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, Gemini, Co-pilot, 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 dynamic and disruptive business landscape, many organisations turn to technology to help stay ahead. For mid-sized organisations, especially those in the Not-for-Profit sector, technologies like Generative AI can provide significant returns on investment, enabling more focus on their mission and purpose.
Here's how generative AI can help:
- 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.
- 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.
- 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.
- Innovation and growth - Generative AI doesn't just automate tasks - it can generate new ideas and insights, and is great at bringing together seemingly unrelated concepts. Whether it's identifying new revenue streams, suggesting novel marketing approaches, or forecasting emerging trends, AI becomes a catalyst for innovation and sustainable growth.
- 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 carefully and plan for sustainable impact.
- 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 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 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.
