Generative AI for small and mid-sized enterprises

Explore the untapped potential of Generative AI beyond marketing and customer service, and learn how to navigate challenges and harness AI for a smarter, more efficient future.

Mid-sized enterprise again are the biggest beneficiaries of a global technological revolution. Just as the explosion of cloud-based software-as-a-service levelled the playing field, so too does generative AI. Nearly every person on the planet can take advantage of this incredible technology, with no apparent advantage for large enterprises.

Generative AI, for the purpose of this article, will refer to tools and technologies that generate content based on an input provided to them. These technologies are most familiar in their applications: ChatGPT, DALL-E, Bard, Claude, Midjourney, and more emerging every day. While many may conflate this technology with artificial intelligence in general, it should be said that generative AI is a subset of the wider field of AI.

Capturing worldwide attention since November 2022 when ChatGPT was launched, generative AI introduced the world to something that was, for certain cases, indistinguishable from humans. There have been huge advances in generative AI capabilities since then, and nearly every SaaS provider has found a way to bake-in generative AI into their products, enabling greater productivity and more value for their end users.

The fact that mid-sized enterprises are able to buy the same version of software as the world's biggest enterprises means that they on a level playing field. Just like how the world's richest person cannot buy a better iPhone than anyone else, these technologies are available to nearly everyone on the planet. The nature of generative AI and the simple interface that it affords end users, using a simple prompt in plain language, means that harnessing this technology no longer requires substantial resources that are typically only affordable by large organisations. In fact, it is the large organisations that are handicapped by their size and vast amounts of data.

This means that mid-sized enterprises have a nimbleness and speed to market advantage that large organisations just cannot match. The opportunity for disruption is greater than ever as smaller organisations can deliver value faster and more finely tuned to customer needs. This article explores how this technology works, how it can be applied, and where it is likely to go in the future.

Why generative AI matters for mid-sized organisations

In an increasingly digital landscape, staying ahead of technological trends is not a luxury—it's a necessity. For mid-sized organisations, especially those in the Not-for-Profit sector, leveraging advanced technologies like Generative AI can be a game-changer. Here's why:

  1. Competitive advantage - Early adoption of generative AI positions your organisation ahead of the curve. It offers innovative solutions for customer engagement, data analysis, and decision-making processes, providing a distinct edge over competitors who are late adopters.
  2. Efficiency and cost-savings - Automating tasks such as data collection, customer service via chatbots, and routine communications can significantly reduce operational costs. generative AI can handle these tasks 24/7, improving efficiency without the need for additional human resources.
  3. Agility and adaptability - The modern business environment is ever-changing. Generative AI offers the flexibility to quickly adapt to new market trends and customer behaviours. Generate AI lowers the barriers to advanced data analytics.
  4. Innovation and growth - Generative AI doesn't just automate tasks; it can generate new ideas and insights, propelling your organisation into uncharted territories. Whether it's identifying new revenue streams, suggesting novel marketing approaches, or forecasting emerging trends, AI becomes a catalyst for innovation and sustainable growth.
  5. Strategic depth - For leaders and decision-makers, generative AI can augment strategic foresight and futures thinking. It can simulate various future scenarios based on current data, aiding in risk assessment and long-term planning. This can be particularly beneficial for not-for-profit organisations that need to allocate resources judiciously and strategize for sustainable impact.
  6. Data-driven decision making - Generative AI transforms raw data into actionable insights. It takes guesswork out of the equation, enabling executives to make informed decisions based on empirical evidence. This is crucial for achieving targeted outcomes and ensuring the effectiveness of strategic initiatives.

Generative AI offers a multifaceted toolkit for mid-sized organisations to not only survive but thrive in today's complex business environment. With the right implementation, it can drive competitive advantage, efficiency, and innovation, making it an essential asset for any forward-thinking organisation.

The first use cases: marketing and customer service

Generative AI has found significant traction in the fields of marketing and customer service. This is not by chance; the technology's ability to automate conversations and produce custom content is invaluable for these sectors. In marketing, AI-driven algorithms can generate personalised email campaigns, while in customer service, chatbots can handle routine queries, freeing up human resources for more complex issues.

