As businesses shift their focus to digital, the popularity of artificial intelligence has exceeded its expectation. Gartner predicts that 80% of emerging technologies will have an AI foundation, which will create $2.3 trillion of business value by 2021. Due to scarce AI talent, businesses look for different approaches to implement AI in their business.
AI as a Service (AIaaS)
AIaaS allows businesses to experiment with AI for various purposes without large initial investments. Thanks to AI services, even companies without a data science department can utilize AI to achieve benefits such as increased data-driven decision making. You can work with tech giants or niche software companies to access off-the-shelf services to improve specific aspects of your processes, products, or analyze your data. Some common services that are provided by vendors are
- Conversational AI / NLP APIs & services including
- chatbots / conversational agents
- text analytics
- speech-to-text
- text-to-speech
- translation
- Computer vision:
- emotion detection
- image recognition
- video analysis
- Document understanding:
- Analytics solutions for demand forecasting, fraud detection, personalization, and recommendation systems
- Other services such as
- knowledge mapping
- Advanced search
- Personalization/ Recommendation Engines
- Security solutions
- Automated Code Review
- More areas of AI use cases are:
- 100+ AI use cases by department
- AI applications by industry
Custom AI development
For cases when the off-the-shelf AI solution doesn’t exist or insufficient for your company’s needs, building a custom solution may be necessary. You can either build an in-house solution or hire outsourcing partners. The right choice depends on
- your business’ AI capabilities,
- data science knowledge of your employees,
- budget for the project
- ownership of data
- privacy requirements of your data
If you already have access to data, and your budget is not enough to enhance AI talent in your organization, hiring an outsourcing partner is a better option. However, choosing the right partner may take time since your requirements are different than an off-the-shelf solution. We provide a whitepaper that highlights all reasons, approaches, and examples for custom AI development:
And if you need to build a custom AI solution with minimal investment, let us know. We can help you with our industry experience:
Services for enabling AI transformation
Consulting
If your company is new to AI and can invest significantly in AI transformation, you can consider hiring AI consultants. AI projects contain challenges such as lack of business alignment, the difficulty of building solutions, or assessing vendors. Their experience in the AI market can help you avoid common pitfalls or applying best practices such as reducing bias in the dataset.
AI consulting services include
- assessing the maturity of your company’s AI transformation
- identification of areas where leveraging AI or machine learning can create value
- formulating an AI strategy to launch new pilot products/services
- building AI solutions
- training your employees for upcoming AI implementations
Services to support your internal data science teams
AI talent recruitment
Data science talent is scarce. Demand for AI talent has grown by 74% annually in the past 4 years. Most of this demand is driven by a limited number of tech-giants. Therefore businesses are looking to complement full time hiring with on demand talent by partnering with on-demand recruiting companies that focus on AI and data science talent.
Data Labeling/ Annotation
Supervised learning is the most common learning algorithm for machine learning. Yet, you need a large volume of labeled data to train an AI system. For this purpose, businesses can rely on different methods such as
- In-house development
- Outsourced employees
- Data labeling agencies
- Crowdsourcing
Each method contains the pros and cons for businesses, and you can check our data labeling article to learn the advantages of each approach.
Data science competitions
You can crowdsource the machine learning lifecycle for example by launching data science competitions to handle algorithm building. This can allow your team to focus on operationalizing AI models within your company which is harder to outsource.
AI Platforms
There are AI platforms such as Peltarion that enable businesses to deploy machine learning models for applications at scale. These platforms ease the process of machine learning model building and productization of ML models.
For more on AI, feel free to check our recommended list of articles:
Bias in AI: What it is, Types & Examples, How & Tools to fix it
Explainable AI (XAI): Guide to enterprise-ready AI
AI in analytics: How AI is shaping analytics
AI in Automation: Discover tasks to automate with AI
And if you still have questions about AI services, don’t hesitate to ask:
