No big budget, no data scientists, no months of hard work – 4 ways your startup can become AI-driven today
Artificial intelligence (AI) is what computer scientist Andrew Ng calls “the new electricity .” But despite its power and charm, AI is not suitable for every situation. In a previous article , I presented five scenarios in which you should avoid investing in AI.
To find out if your startup needs AI, start by prioritizing your business problem. Let’s frame the best approach to solving these challenges and evaluate how technology can help. In most cases, you will be able to work effectively with basic analytics, statistics, or simple machine learning.
In some situations AI horsepower is needed. In such scenarios, additional intelligence and automation will transform your startup. This article is for such cases.
When people feel the need for AI, the next question they often ask is, “Do we really need a big budget to use AI?” The answer to this question is no. It doesn’t take months of hard work, elite data scientists, or a big budget to make your business AI-driven.
Here are four ways your startup or small business can start using AI today. These suggestions are laid out in order from easiest to hardest, so start at the top and see which option best fits your needs.
Here are the five situations in which AI should not be invested, according to Mr.5 Situations Where You Shouldn’t Invest in AI
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1. Enabling AI features for tools you already use
AI is all around us. Your smartphone probably has at least a dozen AI-powered apps. This technology is empowering us to take better photos with our cameras, organize our photos, and curate our social feeds.
Most enterprise tools are adding AI-powered features to their products. Microsoft has built some AI features into Excel. If you insert data from screenshots or take advantage of the insights suggested by Excel’s Ideas panel, you’re using AI.
Salesforce has integrated Einstein , the company’s AI engine, as an intelligent assistant across its popular CRM (customer relationship management) platforms. Some companies will bundle AI capabilities into their core products, while others will need to upgrade.
Ask your vendor if the software you buy has AI capabilities. Existing toolsets may already be AI-driven, or could be adapted with a simple upgrade.
Five popular tools in this option: MS Office , Google for Business , Dropbox , Github , Mixmax
2. Buy off-the-shelf AI-powered SaaS tools
Today, SaaS (Software as a Service) tools are plentiful and available for a reasonable monthly fee. Want to polish your marketing copy? Grammarly ‘s handy copy-editing feature covers the good stuff. Want to transcribe testimonial videos or do professional-grade media editing? Descript ‘s AI features make it easy.
If you have an unmet business need, look for functional SaaS tools with intelligent features. Most SaaS tools come with built-in integrations that make it easy to integrate into your existing IT ecosystem. Even if it doesn’t fit perfectly, what matters is whether it solves most of the problems. If so, you can avoid investing in expensive enterprise licenses for similar AI capabilities.
Evaluate the available tools against your key requirements. Check for matching coverage and ease of integration. If the results of your investigation exceed the acceptable range, let’s hurry to postpone the hiring.
Five popular tools in this option: Zoho Zia , Trello , Grammarly , Descript , WaveApps
3. Incorporation of ready-made AI models into tools
If you can’t find a tool with built-in intelligence, the next best thing is to search the cloud for AI models that you can connect to the tool. For example, if you’re trying to find manufacturing defects in a product, AI can be used to automate visual inspection. Amazon Lookout for Vision is a cloud-based machine learning (ML) service that plugs directly into your workflow.
Unlike the previous step, this step requires DevOps capabilities (including software development and IT operations). It also doesn’t require data scientists, but the team will need programming expertise to link software applications to online AI models. Also pay attention to the subscription cost, which is determined by usage.
To consider this option, identify an online ML platform that has pre-built AI models to solve your domain problem. The space has seen promising startups such as Clarifai, Dialogflow, and SightHound, as well as big players such as Microsoft, Google, and Amazon.
Five popular tools in this option: ML on AWS , Azure ML , Google Cloud ML , Clarifai , Sighthound
4. Retraining publicly available AI models
Once you’ve exhausted the options above, hire a data scientist to train an AI model in-house. Save effort by reusing publicly available AI algorithms and easily curated datasets instead of starting from scratch. These resources can be applied to solve your problem.
For example, let’s say your startup needs to understand customer satisfaction by analyzing text feedback from customer surveys. For that, we need algorithms with natural language processing (NLP) capabilities. Instead of painstakingly training new AI models , teams can build AI models based on models that have won public competitions such as Kaggle , DrivenData and AICrowd .
The best things on the Internet are often free, but it takes time to find them. Look for open repositories like HuggingFace that publish models with pre-trained weights, or communities that publish ML models like PapersWithCode . Many of these sites share rich and curated data that can accelerate the process of model building. As a team, evaluate the effort required to adapt the published models (to the problem you want to solve) and determine the cost of maintaining them as a product.
Five popular tools in this option: HuggingFace , AllenAI , RasaHQ , Kaggle , DrivenData
AIcrowd is an organization that operates a platform that organizes competitions for AI and data science-related problems faced by companies and research institutes and introduces companies and organizations that provide solutions . Planned competitions included the development of an AI that captures the roguelike game “NetHack” sponsored by the former Facebook (prize of $20,000) and the obstacle avoidance ability of drones sponsored by Amazon (prize of $ 50,000 ). )and so on.
Being AI-driven is a journey, not a destination
In this article, we’ve seen four ways to get started with AI and get the most out of your resources. While starting an AI journey is often straightforward, it requires ongoing attention and investment to deliver consistent business value.
Enterprises will need to train users, restructure organizational workflows, and manage the cultural shifts associated with AI adoption. It’s also important to regularly review the total cost of ownership (TCO) of your AI investment. A valid option today may be expensive a year from now.
For example, a subscription to an AI-powered SaaS tool (shown as the second option) might be a good fit for a small team serving an early customer base. As teams grow and usage increases, subscription costs can become prohibitive. At such a stage, you may find it more economical to hire a small team of data scientists and retrain publicly available AI models (option 4).
We have summarized what options are available to streamline decision-making regarding AI introduction.