Let's have a look at some popular applications and how generative AI is impacting them:

  • Email marketing: Generative AI tools analyse customer behaviour and generate personalized email content, improving open and click-through rates.
  • Customer support: AI chatbots can handle FAQs and basic customer issues, directing more complex queries to human operators.
  • Content creation: Some AI systems can produce basic blog posts, social media updates, and even video scripts, although the quality varies.
  • Personalised recommendations: AI algorithms can predict what products or services a customer might be interested in, based on past behaviour and other data points.

While the technology has proven beneficial, it's not a one-size-fits-all solution. The quality of content generation varies, and chatbots can sometimes provide incorrect or unhelpful answers. Moreover, the system is only as good as the data it's trained on; poor data quality can lead to less effective outcomes.

The game changer: The potential for generative AI in strategy

For mid-sized organisations, particularly NFPs in Australia, leveraging Generative AI in analysis, decision-making, and strategic planning can offer untapped advantages, and are often overlooked as potential use case for this technology.

Why these areas matter for not-for-profit organisations

Analysis, decision-making, and strategic planning are core functions of any business. For NFP organisations, these functions are often the backbone of their operations, dictating their ability to serve their communities effectively. Efficient data analysis allows for more targeted initiatives, while robust decision-making frameworks and strategic foresight can have a direct impact on long-term success.

Unlocking new opportunities

  1. Data analysis and forecasting: Generative AI can sift through large data sets to identify patterns, predict future trends, and even offer actionable insights. For NFP organisations, this could mean predicting donation trends, volunteer availability, or community needs.
  2. Decision support systems: AI algorithms can analyse numerous variables to recommend the best course of action in complex scenarios. This can be particularly useful for allocating resources effectively or responding to crisis situations.
  3. Strategic scenario planning: With generative AI, organisations can model multiple future scenarios, exploring the outcomes of various strategic choices. This enhances the ability to anticipate change and make informed decisions.

Case study: Can GenAI do strategy?

From the article: Can GenAI Do Strategy? (


This article presents a classroom experiment that compared a strategy developed by a team of MBA students in the traditional way with one developed using a virtual AI assistant that was linked to a tried-and-tested strategy toolkit.


The results of the two independent processes were largely similar, with the AI strategy being more original. The difference? The students took a week and the AI just 60 minutes.

Practical examples of using generative AI for strategy

Generative AI has utility that extends beyond customer service and marketing. Below are some practical applications specifically geared towards analysis, decision-making, and strategic planning.

Data analysis

How It Works: Generative AI and advanced chat interfaces can quickly and easily analyse data without requiring complex analytics tools, extensive training or a specialised team. Executives and decision makers can quickly gain insights from data.

Practical Steps:

  1. Identify analysis goal: Be clear what objective you have in your data analysis - what question are you trying to ask.
  2. Data Preparation: Acquire the data and ensure that it is clean, structured, and stored securely. Note that some generative AI providers retain data for model training purposes, so if your data is confidential, ensure that you are using a generative AI which will not store data for training or has a privacy policy that meets your needs.
  3. AI Integration: Use the tool to analyse the data. Several tools offer "Advanced Data Analysis" or equivalent features which will allow you to upload the datafile, and then will develop approaches to understand and analyse the data. You should be prepared to help the AI clarify questions about the data.
  4. Run and Analyse: Ask the AI to answer your goal question, or ask the AI to present your data in different ways. Several platforms support multi-modal answers which means that they can plot your data in graphs, tables or other structures to help you understand the analysis.

Decision support

How It Works: Generative AI can be employed to create decision support systems that assist executives in making informed decisions based on real-time data and analytics.

Practical Steps:

  1. Define decision parameters: Identify the decision and the different parameters that will influence the outcome.
  2. Gather background information: Gather the relevant background information to share with the AI, such as any historical events, facts or other information which will help provide more context for the problem.
  3. Construct the prompt: Tell the AI you wish to make a decision, and for it to ask you what information it needs. The AI will ask you a number of questions to gather the background information and the suggest ways to work through the problem.
  4. Chat and decide: Work with the AI to understand the implications of each possible decision and then based on these insights make a more informed decision.

