pain points could be alleviated through better use of AI? - This helps focus the discussion and pinpoint practical applications of AI that could improve customer experiences or operational efficiency, where we know there are likely to be some quick wins.

  5. What are the perceived risks or concerns within your organisation regarding AI deployment? - Understanding hesitations or resistance early on can help in managing change and aligning AI strategy. It also serves as a useful way to identify gaps in understanding that you can begin to fill.

  6. Can you describe any previous or ongoing AI or data-driven initiatives? How have they impacted the business? - This sheds light on past experiences, lessons learned, and will provide an idea of the maturity of the business in handling data and AI projects.

  7. What is your vision for AI in your business? How do you see it evolving over time? - This encourages stakeholders to offer their own views as well as think beyond immediate needs and consider longer-term ideas.

  8. What specific outcomes or metrics would you use to measure the success of AI in your business? - Setting clear, measurable objectives is super important for evaluating the effectiveness of AI initiatives and ensuring they deliver tangible value. Getting people to talk early on about how they will be evaluating success or failure, not only gives you a good heads up, but often signals areas of concern that they may not have articulated before.

  9. How aligned is our current technological infrastructure and talent pool to support advanced AI implementations? - This gets the conversation going about realistically what skills are already available and where are the big gaps in technology to be addressed to support AI objectives effectively. It will also help highlight future implementation risks.

  10. How can AI be integrated into our existing strategic plans, and what adjustments might be necessary? - This question aims to get people thinking about how things will look when the proposed AI sollutions are in place and how things will be different. This helps ensure the right upfront thinking is being done such that that AI initiatives are not siloed but integrated into the overall strategic framework of the organisation, enhancing coherence and synergy.

Hopefully these questions will not only facilitate a deeper understanding of the strategic needs and appetite of your business but will kick off the right kinds of conversations, beyond the fun visionary stuff that often excites people around AI and gets some more heavy hitting conversation going on some of the how.


Identifying AI Use Cases

Assessment-Driven Selection

A successful AI strategy is not about deploying AI everywhere, but more about where it can make the most significant impact. The outcomes of your AI maturity assessment act as a guide here, guiding you to areas ready for AI integration. This step is about translating assessment insights into actionable AI initiatives.

Use cases should be chosen based on their potential to drive your business forward. The criteria for selection should focus on impact potential, data readiness, technological feasibility, and alignment with strategic business goals. A use case with high business value but low current feasibility may require more groundwork, while those with both high business value and high feasibility are prime candidates for early adoption.

Prioritisation Matrix

With such a variety of possible AI applications, a prioritisation matrix becomes a vital tool. This matrix should evaluate potential use cases against two key dimensions:

  • Feasibility: Consider the technical and operational readiness of your business to implement the AI use case.

  • Business Value: Assess the potential impact on revenue, cost savings, customer satisfaction, or other critical business metrics.

Low Business Value

Medium Business Value

High Business Value

High Feasibility

Quick Wins (Low Priority)

Strategic Bets (Medium Priority)

Transformational Initiatives (High Priority)

Medium Feasibility

Consider with Caution

Opportune Gains

Long-Term Investments

Low Feasibility

Not Recommended

Reassess Feasibility

Speculative Ventures

This matrix helps in identifying not just 'low-hanging fruit' but also strategic initiatives that could offer substantial benefits in the longer term. By striking a balance between quick wins and strategic projects, you can maintain momentum while building towards a more ambitious AI-enabled future.

Example Use Cases

For illustrative purposes, here are several use cases that you might consider, depending of course on the results of your AI maturity assessment and strategic objectives:

  • Customer Service Automation: Implement chatbots or virtual assistants to handle routine customer inquiries, reducing response times and freeing up human agents for more complex tasks.

  • Predictive Maintenance in Manufacturing: Use AI to predict when machines are likely to fail or need maintenance, thus avoiding costly downtime and extending the lifespan of equipment.

  • Personalised Marketing: Deploy AI to analyse customer data and tailored marketing campaigns, improving conversion rates through personalised offers and content.

  • Fraud Detection: Leverage AI to identify patterns indicative of fraudulent activity in financial transactions, thereby enhancing security and trust.

  • Supply Chain Optimisation: Use AI for real-time supply chain adjustments based on predictive analytics, optimising inventory levels and reducing waste.

Identifying the right AI use cases is a major step in bridging the gap between AI's potential and actual business value. By carefully selecting and prioritising these, you lay a foundation for your AI initiatives to flourish and drive significant business impact.

Building the AI Roadmap

Short-Term Initiatives

The journey toward AI integration should begin with short-term initiatives that can deliver quick wins. These serve as proof points for the value AI can bring and help to build momentum and buy-in across the business. 

Immediate steps could include

  • Deploying Pilot Projects: Start with pilot projects in areas identified as high-impact and feasible to demonstrate the benefits of AI.

