Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
Subscribe
No Result
View All Result
Dataconomy
No Result
View All Result

Applying AI Solutions at the Startup, Growth and Enterprise Stages

by Paul Christianson
June 11, 2020
in Artificial Intelligence
Home Topics Data Science Artificial Intelligence
Share on FacebookShare on TwitterShare on LinkedInShare on WhatsAppShare on e-mail

We see companies applying AI solutions differently, depending on their growth stage. Here are the challenges they face and the best practices at each stage.

A growing number of companies are seeking to apply artificial intelligence (AI) solutions, whether they want to launch disruptive products or innovate the customer experience. No matter how business is approaching their strategy, they’ll need to label massive amounts of data – text, images, audio, and/or video – to create training data for their machine learning (ML) models. 

Of course, AI isn’t developed with a one-size-fits-all approach. We find that companies apply different strategies based on their size and stage of growth. Over the past decade, we’ve seen companies leverage AI solutions and encounter challenges along the way, as they come to us for data labeling, or the data enrichment and annotation that is required for training, testing, and validating their initial ML models and for maintaining their models in production.

  • Startup companies tend to apply narrow AI to tackle specific problems in an industry where they have deep domain expertise. They typically lack data – especially labeled data that is primed and ready to be used for ML training. They may be challenged by choosing the right data annotation tools, and many lack the expertise or funding to build their own data labeling tools.
  • Growth-stage companies are using AI solutions to enhance customer experience and drive greater market share. They typically have a fair amount of data and domain expertise, and they may even have the capabilities to build or customize their own data labeling tool, although perhaps without features like robust workforce analytics. At this stage, navigating competing priorities can be a challenge, where technical resources can be easily stretched and operations staff can get dragged into performing low-value data tasks. The companies in this stage that are applying AI most effectively are those that are giving thoughtful consideration to their customers and missions, focusing on their core competencies, and offloading what makes sense to outside specialists.
  • Enterprise companies typically are using AI in one of two ways: incorporating AI into a product or using it to innovate business processes to generate better efficiency, productivity, or profit margins. Larger companies often have plenty of data and extensive in-house technical and data expertise. They are spending millions of dollars on data and AI, but siloed communication across products and departments can make it difficult to get a unified snapshot of the data landscape and where there are opportunities for AI to improve the business. In general, enterprise companies are not as advanced on the data maturity curve as they’d like to be.

As companies of all sizes seek to apply AI solutions, the one component that is more important now than ever is the role people play in the process. Data preparation is a detailed, time-consuming task, so rather than using some of their most expensive resources – data scientists – a growing number of companies are using other in-house staff, freelancers, contractors and crowdsourcing to get this massive amount of data work done. 

Best practices for AI solutions implementation 

At the end of the day, it takes smart machines and skilled humans in the loop to ensure the high-quality data that performant AI models require. That’s a crucial dynamic when you consider some of the real-world challenges the technology is in a position to help solve. From the ability to identify counterfeit goods or reduce vulnerability to phishing attacks, to training autonomous vehicles with hardware upgrades that make them safer, it’s quality data that makes AI truly valuable.

For companies that are looking to apply or develop AI solutions, here are a few best practices we’ve identified that can help ensure efficient, productive data operations: 

  • Secure executive support: Leadership is a key factor in success, and lack of leadership leads to 87% of data science projects failing to make it to market.
  • Incorporate data science early: Companies that consider data science and data engineering early in their process will see the most success.
  • Collaborate often: Direct access to and clear communication with the people who work with data makes it easier to adjust tools and process (e.g., guidelines, training, feedback loops), which can positively impact data quality and the overall success of an AI project.
  • Be prepared for surprises: Developing AI is iterative, and change is inevitable. Companies should consider their workforce and process thoughtfully to ensure each one can provide the flexibility and agility they will need to facilitate innovation quickly while maintaining accuracy along the way. When you realize you’re going to need more labeled data than planned, and quickly, it’s critical to have the right foundation for quality at greater levels of scale.  

AI requires a strategic combination of people, process and technology

At any stage of growth, it’s important to understand how to strategically combine people, process, and tools to maximize data quality, optimize worker productivity and limit the need for costly re-work. Leveraging best practices from companies that work with data can put an organization in the best position for success as the AI market continues to grow and new opportunities emerge.

Related Posts

Skybox AI brings AI to VR

Skybox AI brings AI to VR

June 1, 2023
Whispering algorithms of smart surroundings

Whispering algorithms of smart surroundings

May 30, 2023
Infrastructure challenges and opportunities for AI startups

Infrastructure challenges and opportunities for AI startups

May 31, 2023
QR codes in AI and ML: Enhancing predictive analytics for business

QR codes in AI and ML: Enhancing predictive analytics for business

May 29, 2023
See how Photoshop AI Generative Fill feature changes designers’ lives

See how Photoshop AI Generative Fill feature changes designers’ lives

May 31, 2023
Claiming to be the most humane AI chatbot, Replika AI wants to be your empathetic pal

Claiming to be the most humane AI chatbot, Replika AI wants to be your empathetic pal

May 26, 2023

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

LATEST ARTICLES

Sneak peek at Microsoft Fabric price and its promising features

Amazon employees walk out against to board to make a change

RarBG shutdown: Check out the best alternatives to try now

FTC rang Amazon’s bell this time, and it wasn’t as kind as Alexa

Skybox AI brings AI to VR

Achieving seamless compliance with automation

Dataconomy

COPYRIGHT © DATACONOMY MEDIA GMBH, ALL RIGHTS RESERVED.

  • About
  • Imprint
  • Contact
  • Legal & Privacy
  • Partnership
  • Writers wanted

Follow Us

  • News
  • AI
  • Big Data
  • Machine Learning
  • Trends
    • Blockchain
    • Cybersecurity
    • FinTech
    • Gaming
    • Internet of Things
    • Startups
    • Whitepapers
  • Industry
    • Energy & Environment
    • Finance
    • Healthcare
    • Industrial Goods & Services
    • Marketing & Sales
    • Retail & Consumer
    • Technology & IT
    • Transportation & Logistics
  • Events
  • About
    • About Us
    • Contact
    • Imprint
    • Legal & Privacy
    • Newsletter
    • Partner With Us
    • Writers wanted
No Result
View All Result
Subscribe

This website uses cookies. By continuing to use this website you are giving consent to cookies being used. Visit our Privacy Policy.