Strategic scenario planning

How It Works: Generative AI can simulate multiple scenarios based on varying factors and conditions, offering a broader view of potential future states. This is extremely beneficial in strategic planning.

Practical Steps:

  1. Gather key trends and driving forces: Research for trends and driving forces that are affecting your organisation. Identify the impactful of these. Tip - you can also ask the AI for its opinion on these trends and driving forces, however, do note that most AIs do not have up to date information in their base model, so look for AI assistants which have a web-search feature to gather more recent information.
  2. Determine possible scenarios: Using the identified trends, create a set of scenarios where the trends are interacting with one another. There are several techniques to working through what these scenarios look like. The most popular is the 2x2 method where you take the two most impactful trends and align them to each axis of a high/low, more/less, big/small, etc., 2x2 matrix. In each quadrant you then have the extremes both trends.
  3. Generate the scenarios: For each scenario, ask the AI to generate a narrative story about the day in the life of a person living in that future. You can include other trends at this time if there are specific things you think would be useful to consider.
  4. Analyse and Plan: Evaluate your current strategies against each scenario, and write down how each strategy performs and any implications of that scenario for your organisation in that future. Looking across all scenarios, see how you can adjust your strategies to be more robust to work in more of the preferrable futures that you have identified.

I'm working on an innovative approach to strategy design and development.

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Challenges and considerations for implementation

Implementing generative AI in your organisation comes with its own set of challenges that shouldn't be overlooked. These are some key points to consider:

Data privacy & security

One of the foremost concerns when implementing any AI technology is data privacy. Organisations, especially not for profit ones, often handle sensitive information. Using generative AI solutions that require access to this data could potentially risk privacy breaches. Ensure compliance with privacy laws, such as the Australian Privacy Principles, when adopting any AI solution.

There is also some guidance from the Australian Cyber Security Centre on deploying AI systems securely.

Data quality

Poor data quality can significantly hinder the effectiveness of a generative AI system. Inaccurate or incomplete data sets can lead to unreliable results, affecting your strategic decisions. It's crucial to invest time and resources in data cleansing and validation before deploying AI applications.

Intellectual property concerns

The algorithms used in generative AI might generate content or insights that could be deemed proprietary. There's an ongoing debate about the ownership of such intellectual output. Consult your legal team to establish clear guidelines around the ownership and use of any generated material.

Integration into existing systems

Generative AI solutions don't exist in a vacuum. They need to be integrated with your existing software systems, which could include Customer Relationship Management (CRM) platforms, Enterprise Resource Planning (ERP) systems, or other data analytics tools. Consider the compatibility and integration costs when planning the implementation.

Ethical considerations

Ethics in AI is a rapidly evolving field. Be mindful of unintended consequences such as algorithmic bias, which could potentially perpetuate existing inequalities. It's crucial to have an ethical framework for the use of generative AI, ideally one that is in alignment with your organisation's values and mission. Consider reviewing this consultation paper produce by the Australian Government.

Skill gap and learning curve

The field of generative AI is new and rapidly evolving. Your current team may not have the skills needed to manage and maintain AI systems effectively. Consider the costs and time involved in training your team or hiring new talent specialised in this area.

Addressing these challenges requires a comprehensive approach that involves multiple stakeholders, including your data team, legal advisors, and ethical committees. Prioritisation of these concerns will create conditions that make adoption of generative AI easier, safer and more effective for your organisation.

Why you need to jump onboard

The unique challenges faced by Not for Profit (NFP) organisations in Australia, such as limited resources and high operational costs, make the case for adopting generative AI even more compelling. Below are some of the ways generative AI can add strategic value to NFP organisations:

Cost efficiency

Generative AI can automate routine tasks such as data analysis, report generation, and customer interactions. This automation allows for a more efficient allocation of human resources, potentially reducing operational costs.

