This article is for: Software startup founders who want to learn the basis of AI for startups when considering how to use AI in their products. Artificial intelligence tools can be useful if implemented properly – read below to find out how.
Artificial intelligence (AI) has become an incredibly popular buzzword in the tech industry, often portrayed as the next big innovation that your software startup needs to prepare itself for. However, a huge innovation like AI tends to scare off early startups because it seems too complicated of a technology to implement. While large enterprises and even some knowledgeable startups are designing their own AI programs and solutions, less experienced businesses are still able to take advantage of the less advanced artificial intelligence tools that are available to help make work more efficient. There are a wide variety of applications of artificial intelligence that are a simple implementation away from playing a huge role at your business.
AI has become an integral part of the success of many modern tech companies, whether that’s a huge enterprise or a brand-new startup. It delivers intelligent solutions to a wide range of existing business inefficiencies and the ability to logically self-learn to address similar problems in the future. Pretty much every discipline is finding some way to get involved with AI, from the automotive industry to life sciences to video games and beyond!
For startups, new advancements are making it easier to integrate AI into your software or cloud solution. Many investors or other funding sources have shown great interest in AI research and development as well. In general, the reputation of AI as being some sophisticated technology reserved for those with the bountiful means to implement it has come and gone. This article explores how you can implement AI at your software startup today. The following topics will be covered in this article:
- Why Implement AI at Your Software Startup?
- Types of AI
- Common Uses of AI
- 8 Key Steps to Implementing AI
- Helpful AI Support Programs for Startups
- Some Startups Capitalizing On AI
- Key Takeaways
We will illustrate these ideas through case studies of companies and founders who work with the Altitude Accelerator network
AI for startups: why implement artificial intelligence tools at your startup?
As much as the giants of the tech industry are using different applications of artificial intelligence to keep themselves ahead of the competition, startups can also use AI to create efficiencies themselves and avoid wasting the little money they have. A report from PwC says that global GDP will rise by 14% by 2030 because of AI; startups are believed to play a significant role in that growth.
While implementing AI may seem like an unaffordable and unnecessary luxury for your business, you should consider all the ways it can cut costs. For one, extra employees or outsourcing for repetitive tasks can often be replaced by AI. Or by delegating these tasks to AI, your employees can focus their energy on more involved, creative tasks. These tasks can include logistics operations like scheduling or data entry. By refocusing your employees and augmenting their existing skills with AI, you can increase productivity and significantly reduce wasted time.
Many more of the advantages you can get from integrating AI solutions can be found later in this article.
Types of artificial intelligence
AI has become an extremely broadly-applied term in the tech industry that can represent anything from a comprehensive virtual assistant to a simple tool that deletes spam emails. When learning about the options you have for implementing AI for startups, it’s important that you’re able to distinguish between the different categories so you can have a better idea of what you’re getting yourself into with each. If you’re not so technical yourself, familiarizing yourself with these terms makes it easier to communicate with your developers about what kind of AI is right for your business.
Popular types of AI
There are multiple ways that AI technologies are technically categorized, which will be explored below, but here are some of the more common types of AI that you’ve likely heard before.
Machine Learning (ML) involves the creation of algorithms that define a goal and allow the machine itself to learn how to reach that goal. Most of the simpler artificial intelligence tools integrated at a software startup will make use of ML. In essence, we teach a machine how to do something itself by continuously giving it experience with the data involved in the task. As the number of samples encountered by the algorithm increases, its performance increases as well. After identifying natural patterns within a dataset, the machine is then able to get insights and predict the unknown.
Supervised machine learning conducts categorizations and regressions based on a set of input and output data. Unsupervised machine learning finds patterns based only on input data, often used for more exploratory analysis.
Deep Learning (DL) is a subset of machine learning that layers several algorithms in a hierarchy, with each providing a different interpretation of the data being fed through it. This network of algorithms is called an artificial neural network and can make intelligent decisions on its own, as opposed the human assistance required to complement ordinary ML. Deep learning does, however, require much more data to work with.
Natural Language Processing (NLP) involves the manipulation and processing of natural language data, mainly speech and text. NLP is used whenever a computer is able to understand the user’s language and do something with the data. This includes speech-to-text services and spam email recognition.
Machine Vision is exactly what it sounds like, ideally enabling to machines to see what we see. This field aims to capture and analyze visual information using a camera or any other digital signal processing. Vision programs usually makes use of machine learning so computers can figure out what they are looking at.
