There has been ongoing discussion as to whether the AI hype has finally hit its peak. For years, it's been shouted as the next big thing, to revolutionise everything from healthcare to finance. Does AI carry opportunity to help us handle routine work and generate ideas? Yes it does, but expectations and fears have both got somewhat out of hand.
We've seen some pretty crazy uses of AI that seem to be more about marketing buzz than real utility. There's the AI informed dog collar and bowl that optimises your dog's meal sizes with their activity levels, to the AI toothbrush to coach you on better brushing, and even AI-generated whiskey. There goes the craftsmanship, and there are still unresolved issues about data sovereignty and confidentiality, and the data used to train the Large Language Models (LLMs).
If you've got a great startup idea, there's no need to rush out and slap an ‘AI’ label on everything just because it's trendy. The heart of a minimum viable business is delivering value to early customers, not trying to impress them with the latest tech buzzwords. Instead of getting caught up in the hype, focus on what you're already doing well. Your customers will appreciate a genuine, high-quality product or service more than a gimmicky feature.
1. Background to AI
But AI is driving growth opportunities for startups, it has become democratised and accessible, but it's not as new a thing as many think. Back in the summer of 1956 a small group gathered at Dartmouth College in New Hampshire, including Claude Shannon, the father of information theory, and Herb Simon, the only person to be awarded both the Nobel Prize in Economics and the Turing Award. They were brought together by researcher, John McCarthy, to discuss how to make machines use language, form abstractions and solve problems now reserved for humans.
It was the first gathering devoted to what McCarthy dubbed artificial intelligence, following work led by Alan Turing and John von Neumann. It was from these pockets of curiosity and perseverance that today’s boom was born. As the rudiments of the way in which brain cells (neurons) work were pieced together, computer scientists began to wonder if machines could be wired up the same way.
A first attempt to model this was by Marvin Minsky. By the early 1990s neural networks had been trained to do things like help sort the post by recognising handwritten numbers. Its potential was dramatically demonstrated in 2009, when researchers at Stanford increased the speed at which a neural net could run 70-fold, using a gaming pc in their dorm room.
The power of this new approach, known as deep learning, became apparent. Image-recognition systems would be trained to map input in the form of images onto output in the form of one-word descriptions. In 2012 a team led by Geoff Hinton used deep learning to achieve an accuracy of 85% on unseen test images.
Deep learning was also being applied to other problems such as speech recognition (mapping sound to text), face-recognition (mapping faces to names) and translation. In all these applications the more training data they were given, the more their performance improved – the concept of machine learning was born.
Deep learning was deployed in voice-driven devices such as Amazon’s Alexa. The technique evolved to self-supervised learning, and the model teaches itself to complete the analysis.
LLMs attracted attention in 2019 when a model called GPT-2 was released by OpenAI, capable of ‘emergent’ behaviour which they had not been explicitly trained. In November 2022 a larger OpenAI model, GPT-3.5, was presented in the form of a chatbot. Anyone with a web browser could enter a prompt and get a response. No consumer product has ever taken off quicker. AI had made another great leap forward.
Today, where the first cohort of ai-powered products was based on recognition, this second one is based on generation. ChatGPT and rivals such as Gemini (from Google) and Claude (from Anthropic) produce outputs from calculations. They really do seem to use language and form abstractions, just as McCarthy had hoped.
Models are essentially grown, not explicitly programmed, nobody is entirely sure why they have such extraordinary abilities. Nor do they know why they sometimes misbehave, give wrong or made-up answers, known as ‘hallucinations.
2. Opportunities for applying AI to your startup
Trying to maintain a smidgeon of understanding with the pace of AI development is tough. It is a daunting task, but from creating content, optimising operations, handling finances to managing client relationships, AI tools will help startups accelerate growth. Here are some categories and example of AI tools a startup looking to stretch its budget can adopt.
2.1 Video creation and editing
These AI tools are useful for creating product pages on a website, building pitch decks and crafting social media posts. Adobe, Canva and Pixir all have AI features in their respective tools. Magic Eraser is an editing tool to clean up a cluttered image, whilst Pic Craft AI allows you to change backgrounds.
Vidyo AI saves you time by automatically detecting the most interesting moments in a long video, then clipping the video down to bite-size pieces that are already optimised for social sharing. Fliki can help turn anything ranging from a written idea prompt, a blog post, a product page, or even a social media post into a video with just a few steps. Leonardo AI is a powerful tool that gives you multiple AI models to choose from for creative thinking.
2.2 Copywriting AI tools
Maybe you want to send out a press release about your latest product, or you need help with creative copy for your social media posts. You don’t have to be the next Hemingway to write copy that will grip readers. You may just need a savvy AI tool for copywriting.
