2 posts tagged with "Enterprise"

The enterprise data stack is going through an inflection point and the battle lines are being redrawn - which happens every decade or so. This time, to no surprise, the AI agenda in Enterprise is going to determine the winners and losers.

The Changing Landscape​

The battleground for data platforms has shifted dramatically, now centering on enabling AI applications. Success in this space requires excellence in four critical areas:

1. Semantic Layer: Understanding the nuances of business data across the enterprise
2. Application Development Tools: Providing low-code/no-code toolsets for building applications
3. Orchestration Simplicity: Delivering workflows and interoperability that simplifies ecosystem management
4. AI Capabilities: Offering powerful AI frameworks that deliver results

The Major Players​

Four tech giants are aggressively positioning themselves in this space, each making significant moves in AI capabilities over the last twelve months:

Databricks​

• Strong position with Unity Catalog for data context understanding
• Introduced AI Playground and DBRX
• Application toolset strategy remains unclear

Microsoft​

• Leverages strong OpenAI partnership
• Microsoft Fabric provides excellent orchestration
• Strong application development tools
• Semantic layer remains a weakness

Salesforce​

• Comprehensive solution for customer applications with Einstein AI
• Challenge: Limited to Salesforce data ecosystem
• Faces hurdles with external data integration

Snowflake​

• Under competitive pressure but has comprehensive offerings
• Cortex AI shows promise
• Benefits from walled garden approach for orchestration
• Needs to balance user experience with economics
• Working to expand app building capabilities for business users

Enterprise Challenges​

A significant organizational challenge exists: data teams and application teams typically operate under separate leadership with limited collaboration. As AI applications become the central battleground, this could drive:

1. New winners and losers in the Data Platform space
2. Fundamental changes to enterprise IT operating models

Our team at AuxoAI has been deep in building enterprise AI applications for the last twelve months. They span across various functions and here are our key learnings.

Key Learnings​

1. Integration Challenges
2. User Adoption Patterns
3. Performance Optimization
4. Security Considerations
5. Process Automation and AI-Native Workflows

Process Automation Evolution​

Celonis popularized the concept of 'happy path' - essentially how a process (and the software) is supposed to work. In reality, the processes look something like the picture below. It is all over the place with lots of exceptions.

Most of the steps in the less happy path are a result of policy guardrails that require manual research and summarization or upstream data quality issues. This intermediate research step creates bottlenecks and results in a lot of non-value-added work in the enterprise.

The Promise of GenAI in Workflow Optimization​

The promise of GenAI models is that eventually we will see more AI-enabled workflows that: a) Automate the research step more effectively than traditional RPA bots b) Suggest the right guardrails and calibrate the system performance c) Nudge to correct the upstream data challenges

For instance, in creating a sales order, there is a guardrail of credit checks in certain instances which creates friction in the process and needs manual research and intervention.

AI-Native Approaches​

Alternatively, and this is where things get more interesting - you can think of the process itself as dynamic/adaptive. The workflow is in fact a series of decisions, and depending on the type of customer or order, you will take different paths that optimize for process outcome metrics. Not all orders should need the standard sales order generation process. Maybe there is a 'click to buy' equivalent of Amazon's buying experience for certain types of orders or customers. The probabilistic approach to process execution will be the foundation of a new breed of AI-native software.

Implementation Challenges​

Every enterprise software company is going to incorporate these models in the workflow, but the challenge will be how to think about AI-enabled vs AI-native approaches, what is easier for enterprises to adopt, and what is the willingness to pay for it.