The best startup founders dare to innovate. They have a keen eye for rising technologies and their potential to not only increase the value proposition of their business but also to improve the lives of their customers. You already know by now that artificial intelligence is one of these critical technologies and that it can be a major boon to your business. The next step is to find ways to incorporate AI to boost your startup’s success. Let’s explore some proven, cost-effective, and efficient methods of applying AI to your business. Pair AI With Real Business Needs With such high pressures for startup owners to keep up with evolving trends in technology, the fear of missing out can be intense. Business owners often scramble to implement technology without thinking carefully about what needs they are addressing. In this context, this results in businesses incorporating AI for AI’s sake. As a result, these businesses never truly realize the full potential of the technology to benefit their brand. While they might make short-term gains by introducing AI, they lose the long game by not addressing specific goals and market needs. Some questions startups should ask themselves before incorporating AI into their business are: How can AI help us with our everyday operations? Can AI be used to help our target audience solve their problems? How exactly will AI increase our value proposition? The only way forward is to understand the business problem that you want to solve using AI and set clear and realistic goals. Only then will you be able to understand the kind of investment that introducing AI to your startup will require, and your development team will be able to find the most efficient tech approach to achieve your goals. Turning to AI consulting is a way to solve this problem. Successful startups enlist the help of experts who have technical and business experience in implementing AI projects and can find the best solution at the intersection of market needs, business goals, and technical capabilities. Use Third-Party Services and Pre-Trained Models You may think that for AI to work at its best, it requires custom models. And yes, for some applications, new models must be trained to accomplish certain tasks. This applies to unique cases where certain uncommon problems are solved. Developing custom models also involves unique data. But for many tasks, a pre-trained model can perform just fine. There is a much lower cost involved with pre-trained models depending on your use case. Experienced startup owners see the value in pre-trained models and know how to get creative and think of ways to use these models before they resort to creating something entirely new. There are two types of pre-trained AI models that can be useful to startups: standard pre-trained models and foundation models. Standard Pre-Trained Models As the saying goes, we shouldn’t try to reinvent the wheel. The same goes for AI training models. If someone before you came up with an efficient method for AI to do object detection, there’s no need to recreate that model. You can just adjust it with your data. Pretrained models can also be fine-tuned to the desired output for the business, so they can be adapted to your specific use case if necessary. This is best done by data scientists who can understand how the model works and how to fine-tune it for the best results. Foundation Models These newer AI models utilize enormous datasets for training. is one of these foundation models, and as you’ve likely seen, despite being a pre-trained model, it can adapt to a wide range of downstream tasks with little fine-tuning. ChatGPT Foundation models aren’t just about text. There are models designed to work with sound, images, and even video. As foundation models grow, access to high-quality AI implementations becomes easier. These models are often accessed via APIs. These APIs provide customers with powerful machine-learning modules that are already ready to go. This can save you money on the development and deployment of your own AI solutions. However, it’s important to recognize the risks involved in having another business host critical AI solutions. Some potential applications that could use out-of-the-box third-party AI services include: OCR: extract information from documents, like credit card numbers, customer names, addresses, and more. Speech-to-text: speech recognition services can help courtrooms, office executives, and video streaming services. Biometrics: authentication and verification services that use AI APIs allow you to easily implement biometric security into your application/service with little hassle. Such solutions can often be enough for the development of a startup at an early stage. When using third-party services and pre-trained models, all you need is to find developers who can effectively integrate these solutions and customize them if needed. Include AI in Your Marketing Strategy AI’s rich potential for content generation and evaluation of audience data makes it a powerful tool for your startup’s marketing strategy. There are a number of examples out there of how brands are successfully . Even though these examples include big brands, they can lead you to some new ideas customized for your startup. leveraging AI for their marketing Customer insights: AI is a powerful tool for processing data. algorithms in vending machines to collect data on customers to use for future personalized marketing. Coca-Cola uses AI Virtual assistance: some brands like Sephora are using AI to connect with their customers and help them find what they need. on their online store allows customers to ask questions and get personalized suggestions as they shop. Sephora’s AI virtual assistant Content creation: many brands are harnessing AI’s content creation potential. By generating and optimizing headlines, captions, summaries, and even entire articles or videos, startups