In my last blog post, I discussed how to create a strategy.
In this blog post, I will share with you a strategy I wrote as a Head of Product, focused on developing our Product Management practice.
For each section of the strategy, I’ll highlight the choices I’ve made. I’ll label this as commentary. For legal reasons, I’ve removed anything that could be considered confidential. The company name has been changed to ACME (I know!). Also, any specific data points about Product Managers have been altered.
Strategy for Product Management at ACME
This strategy is a high-level plan that outlines how we will meet our vision. We will review our strategy quarterly.
Last reviewed: 28th November 2024.
The aim of this strategy
- Enable our Product Managers to deliver measurable value to clients by developing expertise in core product management, core delivery management, data analytics, and AI implementation.
- Create a competitive advantage by developing Product Managers who can effectively blend traditional delivery and product management with emerging technologies.
- Establish ACME as the premier hub for modern delivery and product management in our area, specialising in AI-enhanced product development.
- Provide clear development pathways for Product Managers at all levels.
Commentary: being clear on how often the strategy is updated and reviewed, demonstrates to your reader that you are serious about the strategy. This section makes it clear what the strategy is for. It’s not just a document on your shelf.
Reflection on current progress
Excellence
- Successfully built and implemented a product toolkit
- Created a template workshop board to accelerate project work
Inspiration
- Delivered six product-related talks at community events reaching 100+ professionals in the local tech community
Capability
- Completed initial benchmarking of product competencies, establishing baseline scores for 100% of Product Managers
- Created a business case for AI tooling
Commentary: the work we do is hard. Celebrate your achievements. Demonstrate you are making tangible progress to your key objectives. At this stage my headings of: excellence, inspiration and capability, will not be well understood by you. Later on in the strategy, I reference what these mean. Also, I share some metrics I will track to judge the sucess of the strategy. Consider including these metrics in this section.
Vision
To establish a centre of excellence in AI and data that promotes a delivery and product-focused mindset, encourages personal growth, and shares knowledge to inspire the wider company.
Commentary: the vision is a statement of intent. It’s not a lofty goal, but a clear statement of what you are trying to achieve in the next 5 years. It should be inspiring.
Insights
Market analysis
- AI is shaping and changing the future of work. 32% of global respondents reported improved productivity due to AI tools like Chat GPT and Dall-E in 2024.
- Growing demand for Product Managers who can bridge traditional Product Management with AI/data capabilities.
- Increasing importance of human skills alongside technological advancement.
Commentary: use data from research organisations and Statista to find key data points about your market. Understanding where your market is heading helps you work out where you will play as an organisation.
User understanding
- Our Product Managers range from Level 1 to Level 5
- Our Product Managers have mixed experience with AI. 3/6 have worked with AI products. 5/6 have a basic understanding of Generative AI, Prompt Engineering and Machine Learning. 2/6 have experience in understanding AI ethics. All our Product Managers use AI tools.
- 4/6 of our Product Managers have built data products. 6/6 have experience in setting KPIs but only 2/6 have experience in A/B tests. All of our Product Managers know how to use PowerBi.
- Our competency mapping shows strong collective capabilities in ‘Product Vision and Roadmapping’
- Our competency mapping shows we need to develop in ‘Product Quality’, ‘UX Design’, ‘Product Leadership’, and ‘Influencing’. We have made progress in these areas by running focused quarterly events.
A chart showcasing average Product Management competency scores. The scores are: Level 1-2 (Beginner), Level 3 (Novice), Level 4 (Practitioner), Level 5 (Advanced) and Level 6 (Specialist). Please be aware this data is self assessed. This data is just one view into capability.
Commentary: if you don’t have data, use surveys or speak to your users to collect it. In this case, I benchmarked our Product Managers through a competency survey to understand where they felt they were in core product, data and AI skills. I would use charts where possible to present data. Please note, I have not included a full summary of what I benchmarked Product Managers against.
Competitor analysis
- Our competitors are providing specialist AI and data services.
- To differentiate from our competitors, we need our Product Managers to become specialists, and well rounded individuals e.g. ‘T-shaped’. Our competitors haven’t done this yet.
- Diversifying our skillset means that we can flex into different opportunities, and ask targeted questions to solve complex problems.
- It enhances our capability to adapt to market changes and emerging technologies.
- It will mean we are in a stronger position to deliver value through data and AI-driven solutions.
Commentary: an alternative structure to follow is the 5 forces framework.
Challenges
These are dependencies and potential roadblocks that we may encounter. These challenges help us prioritise, and help to define our approach (see next section).
