Michael Bachman, Head of Architecture and AI Strategy, Boomi Technology Office, defines what AI-readiness means and how organisations can foster a culture of AI-readiness.
As technology continues to evolve, enterprises recognise that embracing Artificial Intelligence (AI) goes further than simply implementing new tools – it’s a fundamental shift in mindset. This shift was underscored in a recent workshop I attended, where industry leaders discussed the hallmarks of an AI-ready organisation.
But before we explore a selection of strategies for achieving AI-readiness, we need to define what AI-readiness actually means.
AI-readiness defined
Regardless of the sector or industry you operate in, data is your primary currency. Without it, every workflow grinds to an abrupt halt. AI has emerged as one of the most sophisticated data extraction tools available and the stage at which an organisation currently sits with its AI-readiness will heavily influence its status in the future.
Rather than simply having access to a Large Language Model like ChatGPT, AI-readiness refers to an organisation’s access to the people, processes and platforms needed to seamlessly incorporate AI into the nucleus of corporate operations.
People: In terms of people, skilled personnel – from data scientists to Machine Learning experts – is essential for harnessing AI insights and breeding a culture for AI adoption based on curiosity, openness and continuous learning. Where skilled personnel cannot be recruited for whatever reason, cultivating strong and collaborative partnerships with vendors can provide invaluable guidance and support.
Processes: Infusing AI into any process requires you to understand the status of your organisation’s current manual processes and where you aspire them to be. It’s an exercise that involves mapping out workflows, identifying inefficiencies and recognising where automation and AI can deliver improvements. Typically, automation consists of using software or hardware systems that perform tasks without human intervention. However, increasing efficiency and reducing human error must occur in tandem with robust data governance and the upholding of ethical considerations.
Platforms: Modern, market-ready platforms can source data from anywhere, transform it without relying on the extract, transform and load (ETL) processand analyse it spontaneously. Irrespective of the platform types you choose to adopt, AI readiness does require more than merely accommodating the technology. You’ll need a strategic plan crafted around AI that pairs your operational systems of record with modern integration systems. By accessing a context pipeline into your system of intelligence, you’ll be in a much better position to get the most from AI’s potential.
It’s all about the mindset
In an era defined by AI’s rapid evolution, organisations must cultivate a new mindset before they can begin capitalising on the technology’s extraordinary potential. Access to AI opens the gates, but it is this shift in mindset that paves the way to true AI-readiness.
Returning to the workshop I alluded to in the introduction, the gathering of industry experts in attendance developed a non-exhaustive list of six invaluable recommendations for how organisations can foster a culture of AI-readiness.
- Set clear goals: Defining clear goals for how you want an AI integration to perform is a critical first step. It’s an exercise that requires developing a strong understanding of what AI can do and its potential impact and then aligning these insights with your organisation’s vision and available resources.
Vision. Leadership. Knowledge. These three components make defining, identifying and executing use cases and outcomes easier and more fruitful. - Develop comprehensive documentation: Clearly defined processes form the foundation of AI-readiness and depend on proper documentation to ensure they are clear, maintained and adaptable. By developing detailed and transparent documentation, you can enable the seamless integration of AI tools and methodologies necessary for a powerful AI deployment.
- Establish accountability and alignment: Successful AI implementation requires delegation of ownership and alignment of vision. Process owners must have comprehensive knowledge, display robust stewardship and be free to appoint and assemble stakeholders.
This collective approach ensures that all stakeholders can contribute to the effective application of machine intelligence across the organisation. - Prioritise impact factors: The roadmap to AI-readiness is replete with factors that range from complexity and scale of effort required to risk and questions of ethics. Tackled as a whole, these factors can become unmanageable and even derail how machine intelligence is implemented within each process. Accordingly, it is necessary to establish a hierarchy of priorities based on the type of implementation desired and the resources available, with an understanding that this hierarchy may be subject to change periodically, depending on shifting circumstances.
- Introduce automation: Automation is key to creating a context pipeline for AI integration that can be connected to various endpoints like mobile devices, tablets and servers. Moreover, by eliminating manual processes, efficiency skyrockets while risks are mitigated, complexities are reduced and space for limitless scalability is created.
- Focus on data: Data is the lifeblood of AI and AI’s power to turn data into actionable information and outcomes is unprecedented. However, this means that access to relevant, high-quality data, including information system records, metadata, master data, reference data, labels and logs, is crucial. True AI-readiness that canproduce accurate insights requires a record of where data is stored, who owns it, why it is valuable and a knowledge of how it’s generated, mined, refined, secured and governed.
AI-readiness is a systemic transformation
The most profound business transformations rarely involve fundamental changes in just one area of the organisation. Becoming AI-ready is no exception. It requires the contribution of and collaboration between skilled and non-technical personnel who are able and empowered to harness AI insights and apply them to various roles. It relies on nurturing a culture based on curiosity, openness and learning, with strong partners and vendors ready and willing to provide support.
It requires processes to be mapped, evaluated and automated where possible, with ethical considerations respected and robust data governance maintained. It requires platforms equipped to handle data in real-time, in the right form and in a secure manner.
But most of all, it requires a substantial shift in mindset powered by constant learning and adaptation.
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