AI feels inescapable in business today. A plethora of new solutions and AI platforms are trying to get your attention while your current SaaS providers boast about their own AI integrations promising better customer service. The momentum is building, and curiosity is no doubt piqued.

The claims of better productivity through automation are tempting—but not all AI platforms are created equal, and not all are built to do the same thing. Deciding to bring AI functionality into your business is the first and easiest step, but where do you go next? Of all the different AI platforms on offer, what will work for your business rather than against it?

There are positives to AI implementation, just as there are challenges. It takes careful research and direct questions to understand what types of AI platforms for business will help you reach your strategic goals.

What is an AI platform?

The term “AI platform” describes an integrated framework that can help businesses create a custom AI-driven solution, such as a virtual assistant, that can either benefit a company’s internal processes or its customers’ journey. An AI platform can be offered by a cloud provider as a prepackaged set of tools that can be customized, contained within a private deployment, or as a high-powered service as part of an AI-SaaS offering.

Generally, AI platforms consist of multiple tools designed to create an implementation that’s fit for purpose, and can aid the integration of AI into existing operational processes and workflows. By understanding the issue that needs to be solved and combining the necessary pieces of AI at its disposal, an AI platform creates an ecosystem of tools to aid efficiency and productivity.

For example, a custom AI assistant can be trained on the company’s internal knowledge base, learning to find the most relevant answers to queries through AI searches, and relaying all this information back to a user in an easy-to-understand manner using generative AI.

Key components of an AI platform

Most artificial intelligence platforms have a combination of key components, used either in isolation or orchestrated to form an end-to-end solution. Each business will need to determine which features best fit its use case and how to best build and integrate AI into existing workflows.

Machine learning tools

Encompassing the algorithms and libraries that provide the framework for creating and training models, machine learning tools can also support the fine-tuning of models. These tools are a key component of AI in general, but questions to ask to determine the usefulness for your business include:

What kind of algorithms does the platform use for search optimization? Is it a neural network-based algorithm or something more basic? What type of learning does this algorithm use: supervised or unsupervised? Will these algorithms best fit my scenario?
What kind of machine learning model is in place? Is it a regression model, classification model, or another? What model best suits my challenge? Are there different models to choose from? Will I need to organize fine-tuning in-house?

These questions will help define the use case for your business, narrowing down the vast amounts of available solutions. Different models and algorithms work for different tasks, so understanding what one best suits your needs can help ensure you’re following the right path.

Data management

AI platforms can capture both structured and unstructured data from a range of sources, which you may then want to scrub as part of data management. Here, we’re looking at automated processes that take raw data, sanitize it, and fill in any gaps.

Data storage also falls into this category, and it can be achieved through the use of either monolithic or cloud infrastructure that enables the storage of large datasets. Important questions to consider for data management are:

Does the platform handle structured, unstructured, or semi-structured data? Does there need to be a new process for collecting data to ensure this tool can be used? Is the payoff worth it?
What data scrubbing techniques are in use? Is that method needed for my solution?
How is the data stored? Does it employ certain techniques for handling the data for an AI solution, such as embedding? What are the costs associated with data storage? What security implementations are in place to keep data safe?

Answering these questions will help you understand the work you’ll need to put in with your data to have a successful outcome—whether it be structuring your data or suiting your data storage needs.

Compute capabilities

AI platforms can provide cloud-based or on-premises computing, and which you choose can impact scalability, whether manual or automatic. Before continuing, ask yourself:

How does this solution handle computation? Will it be fast enough for my needs? Is cloud-based or on-premises faster? Do we have the infrastructure, space, and budget for on-premises equipment?
How does computation scale? What are the costs for scaling? Does manual or automatic make more sense for my solution? Is the scaling dynamic?

These answers will help to maximize the efficiency of your new solution and ensure you are getting the most out of your budget.

Integration

Most AI platforms will offer baked-in, cross-platform support for a range of different programming languages. Application programming interfaces (APIs) enable simple integration of AI models into existing applications. Ask these questions when researching the integration of an AI platform:

What cross-platform support is available? Does it include the programming languages or cloud-based providers currently used in your infrastructure?
What is the learning curve like for implementing this platform? Is the process lengthy and complicated, or simple and short? Is there ample documentation on the implementation process?

These questions can help you to create a plan detailing how you will implement your new solution for effective integration. They can also help you to encourage best-practice usage by employees. This will help ensure a smoother transition.

