When I started to see more LinkedIn posts from Steve Nouri than Steven Bartlett, I knew March 2023 was January 1848. That was when gold was found by James Marshall at Sutters Mill in Coloma, and the California ‘Gold Rush’ started. Then Bill Gates published a seven-page letter about AI and his predictions for its future, before Elon waded in urging us to press pause for the good of humanity. Elon. Used to be a thought leader and innovator I admired. I wanted flying cars, but he’s given us blue ticks. He’s so far up himself he’s turned inside out.
AI has been making huge waves recently, ChatGPT and Generative AI are dominating tech newsfeeds with loads of truly astounding new stuff popping up. OpenAI launched ChatGPT and Whisper APIs and then dropped the new GPT-4. Google, Microsoft, and Apple are on the AI bandwagon, adding it to their products like Google Workspace, Microsoft 365, and Siri. Also last week, Twitter open sourced its recommendation algorithm. What a week in tech!
Meta’s LLaMA (Large Language Model Meta AI), a variety of model sizes trained up to 65bn parameters nearly got open sourced thanks to what is arguably the pull-request of the year from a chap called Chris King cheekily submitting changes to GitHub (thanks to Tom Stockton for sharing this news). Meanwhile Italy became the first Western country to block ChatGPT, the data-protection authority said there were privacy concerns relating to the model. However, Reddit ran a story that the real reason it’s banned was because Chat GPT confirmed it was ok to put ketchup on pizza.
When did it all start? Well, in summer 1956, John McCarthy, a young maths professor at Dartmouth College, organised a group of computer and cognitive scientists to develop ideas about thinking machines and called the subject ‘artificial intelligence’. It was a pivotal moment. Attendees included Nathaniel Rochester who designed the IBM701, the first computer made by IBM, Arthur Samuel who coined the term ‘machine learning’ and Marvin Minsky who won the Turing Award for his role in shaping and advancing the field of artificial intelligence. Not there was Alan Turing, regarded as the founder of AI, with the annual ‘Turing Award’ acknowledged as the Nobel Prize of Computing.
Currently, Briton Geoffrey Hinton is regarded ‘godfather of artificial intelligence’, winning the Turing ward in 2018. His seminal work on neural networks forms the foundation of machine learning models today, and he regards AI to be in a ‘pivotal moment’, with the advent of artificial general intelligence (AGI) looming closer than we’d think – the term that describes a potential AI that could exhibit human levels of intelligence. AGI would be capable of learning and thinking on its own.
Regardless of the industry bluster hailing its arrival or how long it might really be before AGI dawns on us, Hinton says we should be carefully considering its consequences now — which may include the minor issue of it trying to wipe out humanity: It’s not inconceivable, that’s all I’ll say is his rather chilling comment.
Luckily, by Hinton’s outlook, humanity still has a little bit of breathing room before things get completely out of hand, since current publicly available models are mercifully stupid: We’re at this transition point now where ChatGPT is this kind of idiot savant, and it also doesn’t really understand about truth, because it’s trying to reconcile the differing and opposing opinions in its training data. It’s very different from a person who tries to have a consistent worldview. But Hinton predicts that we’re going to move towards systems that can understand different world views, which is spooky, because it inevitably means whoever is wielding the AI could use it push a worldview of their own.
But let’s set aside the social, moral, and longer-term impact of AI. What does the current state of play in AI mean for startups? I see it as a moment of significant opportunity, not an existential threat. Not long ago, artificial intelligence was considered a niche that only large companies could afford, conducting expensive private research and tapping into its expansive potential. Startups couldn’t imagine gaining access to that kind of technology. Cut to the present, it has become much more accessible, with plenty of boxed AI software products and services available. The rise of low- and no-code platforms is another contributing factor, as these low-cost applications have fallen within reach of smaller businesses.
Let’s focus on four areas where AI can enable faster, more efficient growth for startups, and then how to implement these solutions.
AI drivers of growth for startups
1. Business Model Design – create more value for customers
At its core, the AI business model works as a growth driver, helping with the configuration of your business model and improving decision making on both internal and external strategic issues. However, it is imperative to know ‘what’ and ‘how’ AI can provide you an opportunity.
Through AI, you better understand the cause-and-effect of various elements in your business model and have a better shot at creating value. However, you have to be specific about your functions to improve customer journeys and better communicate your brand identity. Value creation boils down to making the right business choices, and AI helps speed up the process of creating, testing and deploying new business models.
Once your business strategy is informed, you can better spot product-market fit. For instance, it could be an opportunity to lower or raise prices that might change customer attraction and retention. You can use AI algorithms to find the trends that attract the most customers, and the type of customers.
The key is to be specific about your business model and ask questions to eliminate inconsistencies. Focus on specific ideas using AI to determine whether “A” or “B” is a better choice.
The success of your AI driven business plan depends a lot on how accurately you paint the picture of your customers. Create extensive customer profiles to understand their preferences and tastes better and determine whether they’ll use your new product or service.