Technical Challenges
- Access to appropriate tools and technologies for experimentation
- Need for diverse AI model access and training environments
- Hardware and software requirements for practical learning
- Integration of learning with existing client work
Operational Challenges
- Varied learning needs and career aspirations across the team
- Finding learning opportunities within client engagements
- Balancing individual learning goals with market demands
- Need for structured benchmarking in data and AI capabilities
- Resource allocation for training and development
Regulatory Challenges
- Compliance with EU AI Act and emerging regulations
- GDPR and data protection considerations
- Ethical AI development and deployment
- Need for ongoing regulatory awareness
Commentary: the purpose of a strategy is not to explicity list how these challenges will be mitigated. These challenges need to be considered when delivering.
Approaches
Our strategy employs three core pillars:
1. Excellence: drive consistent high standards of work setting the benchmark for quality
We will stand out by delivering superior quality and performance on client work. We will establish clear quality benchmarks for our work. We will focus on outcomes not outputs. The work we do will be measurable. If the client does not have any metrics, we will help them set it. This will show the work we do is having an impact. We will fill our armoury with tools and techniques to overcome common challenges. We will create best practice guidelines for AI and data projects. We are able to work effectively remotely and in-person.
2. Inspiration: play a leading role in operating the Club with excellence
When we deliver great work, we want others to know, so they can learn lessons from us. We will share our work through talks, toolkits, blog posts, podcasts and communities of practice with 80%+ participation. We want to ensure Product Managers have a mentor, and we establish regular knowledge-sharing sessions. We will be the centre of excellence for product at ACME.
3. Capability: inspire talent to reach and exceed their career growth ambitions
We will grow our skillset, in areas identified by competency mapping, to become strong Product Analysts first. To become ‘T-shaped’, we will expand our skills into other spaces.
A. Core delivery
Keeping delivery simple will be at the heart of what we do. We will be ‘flexible’ and not ‘purists’ when applying agile methodologies. We will work in a lean manner to avoid waste. We will ensure our Product Managers can adapt to complex environments. This means knowing how to apply agile at scale or scaling teams from scratch. Where appropriate, we will use data to assess performance to inform targeted actions that yield value.
B. Core product
We will ensure we’ve mastered our core specialism as outlined in our product competencies e.g. product execution, customer insights, product strategy and influencing.
C. Data
We want Product Managers to set metrics, analyse data, present data, understand data regulations and use data analytic tools.
D. AI
Our goal is to help our Product Managers develop AI-focused products and use AI tools to boost their productivity.
We want them to experiment and learn how to use AI to solve problems in ‘mid-market’ organisations. They should understand topics like different models, regulations, safety and ethics, data quality, Retrieval-Augmented Generation (RAG), multi-modal agents, and prompt engineering.
We want our Product Managers to know how to use large language models (LLMs) and AI tools to create important product documents. We want AI to assist in completing tasks so that we can concentrate on strategic discussions e.g. growing accounts, finding new opportunities, and delivering results.
E. Commercial
We want our Product Managers to understand how to negotiate, know the difference between pricing models, budget management and bid writing.
We will not focus on:
Our Product Managers will not become legal experts in data and AI law. We do want them to have an awareness of key aspects of the law to inform product development. A lawyer must be consulted when engaging and developing products in this space.
Commentary: being explicit about what you will and won’t do, can help you prioritise.
Accountability
It’s important to remember that it’s not just one team responsible for our KPIs and measures of success. Many teams work together (sales, operations, delivery, technology), each contributing to our shared goals and overall performance. This teamwork shows how crucial collaboration and communication are in reaching our objectives.
Our most important metrics
These are metrics every team in the Club are contributing towards. We will influence these metrics by delivering against the strategy.
| Description | Baseline | Target |
|---|---|---|
| Utilisation rate | 70% | 90% |
| Revenue | £500k | £700k |
| Client satisfaction scores | 3.5/5 | 4.5/5 |
| Quarterly assessment of individual development progress using the competency framework | Product competency survey completed in August 2024 | 1 level up every 18 months |
| Retention rates | 80% over the last year | 95% |
Commentary: use metrics to determine whether you are suceeding in delivering against your strategy. Make this visible, and don’t be afraid to course correct if you are not meeting them.
Conclusion
I hope you find this real-life example interesting. I made careful choices while writing this strategy and tried to keep it as simple as possible. Remember your audience; not everyone will read a document this long. It might be worth sharing it as a one-page summary. For the next steps, I suggest gathering important feedback from stakeholders to refine your strategy. Then, begin crafting a roadmap and start executing your strategy. Remember, the key to a strategy is its delivery.
