Unless you’re a global goliath with teams of the very best data engineers and scientific minds at your disposal, enterprises must look beyond the technological rhetoric, making their own judgements on what they see and hear.
How can they discover their state of AI readiness and utilise existing technologies, leveraging their potential with AI without breaking their banks or their backs?
As a Lead Software Engineer, I discuss in this blog the reality of AI. I also examine the need to focus on tangible, real life applications - to reign in the urge to future-gaze and let our imaginations drive AI adoption and business goals.
Our white paper addresses the challenges organisations face as agents of both digital transformation and converting AI prophesy into clear requirements and business value.
AI adoption and an organisation’s ability to utilise and deploy it in a meaningful, beneficial way depends on technological and organisational readiness.
These factors are defined by both enablers and inhibitors. Enablers either facilitate and accelerate AI deployment, while inhibitors make adoption more difficult. For organisations, the importance of enablers and inhibitors evolve over time as AI implementation and the need for greater innovation becomes more complex.
Under an Adoption Framework, technological readiness refers to data resources and infrastructure organisations require to successfully deploy and utilise technologies such as AI.
Traditionally, it was very expensive to produce AI models, requiring vast amounts of computing power, infrastructure and subject specific data. However, the widely-available tools, datasets and models of large organisations, and the shift to cloud and serverless technologies has dramatically improved technological readiness. This facilitates access to the powerful tools AI offers.
There are many facets of an organisation’s readiness that can influence their potential success in adopting new AI technologies, such as:
• Their culture
• Management support
• Strategy
• Organisation compatibility
The culture of an organisation can be seen as one of the key factors in the adoption of new AI technologies as it can fundamentally alter its strategies, business model and processes.
An organisation’s acceptance of new technologies is presented in many ways throughout its structure, and the willingness of its engineering teams to learn and incorporate them into their work.
Organisational readiness depends on management support. The complicated processes to embed AI technologies requires adept navigational and organisational skills to effectively deploy resources and coordinate teams.
Strategy goes hand-in-hand with an organisation’s managerial direction; it needs to be clearly defined and is a driving factor in AI deployment.
Describing the approach and stance the organisation will take on the new technology not only provides clear goals and requirements, but the very processes and ways in which they will be achieved. An organisation’s compatibility plays a vital role in the success of a new technology. It must have a clear understanding of how AI can be utilised, and the processes required to define the precise problems the solutions will solve.
There are two major themes in how organisations ultimately utilise AI technologies to provide value: automation and augmentation.
• Automation has long been established as the use of solutions to replace human process and the behaviour of increasing efficiency and repeatability. AI enhances the quality and flexibility of automation due to its versatility in adapting and learning to scenarios that improve over time. AI’s rapid development, due to increased compute power and available training data has shown the potential to surpass human performance and effectiveness in complex tasks.
• Augmentation refers to AI not only being applied to replace and automate human process, but more importantly enhance human ability and cognition. Replacing human intervention in highly repetitive tasks has great scope to improve business process. However, recent advancements have focused on augmentation and AI’s ability to support and assist in human understanding to improve decision-making and complex analytics.
As an engineering team at Exception, we continuously strive to push the limits of our own skills and get our hands on the latest available technologies. We are naturally inclined to delve into the realms of AI and Machine Learning.
However, the real challenge isn’t accessibility or a desire to learn but converting these exciting technologies into clear business use cases. But how can they be applied to real life scenarios to drive the project pipeline and provide value to the organisation?
These applications span a diverse range of areas, not only bringing value through enhancing digital solutions with generative AI and data processing but also optimising the development pipeline. In turn, this supports and enhances our engineering capabilities, evolving how we operate and facilitating digital transformation.
There are many tools and frameworks appearing on the ever-growing AI market, which utilise the capabilities and resources of expansive organisations. Leading AI companies have a wealth of experience to draw upon, with long established data models and the research to back them up. AI offers the potential to not only extend our technical capabilities, but also redefine our ways of working and underlying infrastructure.
There is an increasing expanse of AI methodologies which allow forward thinking and progressive businesses to excel in the field without necessarily having the capacity to forge their own paths.
The availability and ease of use of such technologies as ChatGPT, Google Bard and Microsoft Copilot is evolving modern society in unimagined ways.
The surge in AI technologies and their demand highlights the potential for application in digital transformation and the importance of AI in driving business processes. This is not only an important asset in an organisation’s arsenal, but it also presents increasing difficulty in how we apply AI. Importantly, we must be selective, choose the right tools for the job and meet the challenge of managing and organising these processes.
There is ever growing mysticism on how AI can be leveraged to optimise costs and drive quality, but many companies struggle to determine how to approach something as complex and intangible as AI. Despite its accessibility and ease of use, few can realise these ideals and harness AI’s potential for their business.
Whether it’s Machine Learning (ML) models processing ever greater quantities of data points or generative AI creating unimagined realities, the emergence of AI has sparked the imagination of not only tech and scientific specialists but the wider public.
With experienced monoliths leading the way such as AWS, OpenAI, Microsoft and Google, smaller organisations now have an affordable and effective gateway into this exciting, futuristic world resembling something straight out of the pages of an Asimov novel.
Ultimately, AI implementation is all about the “hear” and now.
But, at the same time, Digital Leaders must keep an open yet discerning mind about future, realistic innovation.
Our white paper provides measured guidance for CIOs and their enterprise on separating AI fact from fiction. This invaluable resource represents the art of possibility in the path towards digital transformation for 2024 and beyond.