AI, and in particular generative AI, is on everyone’s mind at the moment. AI and machine learning (ML) as a technology are proliferating and expanding across industries at a rate that’s near impossible to keep up with. Salesforce’s recent State of IT reveals that at least 86% of IT leaders surveyed believe that generative AI will play a predominant role at their organisations within the next few years.
Understanding the central role of AI in engineering new digital trends, tech-enabled processes and customer experience expectations is non-negotiable at this point. It’s crucial to embrace the wave of change AI has introduced, instead of resisting or delaying it. No matter your industry or customers, as an organisation, you need a proactive AI strategy in place to successfully harness AI in your operations and stay competitive in your landscape.
But, successfully developing, implementing and measuring a gen AI strategy also comes with a fair share of significant challenges that could impact its ROI. Knowledge gaps, process gaps, slow rollout and progress and other factors can seriously hinder an organisation’s success with AI, reducing confidence in its innate potential.
At CloudSmiths, we’re demystifying the complexities of generative AI, making it accessible to businesses of all sizes. Our tailored generative AI workshops are built around your business, industry and operational ecosystem, showing you exactly where and how you can benefit from deploying AI - and showing you how to do this! Learn how CloudSmiths can help you build an informed AI strategy from the ground up.
What are the most common challenges associated with AI implementation?
Despite AI experiencing an unprecedented surge in demand for development and use, many AI initiatives undertaken by organisations lose momentum quickly and hover in development limbo. According to IDC, 31% of organisations surveyed reported that they have AI in production but are stuck in the experimentation or prototyping phase, unable to progress.
Why is this the case? Google recently dentified four key challenges organisations consistently encounter when trying to utilise AI. We’ll take a brief look at them below.
A machine learning skills gap exists
In a recent study, Gartner reported only 10% of businesses employed 50% of more software engineers who also doubled as ML experts. A lack of accessible expertise is holding many organisations from successfully developing and deploying their AI strategies to the end. Limited user proficiency in ML is creating limits on how extensively companies can use AI capabilities to innovate and improve their processes.
AI and ML require multidisciplinary collaboration
The interconnected nature of AI and machine learning means that it often requires analysis and input from multiple stakeholders, teams and departments. Data analysis and statistics, to engineering and building, ML is a multidisciplinary process.
It can be difficult to undertake when teams operate on different, disconnected systems that create data gaps and silos. On top of this, different teams and departments may respond more slowly than others, staggering timelines and increasing costs in the process.
Slow progress in rolling out AI and ML
For businesses that do succeed in developing successful AI strategies, slow rollout and deployment can cause project initiatives to stagnate and eventually erode. This is largely due to the fact that AI and ML models necessitate experimentation, standardisation, deployment, analysis and assessment.
These processes have unique infrastructure requirements that standard software engineering and analytics processes struggle to meet. The lack of cohesive, supportive infrastructure and technologies slow down deployment and adoption and make it more difficult for organisations to adjust their strategies (if need be).
AI and ML’s complexities may create complex infrastructure or resource demands
Again, incomplete or inadequate AI and ML infrastructure can slow down rollout while driving up costs, both of which hurt the project’s ROI. An organisation’s data scientists and internal IT infrastructure teams may be misaligned or even clash over expectations and timelines of projects.
Data scientists may also not have the expertise needed to advise on infrastructure and operations engineering for optimal AL and ML models. As a result, organisations may attempt to deploy AI and ML on hardware not specifically designed to support it, hurting results and ROI.
AI, simplified: CloudSmiths’ free Generative AI Workshops
We’re passionate about the power of potential of generative AI here at CloudSmiths - but we’re also very aware of its complexities and challenges! Understandably, you may be hesitant as an organisation to invest in such a complex technology without fully understanding how it works and, more importantly, how it can benefit you.
That’s exactly why we’ve created our free Generative AI Workshops, designed to simplify gen AI and break down how you can use it in your business. Our Workshops are built around your business and industry and we show you real-life, concrete examples and use cases for generative AI within your organisation.
With your specific business needs, challenges and larger operational goals in mind, we help you identify and select the right AI solutions. We also guide you through developing an optimal AI strategy and how best to deploy it for maximum ROI.
If you’re ready to discover how CloudSmiths can help you build a transformative, results-driven AI strategy, or you simply want to learn more about our workshops, book a consultation with us.