Functional classifications of AI
AI programs are most commonly classified by their ability to “think” like a human, or which human-like functions they are able to perform.
Reactive Machines are the oldest and most limited forms of AI. These machines are not able to learn or retain memories from their experiences, but are able to respond to stimuli in a way that imitates the human mind. They are generally used to respond to a specific set of inputs. One example is IBM’s Deep Blue, an AI program that beat a chess grandmaster in 1997.
Limited Memory Machines encompass almost all of the existing applications of AI we know today. This category includes machines that learn from their previous experience to improve their responses to stimuli. Therefore, this includes ML, DL, NLP, Vision, and more.
The last two categories are both either a concept or a work-in-progress. Theory of Mind machines are able to understand people’s emotions, motivations, beliefs and interact socially. Self-Aware machines would mimic the mind of a human being, having its own conscience and sentience. Many strides have been made in these fields but no fully-functional application is known yet.
The alternative way to classify AI applications is similarly by their capability. Artificial Narrow Intelligence is able to perform a human-like task autonomously as programmed; this includes all reactive and limited memory machines, therefore all AI applications created to date. The classification also includes Artificial General Intelligence, which would boast the functionality of a human brain; and Artificial Superintelligence, which would exceed the capabilities of the human brain.
AI for startups: common artificial intelligence tools and uses in business
AI features in enterprise software
With AI features often readily available in many commonly-used enterprise software, large companies no longer have the competitive advantage of access to enterprise-level AI solutions. Businesses should learn how to take advantage of the services that are readily available to everyone to avoid being left behind by more tech-savvy competition. Especially for businesses worried about the technical and time requirements to implement AI, doing so is seamless when it is part of a platform you already use.
One of the major examples is Salesforce, a common platform for customer relationship management and sales. It launched an AI for startups platform called Einstein that can provide many helpful services to small businesses including:
- Predicting customer service issues
- Identifying new prospects
- Personalizing marketing based on customer preferences
- Automating emails so they’re sent when they’re most likely to be read
- Predicting which leads are most important and the quantity of sales in your pipeline
Artificial intelligence tools for analytics
The discipline in which AI is expected to make the biggest splash is business intelligence and analytics. Collecting, cleaning, and analyzing data can be a taxing activity that gets pushed to the side by small businesses because they simply don’t have time for it. Hiring analysts at a startup is costly and entrepreneurs usually seem to have more pressing issues than inputting data into a spreadsheet. Luckily, AI is expected to take over repetitive tasks like data cleaning or input at startups over the next few years; it will even be able to take over a larger portion of a business’ statistical analysis.
Data collection is perhaps one of the most tedious parts of analytics. Despite having much smaller amounts of data than a large company, software startups are still able to draw key conclusions from what is available to them. Not only that, but startups can also collect much greater amounts of data from the start using AI. Data gathering mechanisms like sentiment analysis or machine learning algorithms can track customer habits and produce massive amounts of data!
Another important part of business intelligence is competitive analysis. AI can help you understand market trends and keep up with your competitors. As your company scales up, it can also help you identify new niches to address while retaining your existing customers.
Crayon is a company that tracks your competitors’ data across a variety of digital channels; that data could be pricing adjustments, marketing language, or much more. Crayon also identifies and analyzes market trends to provide you with actionable insights to inform your strategic planning.
Google Analytics also boasts its own Analytics Intelligence feature that gathers SEO information and reviews key insights on your website’s performance. Mentionlytics is an Ai-powered tool for tracking mentions of your business, your competitors, and the industry. Tellius analyzes your data and automatically makes intelligent discoveries, then you can ask it questions about the insights. IBM Cognos is a cloud-based tool that makes it easier to visualize, analyze, and share business insights. Whatever process you want to get done quicker, there’s sure to be an AI platform out there to help you out.
Programmatic advertising with AI
Using AI for programmatic advertising allows your marketing campaign to target customers that are expected to be receptive to your message. This is made even simpler by advertising through AI-based platforms like Facebook or Google. By taking advantage of the artificial intelligence tools available to you, your business can gain exposure to the right audience without having to hire a whole marketing department. A report from McKinsey found that the business areas most impacted by AI will be marketing & sales, supply-chain management, and manufacturing. OptinMonster is an AI-powered conversion optimization tool that lets you send your users personalized messages, conduct A/B testing on your ideas, and more!
