5 Key Trends In Enterprise Artificial Intelligence & Machine Learning

As AI continues to demonstrate unprecedented potential to transform technology and business, executives at leading enterprises are taking action to enable their organizations to capitalize on emerging breakthroughs.

Here are 5 key trends that innovative companies are driving in enterprise AI.

1. Converging Infrastructure & Connected Solutions

Organizations must be set up to take advantage of AI. Current enterprise technology stacks consist of compartmentalized vendors that tackle different workflows, making whole system and funnel views difficult to gather. To truly be able to take advantage of AI, an organization needs both more data as well as a holistic picture of their data.

AI-progressive organizations invest in converging infrastructure and connected solutions to scale workloads and collect data appropriately.

2. Critical Need For AI Leadership

Strong executive leadership is critical to collecting data enterprise-wide and driving AI solutions through political hurdles. Roles responsible for an organization’s data and AI efforts, i.e. Chief Data Scientist, Chief Information Officer, Chief AI Officer, continue to rise in popularity and importance.

Data is a critical corporate asset, but entails many organizational challenges. Data collection and cleaning is often hampered by unstructured or inconsistent organization, departmental silos, and company incentives and politics.

3. Automation & Augmentation

AI can both automate (replace entirely) or augment (improve efficiency) of employee tasks, allowing staff to focus on strategic or creative responsibilities. AI can even create new jobs, especially those related to the creation and stewardship of AI systems.

Examples of automation include cleaning and de-duping data for analytics, processing forms in the back office, flagging at-risk expense line items in accounting, and scouring the web and social media to automatically surface viable candidates for recruiting. Examples of augmentation include cyborg models (i.e. human-AI hybrid) of customer service and cognitive services that advise on executive decision-making.

Many more examples can be found in our extensive Enterprise AI landscape which features over 100+ AI vendors in 12 enterprise categories.

4. Better Decisions From Better Insights

Enterprises use machine learning primarily to surface insights, but soon tools will evolve to support decision-making based on those insights.

The primary challenge for organizations is incorporate ML-surfaced insights into day-to-day workflows and processes. To facilitate the practice, enterprise AI solutions often include managed solutions on top of product offerings, which reduces complexity for customers and ensures action-orientation. The combined solution helps to point out observations and relationships in data as well as point employees in the right direction, sometimes even automating decisions for them.

We already see examples such as AI-driven marketing campaign management systems increasing automate more and more campaign decisions, ranging from campaign parametrization to creative generation.

5. Democratization of AI Capabilities

Most groundbreaking work in AI in the past few years has been driven by major technology players like Google, Facebook, Amazon, Microsoft, and Salesforce which own the requisite data sets and computing power. However, as cognitive services have become a de facto offering and computing costs continue to decline, we expect the democratization of AI access to enable smaller businesses, startups, and entrepreneurs to experiment with applied AI and build original innovations.