The Definitive Buyer’s Guide to Artificial Intelligence Software

Enterprise AI simulates tasks that were previously only possible by the human mind such as reasoning, rectifying, problem-solving, etc. Enterprise AI is now central to an organization’s daily operations.

Enterprise AI processes data to provide insights and automates redundant and high-volume tasks efficiently. Most of this is done without excessive programming too.

What enables all these data-intensive operations is the artificial intelligence platform which is a combination of powerful hardware, software, and services. Building and maintaining AI systems efficiently requires a skilled team of data analysts, data scientists, AI developers, IT security experts, and network engineers. Enterprises also require powerful computing resources. Data storage is also a priority – as your business scales, the volume of data also increases. To assemble all this can be a mammoth task involving both the technical and business teams.

That’s all good news, but where do you start?

One thing is quite clear – AI is happening. It is one of the key differentiating factors for organizations across various industries. Apart from the competitive advantage you have over your peers, you can significantly reduce costs, save time, improve existing processes and productivity, reduce errors, enhance data and analytics, become more compliant, build new products, and improve customer engagement.

When you begin working with new technology, you need to figure out how you can harness the benefits from the technology while simultaneously reducing the risks that come packaged in it. One way to go about it is to implement best practices in your AI strategy.

Buying an AI platform

If you are looking to bring home artificial intelligence technology and are looking for vendors who offer AI platforms as a service, there are a few things you need to consider.

Planning

To ensure the success of your AI project, you need to set clear objectives on what problem you want to solve using AI and the outcome of the project. These objectives should be in line with your overall business strategy. You require the right combination of skills and talents. Most importantly, you need to have the buy-in from the leadership team and key stakeholders across your organization.

Selecting a vendor

You need to allow enough time for the procurement and contracting process. This helps your team to validate the requirements, assess the market before buying an AI solution. You need to assess the proof of capability before committing to piloting the project. While you do this, you need to make sure this aligns with your business strategy and integration of existing tools that your team already uses.

Pricing

Different AI platforms are priced differently. It is based on the services and features that the product offers and where all this sits in the value chain. Commonly, expert professional services will have a service fee included in their pricing. On the other hand, commoditized work has a monthly or annual subscription fee or a one-time license fee, combined with maintenance and support fees. However, the AI market is beginning to see a shift towards a transaction-based or value-based pricing model. Whatever the pricing model, you need to assess whether the capabilities offered by the service are a good bargain. The biggest question you need to ask yourself while assessing the cost of the AI platform is ‘Is it a scalable pricing model?’

Choosing the AI software

You are essentially buying software. One of the most important things for you to consider is how this technology will integrate or interfere with the existing technology stack that your organization is using. This, we mean not just from the operational perspective but also the licensing perspective. This AI software could be delivered on or off-premise. The pricing model could be time based or value based. There are contractual clauses that cover every aspect of the licensing of the software. Since artificial intelligence is the new technological entrant, there will be issues such as patent infringement which translates to buyer risk. You need to be wary of such issues and risks.

All eyes on data

AI technology is heavily dependent on data. To build AI models using an AI platform, you need data. The real question though is – who owns this data and the output derives from it. Data can be personal, internal, external, belonging to customers. When you are using data extensively, you need to be wary of the privacy laws and regulations surrounding it. These regulations vary based on the kind of data usage and its source. Some of these regulations vary from one region to another. You need to run privacy impact assessments during the early stages of software procurement to avoid high-risk situations in the future.

Closing thoughts

AI is a bleeding-edge technology. It helps businesses across various industries and functions make critical decisions. It positively impacts key performance metrics of the organization such as revenue, growth, customer acquisition, customer churn, etc. But this is a constantly evolving technology – right from the very terminology to its capabilities, everything is rapidly changing. There is no dearth of choices when it comes to the AI software available in the market. Customer expectations are also shaping what features and services an AI platform will offer. Alongside all this, the laws and policies that govern the use of this technology are also changing. Taking a robust approach to contracting, licensing, and continuous management ensures that vendors keep the promises they made during sales.