Predictive analytics for recommendations
Just like how many prominent online stores make product recommendations to shoppers, startups can use machine learning to forecast the demands of their users and make suggestions. There are several platforms available that you can integrate into your online store to generate relevant recommendations; this can convert casual customers or upsell loyal customers.
Analyticly is a platform that can do exactly this by making AI-driven recommendations on your Shopify ecommerce site that learn over time. They are a local startup company mentored by the Altitude Accelerator and their platform is simple to use even if you’re not a data scientist. Using these recommendations, businesses are able to pleasantly surprise users with the exact product they want based on their behaviour.
Virtual assistant services
Although it’s already been said that you obviously won’t be able to afford the robot assistant you’ve dreamed of at the busiest of times, there are still several artificial intelligence tools that can provide human-like assistance to relieve you of some daily tasks that otherwise could be forgotten about.
Using X.ai, you can cc a digital personal assistant in your emails and they will schedule a meeting with whoever you are talking to. FirstAgenda helps record meeting minutes and identifies key words so you can easily go through a recording of the meeting later. You can even have content on your website written and optimized using Atomic Reach. It adds key words and sentences to your content that will get more of an emotional response from customers. Again, an AI tool to match whatever monotonous task you need some relief from is just a simple Google search away.
Deploying AI chatbots at startups
As much as you would love a personal assistant, your users could also use some help when navigating your website or platform. Deploying chatbots using AI allows businesses with limited staff to appear involved with customers 24/7. Chatbots are a way for customers to converse with an intelligent computer program instead of busy employees. This can often eliminate much of the need for outsourcing customer service representatives so you can answer users’ questions without a significant investment. It also creates highly efficient customer service that avoids human error, retains customers, and maintains your reputability.
Chatfuel is the go-to platform for building a chatbot for Facebook Messenger while Flow XO is an excellent platform where anyone can build a chatbot for a variety of platforms. nmodes is a startup mentored by the Altitude Accelerator that builds chatbots and voice bots for your business that can converse with customers about their issues and close sales.
Artificial intelligence tools for hiring at startups
Large companies have plenty of resources to network, recruit, and onboard new talent, making it hard for startups to compete in securing quality employees; AI can streamline and expand the hiring process so everyone has a fair shot. It can also identify which hiring practices worked best in the past, like where you found employees, how you reached out to them, or what kind of communication works best for them. Some AI programs discover people that seem like a good fit and collect information on their past work.
Vervoe is an AI tool that helps you screen candidates during the hiring process by testing them for certain skills and attitudes. Arya is another artificial intelligence tool that identifies top talent based by predicting which candidates perform this; it does so by learning from your current team and previous successful hires.
Build your own AI with open-source platforms
In the off chance that the inefficiency you’ve identified does not yet have a matching AI solution, you may consider building your own AI program. This can even be an option when you are technically skilled and just want a solution that perfectly suits your situation. However, developing your own AI obviously costs a lot of time and money. If a sophisticated AI program isn’t a core competency of your business, it would not be advisable to spend that much time building something from scratch. Therefore, using open-source platforms for creating your own AI program is the best way to cut the costs of mastering AI yourself.
TensorFlow is one of the most widely-used and well-maintained machine learning frameworks out there. It is available in almost all programming language and is used by several huge companies including Uber, Twitter, and eBay. A variety of courses are available to teach you how to make the most of the platform if you have a technical background. Some other popular platforms include Keras, which is known for user-friendliness; and Spark MLlib, which is designed for processing large amounts of data.
Some platforms can even enable those with no coding ability to build a custom machine learning model. Lobe uses a simple visual mechanism to build, train, and implement a model that works with visual or audio data. Google’s Cloud AutoML similarly allows users to conduct custom machine learning according to their needs, capable of working with images, natural language, and structured data. With DataRobot, users can create their own predictive models for data in minutes. With the advances in both off-the-shelf solutions and user-friendly platforms, AI is more accessible than ever before!
8 key steps to learn how to use AI for startups
As complicated as AI technologies are themselves, implementing them across an organization of any size can be difficult for any team. It requires everyone involved to get on board and a lot of trial-and-error before things can be optimized. However, sticking with it through the struggles will pay off if it is implemented effectively.
1. Identify a process
The first step of any AI project at your startup is identifying which process could benefit from being supplemented by AI. After reading this article and learning a little more about what AI can do elsewhere, think about which inefficiencies in your day-to-day operations could be automated. For most startups that are just getting started with AI, a process that is currently manual and time-consuming is chosen. This is some simple task that greatly impacts the efficiency of your business process; a time sink that eats up your productivity. These processes often include data entry, fraud detection, or simple financial tasks. Overall, you want to find a process that makes everyone’s life easier at your business, not something so advanced that it’s replacing employees right away.