Model deployments

Few AI platforms are plug-and-play, so you’ll need a robust plan for not only how to integrate your new AI platform into any existing systems (or sunset the ones you’re replacing), but also how to get employees to adopt the new systems. Ask:

Will this fit with any processes currently in place?
What is the deployment process like? Is this well-documented and easy to learn?
Will we need to hire for specialist skill sets, or can we outsource?
How will we communicate this to employees? Which stakeholders do we need to bring in to help with this—and when in the timeline should we talk to them?

Don’t be swayed by shiny objects; seek a platform that not only best suits your business, but also one that will cause the least amount of friction when integrating with your current systems.

Platform support and customization

Whether you expect the platform to do most of the work for you or you want to be heavily involved in customising the platform for your use case, find out what kind of support the provider offers. For example, what sort of training, software updates, documentation, troubleshooting, and reliability are available to customers?

Security and compliance

Data security ensures end-to-end data protection through all AI data platform operations by using encryption technology and access control methodology. Governance aligns the use of AI software platforms with legal requirements, encompassing the ethical and responsible use of AI models. For AI security and privacy, ask:

What encryption do they use in this AI platform? How up-to-date is the security of this platform, and is it aligned with current guidelines and frameworks? How is it kept up-to-date? Will we need to monitor for updates or is it done elsewhere? Do the security measurements line up with the policy currently in place for our data?
What is the ethos of this AI platform? Do they detail their ethics and explain how they take responsibility for AI use? Does it follow the applicable data compliance laws, such as GDPR or the CCPA?

It’s vital systems meet all the requirements governed both by law and internal policies. These questions will help ensure that your solution follows requirements, and help you to create resolutions to common challenges.

Model management

You can monitor models by using real-time tools that observe performance, highlight any potential problems impacting performance, and uncover issues such as model drift.

Another aspect to consider is versioning, so make sure to use processes that update AI models and can facilitate rollbacks to a previous version if needed.

And finally, auditing as part of model management involves logging transactions with AI models and their decisions.

Key questions for model management include:

How is the model optimized? Is it via pruning, retraining with improved data, or another method? How does this platform combat model drift?
How are versions stored? How far back can versions go? How far can the model rollback?
How are audits stored? Is this in line with our data management policies? How are these audits carried out—by human hands or AI-driven?

Since models can be ever-changing, it’s crucial to have a documented process to roll back systems when challenges arise, ensuring there’s a clear audit trail for posterity.

What are the different types of AI platforms?

There are several different types of AI platforms, each designed to fulfill specific use cases. Each AI platform also comes with its own set of capabilities and functionality, especially once it’s been designed and deployed within your business’ ecosystem.

Commonly used AI platforms right now include:

Generative AI platforms, specializing in the creation of content, such as text, images, and audio.
Machine learning platforms, which focus on providing the tools for developing and training AI models.
Computer vision platforms, which allow analysis of real-world visual data.
Conversational AI platforms, including virtual assistants, chatbots, and intelligent agents.
AI data platforms that enable the storage, management, and processing of large datasets for use in AI applications, such as AI search systems.
Deep learning platforms that are optimized to tackle deep learning tasks using large datasets requiring significant computing resources.
AI research platforms for those working in research, providing them with the tools to experiment with new algorithms and AI models.
Analytics platforms which mine large datasets to uncover actionable insights using predictive analytics.
Natural language processing (NLP) platforms which use AI to understand human language, often used for things like sentiment analysis.

Benefits of using AI platforms

There are many business benefits to deploying and integrating an AI platform to help optimize processes, improve business productivity, increase workflow performance, and enhance operational efficiency. A successful AI deployment could help deliver:

Faster time to market: Some types of business, software, or website developers, for example, can leverage AI to significantly reduce the time it takes for a project to move from the idea stage to the deployment stage.
Automation of repetitive tasks: Time-consuming, manual tasks of a repetitive nature, such as form processing and data entry, can be automated, reducing the amount of resources they consume.
Enhanced and more agile decision-making: AI can provide the data and insights needed to provide decision-makers with accurate information in a more timely manner.
Improved collaboration: AI platforms for business can promote a more collaborative workflow between teams and departments within an enterprise.
Reliable data management: AI platforms provide tools for simplifying and automating the process of sanitizing and scrubbing data, as well as helping to ensure data integrity. For efficient and performant data management, some platforms are increasingly using vector databases.
Scalability of resources: Most AI platforms can scale resource allocation as needed, delivering extra storage and compute power only when required, then scaling back to free-up resources. Some of the best artificial intelligence platforms leverage cloud computing, which is highly scalable and cost-effective for AI applications.
Tighter data security and simplified compliance: Most artificial intelligence platforms come with baked-in data security features such as encryption and enhanced threat detection. This helps with streamlining and automating compliance with data privacy regulations.
Ethical and fair use features: The best AI platforms include tools to help ensure transparency in the use of AI, and for policing AI decision-making to ensure ethical usage.
Access to actionable business insights: Using predictive analytics, AI can mine large datasets and present this data using visualization tools, empowering businesses to unlock and act upon the insights contained within both structured and unstructured data silos.

Use cases for AI platforms

As technology develops, businesses will discover new applications for artificial intelligence platforms. Many businesses across different sectors have already found ways to integrate AI into parts of their workflows and operational processes—such as those below.

AI in energy and utilities

In utilities, AI could significantly improve the efficiency of operations while simultaneously cutting costs.

AI platforms can help predict energy demands within different grid sectors and optimize the generation and distribution of energy, encouraging consumers to use electricity at off-peak times. This can cut costs for customers and allow suppliers to better manage energy consumption.

Additionally, AI platforms can help detect faults and outages within the pipeline. This can lead to rapid responses, with the resilience of grids improved as greater accuracy helps to detect potential faults.

AI in financial services

Financial firms can leverage AI in many ways. A comprehensive AI data platform can be used to help analyze large volumes of financial reports, analyst research, and other internal documents. The insights uncovered can then help professionals to assist customers with financial planning and risk management.

Insurance companies can also leverage AI platforms for processes such as underwriting by building a generative AI solution to highlight risk factors and trends, helping to significantly speed-up the process for analysts.

AI in healthcare

AI is improving the way that health and wellness providers interact with users. AI-assisted coaches can consider user demographics, health goals, and content history to create custom plans for customers. AI coaches that use conversational language to deliver personalized messages can increase user retention and engagement.

Also, AI research platforms have already been used to analyze chemical compounds to aid the discovery of new medicines. This can help to reduce research and development overheads and improve time to market.

AI in the public sector

The power of AI has great potential to consolidate current workstreams in the public sector. For example, administrations could implement AI-powered applications for social workers that streamline the process of writing case notes and assessments, allowing them to focus on direct care instead of administrative work.

AI can play a crucial role across many governmental departments, too. Consider how AI platforms could enhance cybersecurity for defense organizations, detecting and responding to threats in real time and protecting critical infrastructure. Or perhaps how it could help to optimize traffic flow, manage energy consumption, or create more sustainable and efficient urban environments.

AI in manufacturing

Manufacturing firms can increase productivity with AI platforms. AI can help optimize the entire end-to-end production process, manage the complexities of logistics, improve quality control, and predict when machinery will require maintenance.

Complex supply chains are historically one of the most challenging aspects of mass production. AI can integrate into the procurement and supply process to help highlight potential logistical problems before they happen.

How to choose the best AI platform for your business

When deciding which AI platform best fits your business, first you must fully define your specific use case. From there, you can match your requirements to current AI platform offerings.

To help understand what to look for in an AI platform, consider the following process:

Set out your goals and what you aim to achieve with AI within your enterprise.
Create a list of required features that suitable AI platforms must have—your non-negotiables.
Evaluate which AI platforms are potential candidates and align most closely with your needs based on this list.
Take your shortlist of candidates and research their offering, looking specifically at the platform components that are most important to your needs. Ask the questions in the key components section to help with this.

Avoiding pitfalls when choosing an AI platform

These steps could help your business to avoid common AI pitfalls. It’s important to ask the right questions before you start; this will equip you with the information you need to evaluate different AI platforms and their suitability for your enterprise AI use case.

For each AI vendor you’re considering, take some time to evaluate the company’s reputation. Have they successfully deployed AI platforms for businesses in your market or businesses with similar functionalities to yours? An AI platform’s ability to integrate with other tools, infrastructure, and databases is critical for delivering scalable, efficient, and robust AI solutions.

Check any case studies they’ve published and assess whether they’re capable of providing the support you need. Most reputable AI platform providers will offer some form of live demo or free trial, and taking advantage of this should be your next step.

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