ChatGPT can do anything from giving you content ideas to writing blog posts for you. Many other AI tools utilise ChatGPT technology in their software. Jasper offers content creation assistance, in addition to project management and analytic services and an extension for Google Chrome means you can tap Jasper whenever you need it. Jasper also doubles as an image creation tool. Merlin and Browse AI are two additional copywriting tools. I like Gemini (formerly Bard).
The HubSpot AI Content Writer is a useful content tool which reviews your writing, then offers suggestions to rewrite, change the tone, expand, or summarise. Paired with HubSpot’s Blog Ideas Generator, startups can quickly develop unique and relevant content. You’re not limited to social and blog content, either. The HubSpot AI content writer can also sharpen your email marketing copy or design a landing page.
2.3 Financial AI tools
Financial AI tools can help startups get a better handle on their finances. Zeni can handle much of your startup’s financial needs: tracking expenses, reviewing income , analysing cash burn rate, and obtaining insights on your financial habits. Glean.ai uses AI to make accounts payable easier and more efficient. This tool handles data entry, approves invoices, manages contracts and payments etc.
2.4 Customer relationships and service AI tools
Maintaining good customer relationships and customer service is vital for startups, but when you’re stretched making new business sales calls, outbound LinkedIn contacts and attending marketing events, it’s tough. Chatbots can quickly answer customer inquiries, CRM software can store customer data, both making it easier to foster strong customer relationships. In addition, here are some AI tools to consider.
ChatSpot.ai tool integrates with HubSpot’s CRM to provide personalised email messages. The AI chatbot from HubSpot offers a powerful tool that quickly responds to users while still offering a personalised, human-like conversation.
There are many chatbots and bot-building tools, but Drift stands out with some key features like personalised responses to help move clients down the funnel, AI-generated or live video responses to connect promising leads. Drift integrates with HubSpot, Google, and Slack.
Zigpoll is an AI survey tool that develops personalised insights from customer satisfaction data. You can customise polls and surveys, and offer them via website, email, or text messaging. AI analyses responses and offers tailored feedback for how to address key points, even tips how to increase revenue or conversions.
2.5 Operating efficiencies
Startups take a lot of time and resources to manage. With AI tools, founders can reduce the time spent on admin with AI handling many housekeeping tasks. AI tools like Fireflies and Otter.AI can transcribe and summarise meetings, turning discussions into concise, actionable notes, ensuring no critical point is missed.
AI tools like usemotion can automate calendar management, scheduling work blocks to enhance productivity based on tasks and priorities, whilst Clockwise’s synchronises calendars, schedules meetings, and organises your daily and weekly time blocks.
3. Challenges facing startups when adopting AI technologies
The above brief list of the AI tools I use with my startup portfolio showcases the possibilities. However, there are some challenges to consider ahead of its adoption.
Investment: AI deployment requires financial commitment, a further demand on startups' limited budgets. Acquiring the necessary hardware, software, and talent can be a hurdle.
Data Scarcity: AI models rely on a volume and quality of data to function effectively. Startups may struggle to access or generate sufficient data, hindering the performance of their AI systems.
Skills Gap: Implementing and managing demands skills and expertise. Startups will face challenges in attracting and retaining talent with proficiency in data science, machine learning, and AI.
Integration: Integrating AI systems with existing infrastructure and workflows can be complex and time-consuming.
Ethical & Regulatory Concerns: AI deployment raises ethical considerations, such as potential biases in algorithms and concerns about data privacy. Startups need to navigate regulatory landscapes and ensure compliance, which can be challenging without expertise.
Lack of Awareness and Understanding: The rapid evolution of AI tech can create a knowledge gap, particularly those in non-technical industries. Misconceptions and a lack of awareness about the potential benefits and limitations of AI can hinder adoption.
Scalability Challenges: Scaling AI applications to meet growing demands and handle increasing volumes of data can be complex and costly. Startups need to anticipate future needs and invest in scalable solutions from the outset.
Security and Data Privacy Risks: AI systems can be vulnerable to cyberattacks and data breaches, posing risks to reputation and customers. Ensuring robust security measures and data protection protocols is crucial.
Overcoming these obstacles allows startups to leverage AI's transformative potential and potentially gain a competitive advantage in the marketplace.
Summary
To harness AI's full potential, startups need to focus on AI's ability to enhance decision-making and operational efficiencies. AI's capability to automate tasks and generate insights can lead to significant cost savings, providing startups with a leaner, more agile operational model.
The low-cost, high-speed experimentation enabled by AI also allows startups to test and refine their products and marketing strategies more effectively. There is an exciting future for AI-driven entrepreneurship.
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