2. Show the value of the artificial intelligence tools
Before you get started with AI, you will want to make sure your chosen process can truly benefit from the AI services available. This can not only reassure you in your decision, but help other employees and stakeholders see value in the investment in AI. The way you measure the impact of a solution varies by process, but find a way to measure or predict the potential financial, time, and overall business impacts of AI. One common way to prioritize which AI solutions to pursue first is making a feasibility/value matrix. This helps you compare how much value you’re getting from a solution versus how much you’re spending on implementing it.
3. Get buy-in from all stakeholders
An AI solution cannot be properly implemented without complete acceptance from everyone across the organization. This includes staff, cofounders, the board of directors, and anyone else who will need to interact with the technology or contribute to its funding. The first part of getting everyone on board is getting them to understand why and how AI is beneficial. This goes back to the previous section, as finding a way to quantify its impact goes a long way to convincing the skeptics. Stakeholders will want to ensure that it won’t be a waste of money so you need to identify where exactly the cost savings will come from. As you begin implementing it, continue to track time/cost savings so everyone remains on board as the project progresses.
Getting buy-in from employees requires the assurance that AI will make their lives easier. They may be skeptical that AI could talk their job or add extra responsibilities to their current role. Show them how AI will allow them to focus on the more important parts of their job and be more productive. Make sure to also show them what training will be required or how their roles will change upon implementation so they know they will be able to handle it.
Implementing AI requires an organizational culture that favours experimentation and data-driven decisions. If someone is afraid of the slightest signs of failure, they will likely shy away from continued use of AI. Using these solutions is very experimental and requires not only the employees to overcome a learning curve, but the algorithm itself. Therefore, the organization needs to be prepared to work with some imperfect technologies before they learn to perform optimally.
4. Acknowledge the capability gap in your organization
Although new AI platforms are making it more accessible than ever before to non-technical people, you still need some technical know-how at your business to implement solutions into your own platform or software. Because of this, you need to identify who in your organization has some knowledge of working with AI and what abilities need to be brought it. This obviously varies depending on what type of solutions you plan on working with or building, but you should always have someone on board that can identify inefficient processes, manipulate your data, and analyze the findings of AI analysis.
AI talent is hard to come by; the field is expanding much quicker than the talent pool and it’s not very affordable for every startup to hire an expert. Unless sophisticated AI is your startup’s core competency, you probably don’t need to hire a team of machine learning specialists. The most cost-effective way to bring AI skills into your organization is by bringing in a part-time consultant or re-skilling the developers you already have. That’s probably a lot safer than relying on an inexperienced AI student to take care of it.
A study from Accenture shows that 97% of surveyed CEOs plan to enhance their employees’ capacities through AI, while only 3% plan to invest in training or re-skilling for it. Leveraging the existing technical skill on your team and just giving them new tools in their toolbox could give you a big edge on the competition.
However, as stated earlier, many of the simpler AI platforms available can be used by someone without coding skills at all. Many AI providers also work in a consultative way that assists you in implementing their services, so make sure of whether any advanced skills are required before you start investing in training your team in them.
5. Identify, collect, and clean the required data
The size and quality of the dataset being fed into your AI solution plays a significant role in how successful it will be. Beyond identifying which data is required, there are several considerations to keep in mind when managing data. First, you need to ensure you have access to this data, as well as whether updated data will always be available. This means providing everyone who needs to use the data with permission to access it and determining a method for having the data be constantly updated.
Internal data can often be separated between different storage areas, created by different people, or kept in the hands of another business. Wherever your data is, make sure the data you need is kept in a centralized location so it can be cleaned, organized, and inputted consistently. You also obviously need to check whether any parts of the data you’re inputting is incorrect or irrelevant. Lastly, keep track of where data was collected from and who has access to it so you can keep that in mind for future use.
Organizing all of your data may seem like a burdensome task that comes along with implementing AI. However, once you have practise working with these technologies and have a firm system in place for collecting and cleaning data, this process can be done with ease.
6. Run a pilot project to test the artificial intelligence tools
One way to tie together many of the ideas discussed so far is to run a small pilot project to make sure the AI solution is working as you’d hoped. This helps show the value of the solution, keep employees on board, and ensure you have the right data in place for a long-term scalable project. Pilot projects are typically 2 or 3 months long but can vary more widely depending on scale.