Improved decision-making

Data-driven insights are vital for strategic planning in NFP organisations. Generative AI can process large volumes of data to identify patterns and trends, offering actionable insights that can guide decision-making processes.

Enhanced stakeholder engagement

Tailoring communications to stakeholders is often a resource-intensive activity for NFPs. Generative AI can personalise newsletters, donation requests, and other communication materials, thereby increasing engagement rates without additional manpower.

Operational resilience

The ability to adapt quickly to changing circumstances is critical for NFPs. Generative AI supports rapid scenario planning and forecasting, enabling organisations to pivot their strategies swiftly in response to emerging challenges or opportunities.

Ethical and social considerations

Generative AI models can be designed to align with the ethical values and social missions of NFP organisations. This ensures that technological solutions do not compromise the integrity of the cause but rather enhance it.

Generative AI can offer NFP organisations in Australia a competitive edge in a challenging environment. The technology not only addresses immediate operational concerns but also opens up new avenues for strategic planning and stakeholder engagement. Adopting Generative AI is a strategic imperative for NFP organisations aiming to stay ahead of the curve.

What next?

In today's rapidly evolving technological landscape, the application of generative AI is a necessity for competitive edge. While current trends heavily focus on marketing and customer service, there's untapped potential in the realms of data analysis, decision-making, and strategic foresight.

Mid-sized and NFP organisations stand to gain significantly by incorporating generative AI into their strategic planning. It offers the dual advantage of efficiency and precision, helping you stay ahead in an increasingly unpredictable market environment. Furthermore, it addresses specific challenges, such as budget constraints and operational efficiency, which are particularly relevant for NFP organisations.

The challenges of implementing generative AI—privacy concerns, data quality, and intellectual property—while substantial, are navigable. Proper consultation and planning can mitigate these hurdles, setting your organisation on a path to a smarter and more efficient future.

If you are an executive or senior leader looking to explore how generative AI can benefit your organisation, don't hesitate to reach out for a consultation. My expertise in strategic foresight and futures thinking can offer you tailored solutions that align with your organisational goals.

For a deeper dive into how Generative AI can serve your specific needs, book a consultation.

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Not sure where to start? We can help. Get in touch to discover how quickly and easily we can help you take advantage of generative AI.
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Frequently asked questions (FAQ)

What is generative AI?

Generative AI refers to artificial intelligence models designed to generate new data that resembles a given dataset. Common applications include text, image, and video generation.

How is generative AI different from other types of AI?

Most traditional AI models are "discriminative," designed to categorise or make predictions based on existing data. Generative AI, on the other hand, creates new data, allowing for a range of applications from automated content generation to scenario planning.

Why should mid-sized organisations consider generative AI?

For mid-sized organisations, Generative AI can offer a competitive edge through personalised marketing, automated customer service, and enhanced data analysis capabilities. It can streamline operations and contribute to informed strategic decisions.

What are the applications of generative AI in not-for-profit organisations?

Generative AI can assist NFP organizations in grant writing, donor engagement, and scenario planning. It can also automate repetitive tasks, allowing staff to focus on mission-critical activities.

What are the privacy concerns around using generative AI?

Generative AI models often require large datasets for training. Organisations need to ensure that the data used complies with privacy regulations and that generated content does not compromise sensitive information.

How can we mitigate the risks of data quality and loss of IP?

Quality control mechanisms can be integrated into Generative AI systems to maintain data integrity. To prevent loss of intellectual property, only authorised personnel should have access to the AI models and generated data.

Is generative AI difficult to implement?

Implementation complexity varies depending on the application. Basic chatbots may require minimal setup, while more complex systems for data analysis could necessitate specialised expertise. It's advisable to consult professionals for a tailored solution.

Is generative AI expensive?

The cost of Generative AI solutions varies widely, based on complexity and the required customisation. However, the long-term operational efficiencies often justify the initial investment.

How do we start using generative AI in our organisation?

The first step is to identify the specific needs and objectives you aim to address with Generative AI. From there, consider engaging a consulting service specialised in strategic foresight and futures thinking to guide your implementation process.


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Chris Dury
Strategic Foresight Consultant

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