  • Quick Win Selection: Choose initiatives that can be rapidly implemented and deliver tangible results, such as automating routine administrative tasks or enhancing customer service with chatbots.

  • KPI Establishment: For each short-term initiative, establish Key Performance Indicators (KPIs) that will enable you to measure success and learn from these early experiences. These could include metrics like customer satisfaction scores, response times, or operational cost savings.

Long-Term Planning

Long-term planning involves a broader vision for how AI will contribute to your business’s growth and evolution. This includes:

  • Strategic Vision: Define how AI fits into the larger business strategy and the role it will play in future growth.

  • Scalability Considerations: Develop a plan for how to scale successful AI pilots into broader deployments that can transform entire functions or business units.

  • Flexible Timelines: Establish a timeline for your AI initiatives, but ensure that it has the flexibility to adapt to new technology trends, market changes, or shifts in business strategy.

Milestone Development

Creating a roadmap for AI implementation requires setting clear, measurable milestones. This enables the tracking of progress and helps in course corrections as needed. Consider the following:

  • Measurable Milestones: Determine milestones that are specific, measurable, and aligned with strategic business outcomes, such as a 20% reduction in manual processing time or a 10% increase in leads generated.

  • Performance KPIs: Define KPIs that measure both the performance of the AI systems and their impact on business objectives. This dual focus ensures that AI systems are not only technologically sound but also driving real business results.

Do's and Don'ts of Developing an AI Roadmap


  • Engage stakeholders from across the business to ensure the roadmap reflects diverse perspectives and needs.

  • Remain agile in your planning to adapt as you learn from early initiatives and as external conditions change.

  • Prioritise initiatives that reinforce one another, building an ecosystem of AI applications that support shared goals.


  • Don't create a roadmap in a vacuum, isolated from broader business strategies and operational realities.

  • Don't set rigid timelines that don't allow for iteration and learning from early stages.

  • Don't overlook the need for change management and cultural shifts that may be required to support AI initiatives.

A well-constructed AI roadmap is a dynamic document that guides your organisation through the complexities of AI adoption. It should be revisited and revised regularly as new insights are gained and the business environment evolves. With a solid roadmap in place, your AI journey can proceed with clarity, direction, and a greater chance of achieving the intended outcomes.

Resource Allocation

Technology Investment

Investing in the right technology stack is essential to empower your AI initiatives. This step involves:

  • Identifying Required Technologies: Enumerate the software, hardware, and platforms that align with your AI strategy. This could include AI modelling tools, cloud computing services, and data analytics software.

  • Build vs. Buy Decisions: Weigh the pros and cons of building your own AI solutions versus purchasing off-the-shelf technologies. Consider factors such as customisation needs, cost implications, time to deployment, and internal capabilities.

Technology Checklist

Type of Technology


Example Brands

AI Modelling Tools

Software platforms that allow for the creation, training, and deployment of AI models.

TensorFlow, PyTorch, H2O

Cloud Computing Services

Platforms offering scalable computing power and storage, facilitating AI development and deployment.

Amazon Web Services (AWS), Microsoft Azure, Google Cloud Platform

Data Analytics Software

Tools that enable the analysis and visualization of data to extract insights, often with built-in AI capabilities.

Tableau, SAS, IBM Cognos Analytics

Data Integration Tools

Solutions that assist in combining data from different sources, critical for AI data preparation.

Informatica, Talend, MuleSoft

Machine Learning Platforms

End-to-end platforms designed to make machine learning more accessible to non-experts.

Dataiku, RapidMiner, Alteryx

Natural Language Processing (NLP) Services

Technologies that help machines understand and interpret human language, vital for AI applications involving text data.

NLTK, SpaCy, Google Cloud Natural Language

Robotic Process Automation (RPA) Software

Software robots that automate repetitive tasks, often using AI to improve over time.

UiPath, Automation Anywhere, Blue Prism

AI Development Environments

Integrated development environments (IDEs) specifically designed for AI and machine learning projects.

Jupyter, RStudio, Visual Studio Code

Data Labeling Platforms

Services that provide tools or workforce to label large datasets, which is necessary for training AI models.

Mechanical Turk, Label

Predictive Analytics Software 

Applications that use data, statistical algorithms, and machine learning techniques to predict future outcomes.

Alteryx, SAS Predictive Analytics, IBM SPSS Statistics

Conversational AI Platforms

Platforms that build and deploy chatbots and virtual agents capable of natural and human-like interactions.

Dialogflow, Microsoft Bot Framework, IBM Watson Assistant

Talent Acquisition and Development

The success of AI initiatives is largely dependent on the people behind them. For this, you need to:

  • Evaluate Talent Needs: Assess the skill sets required to execute your AI strategy effectively and identify any gaps in your current team.