2. Marketing & Sales
Perhaps the best use of deploying AI solutions for startups is automating and improving marketing and sales functions. In addition to predictive analytics, solutions use NLP applications including concepts such as text classification, sentiment analysis, and recommendation algorithms to decode how your startup can best communicate and connect with your customer base.
AI can help with market research and competitor analysis, facilitate the creation of marketing assets and push them to a broader but focused audience. AI allows you to automate email marketing and social media campaigns, whilst tailoring marketing messages to specific individuals rather than general audiences. Intelligent recommendation engines also effectively generate leads, using AI and machine learning to evaluate your target market’s behaviours and then suggest results with a higher probability of lead generation and conversion.
AI can automate the selling process in different ways. Using chatbots and algorithms to enhance the online selling process has been a major trend. Conversational AI is capable of handling sales at the same capacity as the best salespeople. Such solutions offer an unprecedented level of customisation, self-improvement, and cost-effectiveness that make them accessible to startups. As sales are converted and your startup grows, AI will adapt and continue to take care of the increased traffic.
3. Customer Relationships
For startups, customer retention and returning customers are vital, both uphill tasks no matter how good your offerings are. When you don’t have dedicated resources to deploy to manage customer relations, conversational AI chatbots come to the fore. AI can also analyse customer calls and suggest improvements. Since the inception of machine learning, engineers have been striving to make computers capable of carrying out human-like conversations.
Chatbot digital assistants can be integrated with your online platform, acting as virtual support agents that can guide web visitors through queries of varying levels. The earliest rule-based chatbots were pre-programmed with a fixed dataset of questions and corresponding answers. Users could only choose from a given list of options, and the chatbot would answer with pre-written texts.
Now AI chatbots are much more sensitive, capable of recognising and understanding text-based human languages. These chatbots are advanced to meaningfully resolve customer issues without any human assistance. They make your business infinitely more accessible at a much lower cost, and also make your startup more approachable, since many customers find it more convenient to get answers immediately, rather than waiting on hold for a human assistant. So consider these interactive intelligent voice-response agents at scale to deliver a unique customer experience at scale.
4. Hiring & Retaining Talent
A significant challenge for startups is hiring talent with the required skills yet there’s often not enough bandwidth to spend on attracting talent. AI can complement the person looking after hiring by offloading the time-consuming overhead elements such as evaluating CVs and automating interview scheduling, whilst also improving the candidate experience. The AI driven hiring process is more efficient and effective than the traditional cycle, reducing time and cost of hiring. The AI driven testing process is intuitive, and has become smarter and more accurate, helping businesses hire the best-fit employees.
Implementing AI in a startup
It’s one thing understanding the benefits of AI, but what about implementation? How can startups start using AI? Here are a few things to consider when considering an AI implementation.
Define your goals Before you start using AI, you need to know what you want to achieve. Do you want to use AI to automate tasks, improve your products, or make better decisions? Once you know your goals, you can start looking for AI solutions that can help you achieve them.
Identify use cases. After understanding your goals, identify the specific use cases for your startup. If you narrowed down your options to a few marketing AI tools, for example, check how they align with your marketing strategy, and plan accordingly.
Do your research Before you start buying solutions, do your research and understand the tech. It’s important to choose the right one for your project. You should also be aware of the potential risks and limitations of AI. Consider your budget, and your degree of tech competence to apply AI.
Train your team Adopting AI means your team are trained on how to use it to get the potential benefits. Success won’t happen overnight with adopting AI, there is a learning process for the tech that is driven the learning of your folks.
Start small and test It’s usually best to start small when you’re first getting started with any new tech, don’t bite off more than you can chew. Start with a simple project that you can complete quickly, test rigorously, and then gradually increase the complexity of your projects as you become more comfortable with AI. You should also have a plan for how you’ll handle unexpected results.
Implement AI gradually Having achieved some early, quick wins, then don’t try to implement AI across the business all at once. As above, start small, test, iterate then build out. You need to give your folks time to get used to using AI and integrating it into their workflows and also the culture. It’s also important to track and monitor feedback from customers.
Assess value AI solutions should never be left on autopilot. Periodically review the tools you employ to gauge benefit and cost. If the tools are not making sense, experiment until you find the right balance. Monitor and evaluate performance.
AI is undoubtedly coming of age, startups should look at the open window of opportunity. By understanding how your use cases could make the most sense for your venture, you can find the balance of human intelligence and artificial intelligence necessary to succeed.
AI does not perform well on its own. While AI solutions are great at automating repetitive tasks and driving growth, they still need a personalised touch, performance peaks when machines and humans work in tandem. If you want to make the most out of artificial intelligence integrated into your startup, consider how you can develop multi-faceted AI capabilities rather than entirely replace human functions. Understand that it takes willingness and broadmindedness to embrace new technology with existing people and processes, and tie them together with your business model.