Artificial intelligence (AI) promises to be the most disruptive technology since the advent of the internet. AI adoption has exploded across enterprises, with 97% already actively experimenting according to recent surveys. The global AI software market is projected to reach over $500 billion by 2028 as companies embed ML-powered intelligence into more processes, products and decisions.
But the range of AI platforms available today is vast and complex. Legacy tech giants like IBM, AWS and Google offer robust toolkits while scores of agile startups target specialized niches. With so many options to evaluate, identifying the best fit for your specific needs can be challenging.
This guide serves as an invaluable resource for leaders embarking on an AI platform journey. It provides an unbiased expert overview of the leading contenders in the space and equips you with a structured process for determining what solution aligns to your business goals and technical environment.
Key AI Platform Criteria
Here are the core features and capabilities to analyze when assessing AI platform options:
Ease of use – the simplicity of model building with autoML capabilities and pre-trained models
Access to AI experts – availability of professional services or consulting to guide adoption
Trust and ethics – rigorous procedures that minimize biases and ensure integrity
Flexibility and control – ability to customize beyond default settings as skills advance
MLOps & governance – workflow automation, reproducibility and model monitoring post-deployment
Interoperability – integration with complementary data tools you already utilize
Scalability – ability to cost-effectively scale usage as demand grows
Business accelerator – pre-built solutions to jumpstart ROI in your industry
Transparency – model interpretability and explainability tools
With so many factors at play, performing an objective apples-to-apples comparison between prospects using these criteria is crucial but time-consuming. That‘s why leveraging external expert analysis can hugely shortcut your selection process.
Forrester, Gartner and other leading research firms invest tremendous resources annually evaluating vendors across all the above considerations. These offer invaluable third-party guidance based on real customer feedback and informed analysis specialized in AI software. I reference some of their recent assessments throughout this report.
Now let‘s survey some of the top AI platform contenders that should be on your radar.
Google Cloud AI
With deep investments in AI research and a vast technical workforce, Google Cloud has quickly evolved its AI solutions for enterprise customers beyond consumer offerings like Google Assistant.
Key Strengths
- Over 200 pre-trained ML models available
- AutoML for no-code model building and TensorFlow for advanced users
- End-to-end managed AI services from data labeling to MLOps
- Enterprise-grade capabilities for security, reliability and scale
Use Cases
- Image classification
- Price optimization
- Predictive maintenance
- Personalized recommendations
- Sentiment analysis
Integrations
- BigQuery, Looker, Dataproc, Kubernetes Engine + all Google Cloud data and analytics services
Limitations
- Less specialized offerings tailored for vertical industries
- Can be complex navigating all capabilities
Specification Details
Languages Supported Python, R, SQL, Javascript, Java, Go
Delivery Options Cloud or on-premises
License Pay-as-you-go based on usage
Model Deployment Environments Supported TensorFlow, scikit-learn, XGBoost
MLOps Capabilities CI/CD integration, model monitoring,
metadata managementTable summarizing some key Google Cloud AI platform specs
Microsoft Azure AI
With 90% of Fortune 500 companies utilizing Azure, Microsoft‘s cloud boasts one of the largest enterprise installed bases. Its AI platform leverages all that institutional knowledge.
Key Strengths
- Strong digital assistant, vision and conversational AI capabilities
- Deep learning automation reduces need for coding
- Simplifies compliance for regulated industries
- Cuts cost/risk by identifying model drift
Use Cases
- Predictive forecasting
- Intelligent process automation
- Anomaly detection in IoT data
- Automated virtual agent creation
- Semantic search experiences
Integrations
- Power BI, Dynamics 365, SQL Server, Azure DevOps + all Azure data, analytics and infrastructure services
Limitations
- Less flexibility for advanced users to customize workflows
- Primarily focused on Microsoft-centric organizations
Specification Details
Languages Supported C#, Python, JavaScript, Java
Delivery Options Cloud or on-premises
License Pay-as-you-go based on usage
Model Deployment Environments Supported ONNX, TensorFlow, scikit-learn,
PyTorch
MLOps Capabilities CI/CD integration, model
monitoring, model comparisonTable highlighting Microsoft Azure AI platform parameters
IBM Watson Studio
IBM offers a robust enterprise AI platform combining trusted data and analytics foundation with Watson‘s AI capabilities honed over years of widening real-world adoption.
Key Strengths
- Simplifies and accelerates model building with AutoAI
- Curated AI services catalog for easy discovery
- Local compute options to enforce strict data governance
- AI Fairness 360 toolkit to mitigate harmful bias risk
Use Cases
- Clinical trial matching
- Supply chain forecasting
- Financial risk modeling
- Call center automation
- Predictive building maintenance
Integrations
- IBM Cloud Pak for Data, SPSS, Cognos Analytics, Streams, Db2 + all IBM analytics and cloud infrastructure software
Limitations
- Steep learning curve across breadth of offerings
- Heavily optimized for existing IBM customers
Specification Details
Languages Supported Python, R, Scala, SQL, Java, Lua
Delivery Options Cloud (public, private, hybrid) or on-premises
License Subscription or consumption-based payment
Model Deployment Environments Supported TensorFlow, PyTorch, Spark ML, sklearn, xgBoost
MLOps Capabilities CI/CD integration, model monitoring,
lineage tracking Overview of some Watson Studio platform specifications
Key Inputs Guiding Your AI Platform Selection
With the platform landscape evolving so rapidly and each vendor sporting unique strengths and incentives, making the optimal choice for your needs requires thorough diligence across a range of considerations.