can greatly streamline the creation process of their marketing content. The Washington Post utilizes , an AI writing tool, to create short stories and updates on sports, elections, and the weather. Heliograf Sales forecasting: AI can help businesses with predicting patterns in sales over time. This allows companies to prepare for waves of sales with enhanced targeted marketing strategies. Many retail companies like Walmart are using AI to predict sales or demand fluctuations. There are plenty of other opportunities for AI to make a difference in your business’s marketing. Get creative and think carefully about how AI’s potential can benefit you and your customers. Challenges of Adopting AI and Ways to Overcome Them is not without its challenges. However, good startup owners know that challenges are meant to be overcome for their business to stand the test of time. Some of the primary challenges involved are data collection, quality, and AI degradation. Implementing AI Data Collection Any startup owner experienced with AI knows that the most important bottleneck of AI is high-quality data. Ensuring that this data is accurate, complete, and consistent is crucial to the success of an AI project. For new projects, this can quickly become expensive and difficult to maintain. In some cases, businesses can train AI models with existing data that they have already been collecting. This might be enough, or it might require additional research. In other cases, entirely new systems of monitoring might be needed to gather the data required. These situations are called ‘self-collection’, where you collect the data yourself for implementation for model training. However, those aren’t the only methods of acquiring useful data for AI. Here are some alternatives: Synthetic data: simulating real data sets in software. Unity and Unreal Engine can be used to generate data. If the data is realistic enough, the results will be comparable to real-world sets. Data Purchase: it’s possible, depending on your situation, that someone else has done the work already to curate valuable datasets. If this option is available, weigh the costs of doing it yourself against purchasing the data. Outsource dataset creation: businesses can order the creation of datasets from other companies. For example, Amazon Mechanical Turk allows you to crowdsource dataset collection. Web scraping: although controversial due to privacy, consent, and intellectual property rights, there’s no denying that datasets made through web scraping have made some of the most powerful AI models in the world, like ChatGPT. Data Quality Every dataset will have some margin of error. Some names might be spelled wrong. Some phone numbers might have a leading country code, and others may not. Some fields might be left blank. There are many, many different reasons why these errors might occur. If more than half of a dataset is riddled with errors, it won’t be suitable for many tasks with AI. In fact, it might be more hurtful than helpful. The majority of the issues associated with data quality problems can be attributed to practice and procedure. AI app development experts and data science teams know how to approach these problems. Improving data quality is required to form the most relevant input before the development stages. AI Degradation Over time, AI models drop in performance. The reason for this is that the data that the model uses will become outdated over time. New data is necessary to fill in the gaps that will appear since the AI’s inception. Startup owners should look ahead to address this problem continuously, or they should plan to create an entirely new model when needed. However, some businesses use models that expire in weeks or even days. Some of the most volatile models are used for demand forecasting or stock price predictions. These tasks are said to have a high rate of model degradation. To approach this problem, businesses should consider automating data collection and sourcing. This involves a separate ecosystem where your AI model can source data for retraining while operating in a production environment. Approaching data degradation and other challenges of adopting artificial intelligence is an essential part of your work if you decide to connect your startup with AI. AI engineers and MLOps experts will help you set the right infrastructure for your machine learning model, which will be able to automatically monitor data quality and model performance, and then quickly react (rebuild the model). The Next Step for Startups Startup owners should first consider how AI will benefit them and think carefully about how to integrate the technology with their business model. Then, they should start coming up with ideas on how to implement AI based on those business goals. In most cases, startups will be short on data needed to make their AI solutions work at their best. This might be attributed to the business being young, or it might be that their use case is unique. Whether you decide to use alternative data sources or start generating it yourselves, the best return on investment will be to reach out to AI consultants who have experience in the field. They know their way around collecting, curating, and implementing training data into AI models. They can also come up with plans to update the model based on its level of degradation. The right experts will also be able to work with you to make sure your product is developed while maintaining your intellectual property rights. Regardless of the approach you choose, you must remember that AI is not magic, and all ideas must be feasible with existing technologies. So, to really boost the success of your startup with AI, you need to find the best way to implement it and the right people to help you with this task. Image source

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

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