Keep in mind the value you expected to gain from the solution when a process was identified and keep track of whether the actual project is keeping up with that prediction; it can help you make more accurate predictions for the next project. You should also measure the standard cost of the process when it is done manually so you can compare that to the cost of the process when it is automated by AI.
During a pilot project, keep both the coders and business people involved. Coders can learn about implementing the AI and how well it is working while business people can start to draw conclusions from the results and figure out where else the business could benefit. At the completion of the project, analyze whether the AI implementation was effective and how it could be used and scaled long-term.
7. Start small and slowly
Implementing AI requires a lot of experimentation, and often new skills to accompany new features. Implementing a lengthy collection of solutions at once can lead to disaster since it’s difficult to keep track of what’s working and what’s not when you’re managing several parallel projects simultaneously. Therefore, it’s important that your AI implementation starts off small and slow, leaving room for it to grow with the company. When you’re not yet sure what benefits AI will give you, you definitely should hold off on adding every cool tool you see.
Starting off small involves selecting one or two tasks that could benefit from AI and taking your time to track whether they are having a positive impact on your business. As you continue to tackle small tasks with AI, you will get a better idea of how much you are willing to invest in it. AI investment decisions should always be aligned with your business’ goals; it’s difficult to measure which solutions are contributing towards achieving those goals when you’re adding five new projects at once.
When implementing AI at your startups, you need to have the data and skill set to support it. Starting small and slow lets you take simple steps to add skills to your team and work with the data you have. As the company grows and more resources can be poured into recruiting, training, and data science, the AI can be scaled up alongside the business.
8. Get ready to grow
At a certain point, your business will be far more comfortable with and capable of implementing AI solutions. Investing in scaling up your AI projects and adding more over time will incrementally increase the value that AI brings to your business. However, you always need to be prepared to technologically accommodate the AI system you plan to put in place.
One of the major infrastructural requirements of a growing AI department is storage space. The hardware you use should accommodate the bandwidth required for storage, graphics processing, networking, and more. To make sure you’re well-prepared, consider hiring a technical consultant to help you through the business’ transitional periods. Adding a sophisticated new algorithm is certainly part of growing your company’s AI competencies, but with more AI comes the requirement for a larger volume of data to build more accurate models and achieve more computing objectives.
With such large amounts of data and knowledge in your hands as the company grows, security is key to keeping important information in-house. You should at least have the proper encryption method, virtual private network (VPN), and anti-malware software in place to ensure you are as safe as possible.
Learning about funding, support programs and AI implementation at startups
Although the implementation of AI can lead to great cost and time savings in the long term, it does require you to overcome a bit of a learning curve and can be expensive to get started. Reach out to the Altitude Accelerator for updates on informative events, resources, or connections to funding sources as your company prepares to implement AI. RIC’s experienced team of advisors have expertise in AI for startups. Here are some other resources to support AI projects at your startup.
Learn more about AI for startups and how to use AI in software projects
Before implementing AI solutions, it’s obviously very important to get familiar with the different applications of AI, what they do, what platforms they’re available for, and how to implement them. There are several informative courses online, many of which are featured on the same websites where you can learn to code yourself. Some of the main websites to look to for help include:
- Learn with Google AI
- LinkedIn Learning
- Kaggle Learn
- Applied AI Course
- Fast.ai machine learning for coders
To read more about current research on the applications of AI across different industries, look no further than Stephen Hawking and Elon Musk’s Future of Life Institute and OpenAI, an open industry and academia-wide deep learning initiative.
NextAI from NextCanada
NextAI is an accelerator for early stage AI-enabled startups. It is delivered in Montreal and Toronto and looks to help founders commercialize their AI research to build a global company. They provide technical and business education for founders and up to $200K in funding for your idea. Being a more AI-focused organization, this program is meant more for startups whose core technology is based around AI.
Innovation Funding and Support from the Government of Canada
The Government of Canada of Canada offers connections to a wide range of financial plans, funding sources, and supportive facilities for both business and technology innovation at domestic businesses. This website allows you to search for programs that fit your business exactly based on your industry, the stage your business is at, what your goals are, and more. Search through the hundreds of available programs to find the ones that will benefit your business most and get your AI projects off the ground!