  • Recruitment and Training: Plan for recruiting new talent with the requisite AI expertise or up-skilling existing employees. Offer training programs to develop AI skills across the business.

  • Cultivate an AI Culture: Promote an environment that embraces AI and encourages continuous learning. This involves integrating AI into various aspects of the business and empowering employees to innovate using AI.

It's also going to be really important that you reflect on the specific skills and capabilities you will need. It can be tempting to look at job titles and say "hey we've got plenty of people that can do data", without realising that the skill sets and capacity to "do AI" is very different.

By way of example, here’s a table that compares the roles of a traditional Business Intelligence (BI) Analyst, Data Analyst, and Data Scientist, specifically focusing on their skills and capabilities in developing AI solutions.

This comparison highlights how understanding how each role contributes differently to AI initiatives and why you need to factor this into your planning.


Core Skills

Capabilities in AI Solutions Development

BI Analyst

SQL and database management: Reporting tools (e.g., Tableau, Power BI); Basic statistical analysis

Limited AI development; primarily uses AI tools for enhanced reporting and data visualisation

Data Analyst

Advanced SQL:Statistical software (e.g., R, Python): Data cleaning and preparation

Moderate AI capabilities; can implement and modify existing AI models, primarily for predictive analytics

Data Scientist

Machine learning: Advanced statistical analysis and mathematical modelling: Programming in Python/R: Data engineering

Extensive AI development; designs and builds new AI models, integrates AI into existing systems, and innovates with deep learning and other advanced techniques

Infrastructure Considerations

The underlying infrastructure must be capable of supporting the scale and complexity of AI applications. This includes:

  • Current Infrastructure Assessment: Review your current IT setup to identify any limitations that could hinder AI deployment.

  • Infrastructure Upgrades: Determine the upgrades needed to support AI, such as enhanced data processing capabilities, increased storage capacity, or more robust security measures.

  • Budgeting: Allocate budget for necessary infrastructure enhancements, considering both initial implementation costs and ongoing operational expenses.

Resource allocation for AI is not just about capital investment; it's about investing in a future where AI is an integral part of your business strategy. This investment positions you to capitalise on the transformative power of AI, fostering innovation and maintaining a competitive edge in an increasingly AI-driven marketplace.

Operationalising Your AI Strategy

Integration Into Business Operations

The real test of an AI strategy's value is its seamless integration into day-to-day operations. Achieving this requires:

  • Embedding AI in Workflows: Identify and map out the workflows where AI can be integrated. This might involve automating certain tasks, enhancing decision-making with AI-driven insights, or creating new services powered by AI.

  • Cross-Departmental Collaboration: Encourage collaboration across different departments to ensure that AI solutions are effectively integrated into all areas of the business. This helps in fostering a unified approach where AI becomes a shared tool for achieving business goals.

  • Securing Buy-in: Gain the support of key stakeholders by clearly communicating the benefits of AI integration and how it can make their work more efficient and impactful. This buy-in is crucial for the successful adoption and usage of AI across the business.

Change Management

The introduction of AI is as much a cultural shift as it is a technological one. AI is always going to change a process in some way and it's extremely likely that will impact a team and or individuals along the way. At Beyond we see that people react very differently to change and whether this is a positive or negative reaction, managing that change and embedding new ways of working or adaptations is an important part of ensuring the successful implementation. Th therefore requires:

  • Strategic Change Management: Develop a change management plan that addresses the human side of AI adoption. Clearly articulate the benefits, address any concerns, and foster a culture of openness to change.

  • Comprehensive Training Programs: Implement training programs to build AI literacy across the organisation. Ensure that all levels of staff have the knowledge and support they need to work alongside AI.

  • Ongoing Support and Resources: Provide resources and support to help employees adapt to AI tools and platforms. This includes having a helpdesk, providing access to online learning resources, and setting up a system for feedback and continuous improvement.

Operationalising your AI strategy is not a one-time event but an ongoing process. As AI technologies evolve, so too should their applications within your business.

By continuously refining the integration of AI and ensuring your team is equipped to leverage it, you solidify AI as a fundamental component of your operational strategy, driving innovation and efficiency throughout the business.


Embarking on the journey to AI maturity is a strategic imperative for any forward-looking business. The pathway to a successful AI strategy we've outlined here is designed to set out your stage for a future where AI becomes more than just a buzzword but key tool in your kit bag and a driver of business transformation. At its core, this journey is about aligning AI capabilities with business goals, identifying and prioritising impactful use cases, careful planning and allocating resources, and integrating AI into everyday business operations.

Crucial to this journey is the acknowledgment that AI strategy development is not a static plan but a dynamic process that relies on continual learning and adaptation. As AI technologies and your business evolve, so too should your strategy. It requires an iterative approach, one that is receptive to change and resilient to the challenges that will undoubtedly arise.


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