The Challenge of Implementing AI for Real Business Applications
The artificial intelligence (AI) ecosystem has grown immensely over the past decade. According to IDC, global spending on AI systems will reach $97.9 billion in 2023, up from $37.5 billion in 2019. These numbers reflect a compound annual growth rate (CAGR) of 28.4% between 2018 and 2023. As this momentum grows, companies around the world have taken notice.
Highlighting this dynamic, global revenue from enterprise-specific AI applications will reach $31.2 billion by 2025, a 52.59% CAGR over the forecast period. Gartner’s 2019 CIO Agenda also indicates 14% of global CIOs have already deployed AI, and 48% will do so by 2020.
Despite the well-documented potential of AI, companies must also remain diligent in following a robust due diligence process from development to implementation. A thorough assessment is crucial to ensure an AI project is appropriate for its intended purpose.
Artificial intelligence for business applications
Although AI adoption comes with inherent obstacles, Gartner reports that enterprise maturity, fear of the unknown, finding a starting point, and vendor strategy pose the biggest threat to success. As such, many companies are turning to AI solutions that streamline model development, reduce process friction, and improve project outcomes.
By engaging a third party to manage many of the daunting peripheral tasks, companies can focus on what matters most. That is, getting their project off the ground and achieving a healthy return on investment.
Data scope and quality
Artificial intelligence requires vast amounts of data to produce task-appropriate models. However, collecting the right kind of data is just as crucial when developing and implementing AI solutions. Further, employees with the appropriate skills must oversee this process to ensure optimal value extraction.
Source: Splunk Documentation
Platforms offering AI implementation support can help companies get started by providing a central repository or models and decisions built with clean data. Further functionality might include tracking of every model, outcome, and data used to make a decision, analytics reporting, proactive drift, and anomaly detection.
Implementation costs
Perhaps most daunting is the cost associated with introducing an AI solution for real business applications. According to Gartner, 50% of AI investments will be quantified and linked to specific key performance indicators to measure return on investment by 2024. In many instances, companies fail to anticipate capital costs associated with AI software and AI-building tools. As such, projects may stall before they can accelerate business growth and drive conversions.
However, a third-party platform can drastically reduce the risk and up-front investment associated with AI-integrations. According to industry data, custom AI software solutions can cost up to $300,000. However, by using a third-party platform, companies don’t need to deal with complex hardware configuration and purchase decisions.
Source: WebFX
Further, the AI software stacks and development frameworks are already in place, making implementation faster and more predictable. If the supporting platform can configure and scale elastic runtime environments to run anywhere, the benefits are even more significant.
Those platforms supporting both on-premises and cloud deployment can help companies develop a long-term strategy that suits their unique needs. According to Mordor Intelligence, the global cloud AI market is forecast to achieve a CAGR of over 20% from 2020 to 2025. As the adoption of cloud-based services accelerates, companies are likely to see even greater flexibility through hybrid deployment models.
Source: Mordor Intelligence
For many companies looking to start an AI project, their development work might begin in the cloud and then move to their infrastructure as needs change. If one third-party provider can facilitate this process, cost savings may be significant in the future.
Integration complexity
As most companies will continue to use legacy systems alongside new AI solutions, integrating the two is a crucial consideration. Open architecture is essential to ensure AI implementations communicate effectively with the environments they will be impacting.
To assist with this undertaking, some AI platforms offer API’s, or connectors, that facilitate pre-built integrations with existing applications. Such integrations mean that it can read data sources, write data into apps, and publish the results of machine learning into the application itself.
The simple way to implement AI
Platforms like Quickpath can help companies overcome the challenges associated with building an AI-enabled application. This powerful platform gives businesses access to cost-effective, reliable AI solutions that eliminate the burden of development and implementation - regardless of the industry.
Source: Quickpath
Further, the Quickpath platform removes barriers to AI development by dramatically lowering associated costs. Instead of making substantial investments in new software and hardware, companies can get a head start - while also muting concerns of data corruption. By further democratizing access to AI models, Quickpath is bringing future tech to a broad range of companies that will contribute to the ecosystem for decades to come.