AIaaS adoption: What to overcome and how to succeed

Why should businesses adopt AIaaS?

Choosing an AIaaS approach accelerates organizations' opportunities to gain AI-powered IT and business results using a cloud resource model with fee-based services.

Some of the benefits of AIaaS include the following:

• Cloud familiarity. One advantage of AIaaS is that most businesses are already familiar with adopting cloud resources. With such familiarity, staff will require minimal training, and businesses can implement AI services fast.
• Cost-effective. Some businesses lack the resources to build new IT infrastructure and develop in-house AI capabilities. By adopting AIaaS, organizations can use cloud-based AI services without substantial infrastructure overhauls and investment. Moreover, with more affordable AIaaS in place, businesses can access the massive compute power that machine learning (ML) models require for training.
• Business focus. Without the complexities of infrastructure and unfamiliar systems, companies can design business and IT use cases that achieve high levels of process automation based on data-driven insights. They can also use vendors' expertise to guide AI deployments, suggest appropriate ML algorithms and provide advice on model training.

What are the main challenges to AIaaS adoption?

Every company will experience its own set of challenges specific to its industry. However, the five main challenges that most will encounter during AIaaS adoption include the following:

48% of companies surveyed lacked enough high-quality data to operationalize their AI initiatives effectively.

• Data formatting. Consider the large volumes of unstructured information residing in siloed repositories and data lakes. Businesses must ensure that relevant data exists in the correct format for training large language models (LLM). Incomplete, inappropriate or outdated information can result in incorrect results and wasted AI deployment efforts. Recent research indicates that 48% of companies surveyed lack enough high-quality data to operationalize their AI initiatives effectively.
• Integration issues. Hurdles emerge when businesses decide to align an innovative AIaaS tool with existing legacy infrastructure. Outdated or proprietary software can pose compatibility issues when paired with AI deployments. While service providers offer APIs and pretrained models to simplify AIaaS adoption, users cannot access the low-level algorithmic code, which limits the potential for customizing AI deployments.
• Skills gap. The lack of skilled data analysts and in-house AI expertise represents a significant challenge for both startups and well-established enterprises. Companies can overcome deficits in AI training by collaborating with AIaaS providers and using their specialized knowledge and cloud-based tools to streamline AI deployments.
• Vendor selection. Companies need to assess vendor compatibility based on key parameters, such as support for data formats and the ability to handle large data volume levels. Due diligence also extends to assessing a service provider's scalability potential, regular model updates and their ability to handle future growth demands.
• Security. Businesses need to address data governance, compliance and security risks. Administrators and IT leaders should ensure that both vendor-based and customized protections are in place to prevent training data exposure and privacy violations.

How do businesses guarantee successful AIaaS adoption?

For administrators and IT leaders, aligning AIaaS with in-house procedures will help ensure optimal outcomes. Use the following best practices to guide successful AIaaS adoption:

• Start small. Businesses should start small with pilot projects. Make necessary adjustments through testing and scale gradually as AI projects mature and extend across an organization.
• Choose the right vendor. Companies should pick a provider that specializes in the type of AI they want to deploy, such as process automation, intelligent customer services or predictive IT maintenance. The vendor's platform must support the organization's data format and volume. Thorough audits of data repositories and investments in data readiness will ensure that AI models generate accurate results.
• Ensure data quality. Information management plays a primary role since quality data is the fuel for ML algorithms and is critical for high-functioning AI models. Effective data usage policies serve as guardrails and guide how client information and sensitive data are collected, distributed and secured. Improper data can produce a cascade of problems, like complicated analyses and outdated information.
• Implement KPIs. Introducing KPIs can help administrators monitor AI adoption progress. Businesses employ KPIs to calculate ROI, measure the effect of AI efforts and gauge the success of short and long-term goals.
• Invest in staff. Reskilling programs for business and IT team members will foster ownership and acceptance of AI as a partner. While AIaaS deployments can compensate for a general lack of internal expertise, specialized training can help to jumpstart AI efforts and help the workforce adapt to a constantly evolving AI landscape.

Kerry Doyle writes about technology for a variety of publications and platforms. His current focus is on issues relevant to IT and enterprise leaders across a range of topics, from nanotech and cloud to distributed services and AI.