Document Your Target Business Outcomes
Be extremely clear on the key initiatives driving your AI platform search. Common strategic goals include:
- Improving customer experience via personalization
- Increasing sales conversions with predictive lead scoring
- Accelerating research through better insights from scientific data
- Reducing operational costs with intelligent process automation
- Detecting financial fraud faster amid expanding volumes
- Strengthening competitive advantage as boundaries are removed
These objectives directly influence platform functionality requirements like specialized analytics toolkits, vision capabilities, conversational interfaces, automation enablement or data connectivity needs.
Of course, documenting your current pain points and desired outcomes also helps establish clear ROI expectations and metrics upfront that can be tracked post-implementation to gauge project success.
Assess Overall Organizational Maturity
The maturity level of your infrastructure, staff skills, executive alignment, governance and change readiness determines the amount of hand-holding and knowledge transfer required during platform rollout and ongoing operations.
Key variables that influence AI adoption success include:
- Skills gaps – 50% of organizations report lacking skills for scaling AI initiatives
- Data readiness – only 53% have mature data management capabilities
- Culture challenges – 63% struggle driving company-wide adoption
- Unclear ROI – 55% are unsure of the business value AI will uncover
If your organization is early on the AI maturity curve based on factors above, search for platforms offering guided services like proof of concepts, training bootcamps, playbooks and expert support.
Estimate Current and Future Usage
Carefully project both your short and long-term needs for platform data processing power and number of models to be deployed.
On the data side, document current and expected volumes around:
- Batch records for historical analytics
- Streaming records for real-time decisions
- Endpoints like mobile devices, APIs or IoT systems ingesting data
Likewise tally the types of ML models or apps required now and in the next couple years across:
- Custom developed models
- AutoML generated models
- Conversational interfaces
- Intelligent process automation bots
These volume estimates help right-size your platform selection and cloud resources to ensure smooth scalability as needs evolve.
Map Existing Technical Architecture
Carefully analyze your current analytics, infrastructure and end-user application landscape to understand compatibility demands and gaps your AI platform must fill via integrations or native capabilities.
Crucial areas to map include:
Data platforms – where is core business and operational data stored? Schema complexity? Access needs? Security protocols? Governance policies? Is a data lake, data warehouse or hybrid architecture leveraged?
Analytics tools – what existing BI, data science, data engineering and visualization tools are analysts and data professionals leveraging day-to-day? Will these be retired or need integration?
Infrastructure – is on-prem, public cloud, hybrid cloud or edge infrastructure utilized? Kubernetes foundation in place?
End-user stack – what channels, apps, interfaces and touchpoints does the AI platform need to ultimately connect to for staff or customer consumption?
This architectural review exposes technical debt, dependencies and gaps needing addressed in your platform choice.
Carefully Calculate Total Cost
When tallying expenses, look beyond superficial sticker prices to incorporate ongoing resource needs like additional cloud storage, compute and services to support your initiative as it scales.
Cost factors to consider:
- Data storage – how much needed now and in future as usage expands?
- Model hosting – initial and forecasted model quantities driving compute needs
- Training times – potential savings utilizing automatic ML capabilities
- Data acquisition – any new sources required?
- Orchestration – workflow automation and MLOps expenses
- Professional services – outside experts to guide adoption?
- Licensing – pay-as-you-go, consumption or subscription options?
Compile all cost factors above into a 5-year TCO projection model comparing platforms. Include contingencies given the likelihood of exceeding initial estimates as adoption accelerates post-implementation.
Assess Overall Platform Viability
Given the market‘s fast evolution, scrutinize prospects across factors like financial health, installed customer base growth, pace of innovation and other indicators that influence platform longevity and partnerships.
- Company size – larger vendors inspire more confidence regarding stability and sustained platform investment
- Revenue growth – quickly growing sales signal strong product-market fit and adoption momentum
- Funding – well-funded startups likely to continue disrupting incumbents
- Leadership – tenured, pedigreed execs and data scientists inspire trust
- Client logos – marquee customer lists indicate applicability across use cases and verticals
Applying the structured decision framework detailed above will optimally position you to determine the ideal platform matching technical, business and budget requirements for your AI success.
Realizing Maximum Business Value from AI
Selecting the right underlying AI platform to enable your ML aspirations is a crucial first step. But sustained commitment across management, staffing, processes and technology is vital to crystallize goals and systematically scale efforts.
Here are 5 best practices to drive widespread adoption and stellar returns on your platform investment:
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Evangelize a data-first culture – consistent data quality and accessibility is the fuel that powers AI success. Make analytics excellence a company-wide priority.
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Structure ROI tracking – tie back key initiatives and platforms to financial return projections shared across leadership.
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Champion visible quick wins – spotlight early pilot successes across the organization to inspire engagement.
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Prioritize skills development – aggressively train both data and non-data staff on AI tools to expand productive capacity.
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Iteratively expand use cases – don‘t get hung up on perfection – embrace an agile, test-and-learn mindset.
The democratization of artificial intelligence ushered in by these modern platforms promises to be the single most transformational business driver and industry changemaker in the decades ahead. With a carefully structured evaluation and adoption approach, your organization can ride this wave to new heights of operational efficiency, customer intimacy and sustainable competitive differentiation. The future powered by AI looks exceptionally bright.
