If, at a glance, you wish only to read the minimum, these are the seven steps – core to them, those editorially proven worldwide over generations of commercial publishing – to take to bed down your AI data-modelling processes to generate, at source, your business’s cleanest possible initial AI-data inputs.
Yet after skimming this, if you then wish to read more, in this earlier, related document, I spell out more of the supporting rationale for setting these steps out as your critical starting point for getting your best return, as early as possible, from the human factor that is key to your AI investment.
The seven steps
Step one: Supported by appropriate industry research and guard rails, your leaders should articulate the strategic benefits that might be achieved if an AI program is to be undertaken successfully within your organisation.
Step two: Pick your team of authors to ensure the best minds across the business are engaged in framing and creating the collective intelligence on which the organisation’s AI is to be modelled, and from which it will learn.
Step three: To articulate your first AI mission and to communicate organisation-wide its rationale, and how and why it will succeed, work backwards with your team and its lead authors, adopting Amazon’s well-known “working backwards” methodology for innovating to deliver superior products, to spell this out unambiguously.
Step four: To set your AI initiative’s goal posts, identify and install your AI content’s high priest as your business’s “commissioning editor.” This key individual’s role will be a critical factor in AI quality control and this post will almost certainly be occupied by a highly literate senior manager with both broad and deep knowledge and experience of your business, and trust across it, from the CEO, board and/or senior leadership team down.
Step five: In consultation with your data scientists, to create the certainty that all published content is consistent, accurate, easy to comprehend and readily machine-readable, set up your editorial style guide. This document will set the rules by which to ensure the structures and content of the key reports and documents generated by your team meet all critical continuing AI machine-learning needs.
Step six: Decide, define and create as its initial strategic platform of reference a carefully designed collection of the most important internal organisational analyses from which the AI model of your business is to learn.
Step seven: Train your team in how to create all future written documentation reliably and repeatedly in accordance with the style guide and its AI-quality-enforcing rules.
Act now
As there is no time to lose, take advantage of my longstanding professional editorial experience – I formerly worked as a sub-editor, a key fact-checking, sense-making and quality control editorial role in all professional media, on the pages of The Australian Financial Review newspaper group in Sydney – and let’s set up a low-cost workshop in which we can begin to engage and direct the intelligence of your team to get working for your business the essential discipline of generating reliably clean AI data, every day.
Contact me:
Graham Lauren
0416 171724
cloudcitizenx@icloud.com