Wondering how to leverage AI to solve mission-critical business problems? Choosing the right enterprise AI platform is key to maximize value and scale intelligent capabilities across the organization.
In this comprehensive guide, we’ll cut through the noise around AI platforms so you can confidently invest in the ideal solution tailored to your needs.
You’ll discover:
- Leading platform capabilities explained
- Emerging innovations to evaluate
- An evaluation framework to compare options
- Tips to accelerate adoption and outcomes
Let‘s get started!
The Growing Importance of AI Platforms
AI adoption hit an inflection point as leading platforms enhanced accessibility for mainstream business use.
According to IDC, worldwide revenues for AI platforms will soar from $37.5 billion in 2022 to over $200 billion by 2026 – signifying tremendous momentum.
What’s driving this growth?
AI platforms radically simplify unlocking intelligence from data via techniques like machine learning, neural networks, optimization and knowledge representation.
Capgemini research found over 56% of organizations rely on AI platforms to leverage customer data and insights. And McKinsey estimates $9.5 trillion of annual economic value globally by 2025 from enterprise AI adoption.
Now even non-technical teams can leverage AI to solve problems like:
- Personalizing customer experiences
- Optimizing supply chains
- Enhancing product quality
- Improving demand forecasting
- Automating repetitive tasks
Thanks to end-to-end platforms accelerating modeling, deployment and lifecycle management.
So how do you pick the right solution tailored for your industry use cases and stack? Let’s explore.
Key Enterprise AI Platform Capabilities
While offerings vary across dozens of vendors, most AI platforms aim to simplify 4 key steps:
1. Data Ingestion and Transformation
First, data from relevant sources gets aggregated and processed via tools like Spark, Kafka and Hadoop into formats consumable for modeling.
This data pipeline connects to sources like:
- Business applications
- Cloud storage
- Databases
- IoT sensors
And transforms it for training use via:
- Sampling
- Labeling
- Splitting batches
- Feature engineering
So you can feed clean, unified data into the next phase.
2. Model Building and Training
Here extracted features get input into machine learning model architectures – like neural networks, random forests and logistic regression – tailored for targeted behaviors such as classification, forecasting etc.
The platform trains models using techniques like:
- Supervised learning
- Reinforcement learning
- Unsupervised learning
Automation features may guide architectures, hyperparameters, algorithms and pipelines to enhance model accuracy.
Users can also select prebuilt industry or function-specific models as starting points.
3. Model Deployment and Management
Once trained, tested and approved, models get deployed into production via integration gateways, microservices and other endpoints.
This enables serving predictions or recommendations in real-time response to application requests and data payloads.
Management, monitoring and governance features help:
- Log performance
- Detect data drift
- Mitigate unfair bias
- Refresh models
So you derive maximum value safely over their lifecycle.
4. Application Integration and Development
Finally, via SDKs, widgets and REST APIs, platform capabilities integrate within apps like:
- Mobile and web apps
- Business software
- Collaboration tools
- IoT and edge devices
This “AI-infuses” experiences across touchpoints – from customer-facing to employee-facing.
Let’s now see how leading vendors enable these workflows.
Comparing Leading AI Platform Vendors
Many commercial AI platforms target these 4 steps with varying approaches and pricing models.
AWS, Microsoft Azure and GCP
Hyperscale cloud providers offer end-to-end solutions tightly coupled to their infrastructure and data services.
Amazon SageMaker enables building, training and deploying machine learning workflows with close ties to the AWS stack.
Microsoft Azure Applied AI Services provides low-code access to prebuilt AI capabilities that integrate across enterprise Microsoft products.
And Google Vertex AI combines MLOps software, managed notebooks and AutoML to accelerate building ML models on Google Cloud.
These platforms offer unmatched breadth, depth and scalability backed by cloud giants. But can limit flexibility in heterogeneous environments.
DATAROBOT, H20.ai and Dataiku
These focus specifically on using automation to enable non-technical citizen data scientists to leverage AI accelerating time to value.
For example, DataRobot auto-generates models then manages deployment and monitoring.
H20 Driverless AI automates feature engineering, model building and tuning.
And Dataiku provides collaborativedata prep and modeling with centralized model management.
They excel at usability but advanced practitioners may desire more customization.
IBM Watson, SAP Leonardo, SAS Viya
Legacy enterprise software brands blend proprietary data and analytical assets with partner technology into AI platforms.
For instance, IBM Watson applies cognitive computing for industry use cases by combining IBM and third-party services.
SAP Leonardo enables building intelligent apps leveraging SAP’s rich data foundation and emerging capabilities like explainable AI.
And SAS Viya allows coders and non-coders to solve complex analytical problems leveraging SAS’s analytics core.
Their strength lies in tight business application alignment but could benefit from more open architectures.
While dozens more options exist, evaluating providers against critical capability and architecture considerations helps determine fit. Let’s explore key selection criteria.
Choosing the Right AI Platform For You
All platforms offer similar fundamental capabilities – but vary widely in delivery models. Aligning specific architectural needs and constraints is vital for long-term value.
Here are key dimensions to analyze head-to-head:
| Criteria | Key Considerations |
| Total Cost | Compare subscription plans to expected production workloads and growth |
| Time-to-Value | Speed of onboarding and ability to show quick wins matter |
| Ease of Adoption | Evaluate sophistication required and learning curves for target teams |
| MLOps Capabilities | Requirements to monitor, maintain and update models at scale |
| Security | Protections against data loss, model theft and adversary attacks |
| Interoperability | Flexibility to leverage existing data, tools and languages |
| Scalability | Ability to cost-effectively scale complex models and high workloads |
| Compliance | Compatibility with regulatory policies on data control and algorithmic auditing |
Scoring high-potential options based on must-haves accelerates finding the ideal match.
Let’s look at best practices to run successful evaluations and pilots.
Tips For Selecting The Right Platform
With so many vendors vying for your business, navigating AI platform selection requires structured diligence. Here are pro tips:
1. Start With Business AND Technical Use Cases
Gather key stakeholders early across data, engineering and product teams to document tangible needs.
Prioritize 2-3 leading opportunities using AI to guide requirements. For example, predictive maintenance or real-time recommendation engines.
Combine this with technical “user stories” capturing existing constraints around tools, languages, infrastructure and data.
This 360-degree view sharpens your criteria.
2. Conduct In-Depth Demos Tailored To Use Cases
Shortlist vendors best matching your needs for deeper dives. Ask questions like:
- “Show how your AutoML capability would work for our predictive pricing model.”
- “Demonstrate MLOps features to address model degradation for our call center chatbot.”
See how their platforms align to your exact problem and environment.
3. Kick Off Collaborative Proofs-of-Concept
Further evaluate top contenders via multi-week PoCs tackling real challenges.
Measure speed to develop working prototypes with good accuracy. And test interoperability with infrastructure in sandboxes mirroring production.
Many vendors provide credits and engineering help to de-risk this process.
Conclusion
Selecting the right AI platform lays the foundation for enterprise AI success. Aligned to business goals and architectural needs, platforms rapidly unlock intelligence that outperforms expectations.
They enable fusing rich data with cutting-edge algorithms – allowing all software to incorporate predictive intelligence at its core.
Are you looking to tap AI’s potential? First explore the leading options available through an insightful selection process described above.
Then pilot the shortlisted few that could empower your organization’s digital aspirations via applied AI transformation.
The future awaits!
