back to blog

You’re not behind on enterprise AI: Where to invest in 2024

Read Time 10 mins | Written by: Cole

You’re not behind on enterprise AI: Where to invest in 2024

While the world debates whether AI will destroy us, intelligent enterprises are already building AI/ML capabilities to scale their business. And while generative AI is booming, this landscape isn’t just about new chatbots or LLMs. AI and ML capabilities in 2023 and beyond touch everything from global process automation to advanced pattern recognition in data analytics.

You’re not behind yet, either. AI and ML have already changed the world, but even the AI experts are trying to catch up and figure out how to use AI. There is still a long runway to find out where to invest in AI as a business and get the best ROI from the new tech.

It might be an internal, enterprise-grade LLM based on OpenAI’s ChatGPT API, so you can maintain data security and give your employees access to a new efficiency booster. Or you might want to use AI and ML for wide-scale automation of business processes and data analytics. AI can even control your IT and app resource management or edge compute allocations to save you a lot of money over time.

We’ve already started to build AI and ML products for our partners using the latest advancements in the field. Here’s an overview of where we’d invest as a growing enterprise in 2023. 

First, let’s take a quick look at how AI and ML (machine learning) differ. 

What’s the difference between AI and Machine Learning?

Artificial intelligence and machine learning get lumped together a lot. That’s because machine learning models are often critical to the AI capabilities that we use. But not always. Some people even use these terms interchangeably, but they’re two separate fields in computer science. 

Here’s a quick breakdown of the difference.

Artificial Intelligence (AI):

  1. Definition: AI is a broader concept that refers to the development of computer systems that can mimic human intelligence and perform tasks such as problem-solving, decision-making, language understanding, and learning.
  2. Scope: AI encompasses various techniques and methods, including rule-based systems, expert systems, search algorithms, and optimization techniques.
  3. Goal: The ultimate goal of AI is to create systems that can function autonomously, adapt to new information, and perform tasks that typically require human intelligence.

Machine Learning (ML):

  1. Definition: ML is a subset of AI that focuses specifically on developing algorithms that can learn from and make predictions or decisions based on data. It's about creating models that can find patterns in data without being explicitly programmed for a specific task.
  2. Scope: ML relies on statistical techniques to enable machines to improve their performance with experience (i.e., data) on a specific task. It includes techniques such as supervised, unsupervised, and reinforcement learning.
  3. Goal: The goal of ML is to develop models that can generalize from known data to unknown data, making predictions or decisions without being explicitly programmed to perform the task.

Overall, AI is a broader field encompassing various techniques for mimicking human intelligence with computer software and systems – it includes ML as a subset. And ML is a specialized area within AI focusing on learning from data. 

All machine learning is AI, but not all AI involves machine learning. 

10 AI & ML enterprise capabilities worth investing in now

Internal LLM tools, product and content recommendation engines, data analytics, and IT resource management are some of the significant areas worth investing in right now. AI and ML capabilities will continue to evolve, and if you get these onto your roadmap and learn as you implement them, you’re also setting your business up for success in other areas. 

In general, AI and ML work best for already well-defined processes that perform well and need to scale across global operations. But, there are also plenty of unexplored opportunities and new areas worth spending R&D time on.

Based on what our venture-funded and Fortune 500 clients are doing, here’s where we’d recommend focusing your early investments. 

  1. Internal LLM tools
    Building internal LLM chat tools through APIs gives employees a secure way to use this new technology to improve their business.
  2. Customer service automation
    Using AI-powered chatbots for 24/7 customer support and sentiment analysis.
  3. Predictive analytics
    Forecasting sales, market trends, and preemptive equipment maintenance.
  4. Fraud detection and risk management
    Real-time transaction analysis for fraud detection and financial risk assessment.
  5. Supply chain optimization
    AI-driven forecasting for inventory management and route optimization.
  6. Personalization and recommendation systems
    Tailoring product recommendations and content to individual preferences.
  7. Healthcare analytics and diagnostics
    AI-assisted predictions for patient disease risk and diagnostics through image analysis.
  8. Image and voice recognition
    Implementing facial recognition for security and voice-enabled services.
  9. Environmental monitoring and sustainability
    Utilizing AI to predict environmental trends, manage energy usage, and aid in sustainability efforts.
  10. Sales and marketing automation
    Using AI for lead scoring, customer segmentation, and personalized marketing campaigns.
  11. IT resource allocation/cloud compute management
    Automating IT resource provisioning, scaling, and cost optimization in cloud environments.