The Vector Institute focuses on improving Canadian AI to foster economic growth and improve Canadian lives. They are a government funded, not-for-profit organization dedicated to research in the field of AI, excelling in machine and deep learning. The Vector Institute works with a broad range of startups to advance AI research and drive its commercialization across Canada.
Altitude Accelerator Case Study: Robyn Halbot from Analyticly
Robyn Halbot is the cofounder of Analyticly, a supplier of AI-driven software that helps businesses with financial forecasting and Shopify product recommendations. She personally has a background in financial forecasting but plans to adapt her products to other industries in the future, including curated market intelligence.
Analyticly works with startups to replace the burden of creating financial formulas on spreadsheets with a machine learning (ML) model that makes advanced predictions to improve their forecasts. The business-user friendly platform allows even non-technical professionals to select, configure, and run models. With so much experience in helping startups implement AI into their business operations, Halbot sat down with us to give her advice about getting started.
She says that while enterprise companies possess the resources to have in-house developers explore and develop different options for AI, small or mid-level businesses should stick with platforms with built-in ML capabilities as an entry point. She clarifies that these smaller companies should start with ML as opposed to more advanced AI. Halbot views ML as an algorithm trained to make predictions while AI is able to learn and give you knowledge “on the fly.”
At their outset, companies implementing ML should find a process with a defined outcome and try to streamline it. To do this, they need to establish the precedent for what good performance in that process means and use it as a baseline to compare against the ML-automated process. A statistical model should be created to measure whether ML is actually improving the process’ efficiency. This also helps you communicate why there is a benefit to ML implementation to other stakeholders.
Halbot often finds that a company’s biggest struggle isn’t usually the use of AI, but rather the communication of its value. This communication is important because business people tend to care much more about the value added than the technology itself. The fact that value can be difficult to show early on makes this a struggle for many startup teams.
Halbot also admits that implementing AI can be costly up-front. However, process automation using another company’s pre-made technology (like Analyticly) makes it more affordable for startups. She advises against getting into machine learning “just for the sake of it,” seeing as its only beneficial when there is a specific inefficiency you’re trying to address.
Altitude Accelerator Case Study: Chris Houston from SurfEasy
Chris Houston was the CEO and founder of SurfEasy, a VPN service focused on security at large companies, which has since been acquired twice. Houston now acts as the VP of mobile product development at Symantec, SurfEasy’s latest acquirer. There he uses a pattern-recognizing machine learning technology to get rid of spam. By having an “abuse module” to inspect user traffic and patterns and take action against spam, employees don’t have to look at any user data. As someone with a huge technical background, Houston was able to implement this system with the help of some more knowledgeable developers.
Examples of how startups use AI from the OCE Discovery Conference 2019
At the OCE Discovery Conference this past May in Toronto, hundreds of innovative new startups were showcased to thousands of conference attendees. Below are some of the startups whose innovative applications of AI technology stood out.
Pantonium is an on-demand public transit optimization platform. Their aim is to encourage an increase in public transit participation and a decrease in busses in circulation. Integrated with a city’s public transit system, Pantonium uses proprietary algorithms to build schedules and routes on-demand for a variety of transportation operations.
The LUCID multisensory wellness station makes use of ML-powered sensors to play music for users based on their biometric data. The company aims to provide an immersive mental wellness solution for students, employees, healthcare providers, and beyond. At their outset of development, their team used the open-source AI platform TensorFlow to get their MVP off the ground. As their business continued to progress, they later created their own AI system.
QuantWave is a real-time, AI-powered microfluidic sensing system. They aim to improve the quality and decrease the cost of industrial manufacturing processes. This technology is applicable across a variety of industries from food & beverage, to pharmaceuticals, to cosmetics. One issue QuantWave found with developing their own AI was managing communication between front-end and back-end developers.
Key takeaways on artificial intelligence tools and how to use AI at software startups
- Startups that are just getting started with AI should focus on using machine learning to automate a process that is currently time-consuming when done manually
- AI is most easily implemented into your existing products and business operations when using the artificial intelligence tools available on enterprise software platforms or using other off-the-shelf pre-made solutions
- Analytics and marketing seem to be the areas where AI will have the largest impact, but it is also useful in customer service, hiring, scheduling, and more
- Start small and slow when implementing AI, only working on one or two processes at a time and giving the algorithms time to learn
- Get organization-wide buy-in on AI implementations by using measured results to show its value and investing in employee AI training
- Keep track of what data you need, where to get it, whether you have access to it, where to centralize it, whether you have the storage for it, and how you will secure it