How global companies leverage AI & ML

The world’s most successful companies are already using AI and ML to scale their business, increase revenue and customer engagement, and gain substantial competitive advantages. From your product recommendations on Amazon to supply chain software automation, there’s definitely some part of your business that can benefit from investment in AI and ML. 

Here’s a list of brands across industries that use AI and ML capabilities in areas you can invest in too. 

  • Google
    Search algorithms, voice recognition (Google Assistant), recommendation systems (YouTube), self-driving technology (Waymo), and cloud services with AI/ML capabilities.
  • Amazon
    Personalized recommendations, fraud detection, supply chain optimization, Alexa voice service, and Amazon Web Services (AWS) for AI/ML cloud computing.
  • Facebook  (Meta)
    News feed algorithms, targeted advertising, facial recognition, AI-driven content moderation, and development in virtual reality (VR) and augmented reality (AR).
  • Microsoft
    Cloud computing with Azure AI, LinkedIn's recommendation systems, AI-driven cybersecurity solutions, and AI-powered productivity tools like Office 365.
  • IBM
    Watson AI platform for healthcare, finance, and legal applications, AI-powered cybersecurity, and supply chain management.
  • Netflix
    Personalized content recommendations, optimizing streaming quality, and predictive analytics for content success.
  • Tesla
    Autonomous driving features, battery optimization, and manufacturing automation.
  • Alibaba
    Personalized shopping recommendations, logistics and supply chain optimization, and cloud computing services with AI capabilities.
  • Siemens
    Industrial automation, predictive maintenance, energy management, and healthcare diagnostics using AI and ML.
  • General Electric (GE)
    AI-driven monitoring and diagnostics in healthcare, energy management, and industrial IoT (Internet of Things) applications.
  • Salesforce
    AI-driven customer relationship management (CRM), sales forecasting, and customer service automation.
  • Adobe
    Creative Cloud AI features for design optimization, marketing automation, and customer experience personalization.
  • Spotify
    Music recommendation algorithms, personalized playlists, and ad targeting.

How to start investing in AI & ML capabilities

Investing in AI and ML capabilities is more than a technological endeavor; it's a strategic business move. By following a systematic approach—from understanding business needs to ongoing refinement and improvement—your enterprise can align investments with specific goals and create sustainable, value-driven AI/ML solutions.

Here’s how we’d start exploring an investment in new AI and ML capabilities.

  1. Understand your business needs and objectives
    Identify key challenges, opportunities, and specific goals that AI/ML might help you address.
  2. Assess your existing capabilities
    Evaluate current technology, data availability, and in-house expertise to understand what you need to implement AI/ML solutions.
  3. Research AI/ML technologies and industry applications
    Look into various AI/ML techniques and tools and study how similar businesses or competitors have applied them.
  4. Consult with AI/ML experts
    Seek advice from professionals or consultants specializing in AI/ML to understand what technologies align with your business needs.
  5. Consider ethical and legal implications
    Understand potential legal constraints, especially concerning data privacy, and implement responsible AI/ML usage guidelines.
  6. Start with a pilot project
    Design a small-scale project to experiment with AI/ML, learn from it, and refine your approach.
  7. Analyze ROI and scalability
    Examine both short-term and long-term returns on investment and assess how the solutions align with your business growth plans.
  8. Invest in training and skill development
    Educate your team on the adopted technologies and processes to ensure a smooth transition.
  9. Stay updated on industry trends
    Continuously monitor technological advancements to understand their relevance to your business.
  10. Iterate and continuously improve
    Regularly review the success of your AI/ML initiatives, being ready to adapt and refine your approach as you gain more insights and as the technology evolves.

How to start building AI & ML capabilities in 4-6 weeks

You need a team of AI and ML experts to build enterprise capabilities and it could take 
6-18 months (or longer) to hire those internal resources. These senior software engineers and agile professionals are hard to find, demand high salaries, and are expensive to maintain. But if you want to put AI and ML on your roadmap sooner than that while saving some money, you can get started with 4-6 weeks with Codingscape. 

We have senior software engineers and agile pros who are experts in AI and ML. We’re not a software engineer recruiting agency either. You scope out the work with us, and we’ll integrate with your team, technology stack, and partner with you for as long as you need us. 

Zappos, Twilio, and Veho are just a few companies that trust us to build software with a remote-first approach. We know AI and ML at enterprise scale and build high-quality solutions you can stake your reputation and career on.

You can schedule a time to talk with us here. No hassle, no expectations, just answers.

Don't Miss
Another Update

Subscribe to be notified when
new content is published

Cole is Codingscape's Content Marketing Strategist & Copywriter.