Are We “Data Ready” for AI-enabled Transformation?
Whether you are deep in the planning and execution of AI initiatives to support your key business processes, or suffering a little ”AI fatigue” from the constant stream of commentary, AI is here to stay.
We may well be in an AI investment bubble, but just like the dot-com bubble, the eventual “exhalation of excess gas” will not change the fundamentals of how the technology can drive business value.
AI is here to stay, but the biggest challenges are not technology.
They include;
- Understanding the real business problem or opportunity
- Understanding how that topic fits into one of the end-to-end processes or value streams in the business
- Assessing how best to eliminate redundant activities and simplify and harmonise what is important.
- Aligning with an optimal customer experience
- Identifying the right class of technologies for the automation and augmentation required – where do deterministic and probabilistic approaches fit best within the process
- Ensuring the critical data foundation is “fit for purpose”
AI in all its forms (analytical, generative, agentic et al) promises to redefine how work gets done – making recommendations, supporting decisions, generating insights, executing workflows, and learning from execution experience.
But there’s a catch:
AI is only as powerful as the data behind it.
To be fair, this has been true for all business process automations and augmentations.
We continually build incremental systems, workflows and SOPs to manage the exceptions originated by data integrity problems.
This problem of “Failure Demand” continues to accelerate with our adoption of AI.
We need to go back to the source.
Data.
But the mantras of “Data Centric” and “Data Driven” are often presented as truisms, without depth.
Glen McCracken wrote a great piece reminding us of the dangers of these exhortations.
He describes the ‘Dashboard Delusion’: the comforting idea that more data is automatically a “good thing”.
But as he so clearly articulates;
– Data is partial: It never tells the full story
– Data is biased: It is a reflection of the systems, assumptions, and incentives that created it
– Data is retrospective: It describes what happened, not what should happen next
I have a favourite image that encompasses these three realities, a ripe apple in front of a mirror.
The reflection shows a beautiful rosy, ready to eat to apple.
The apple itself, in the foreground, has a large bite of out it, the flesh slightly oxidised and less appealing.
But the mirror only reflects what is in front of it.
It is too easy to put Data in the “Too Hard” bucket or relegate it to a technology issue
Data is a business issue.
There are four key, current, tectonic shifts that are bringing this truth to the front of mind in the C-Suite.
- Tariffs, taxes and related geopolitical trends. Companies around the world are realising they cannot trust their data on country of origin, tariff codes and material categories. The data lacks currency, accuracy, integrity and the mechanisms to create and keep data “clean” are ambiguous or failing.
- Efforts in business transformation and end-to-end process/value stream integration are foundering on silos of data, representing different elements of the “data mirror”
- Global ERP standardisation initiatives, driving multiple discrete, regional or BU specific ERPs to a single instance of S/4HANA, for example, are highlighting the critical need to standardise data that drives the business, which can no longer reflect purely local or functional requirements.
- And, of course, hotly anticipated AI initiatives are failing to deliver the expected business results due to ambiguous, erroneous, redundant and duplicated data.
The “Data Problem” is often put in the “Too hard” bucket because previous attempts have resulted in lengthy initiatives that feel like trying to “Boil the Ocean”.
It doesn’t need to be that way.
The key priority is the essential Master Data that drives our business processes and fuels the machinery of our automation and augmentation initiatives, such as;
- Customers and Suppliers/Vendors or “Business Partners”
- Products
- Materials
- People
- Locations
- Assets
There are typically one or two of these Master Data domains directly relevant to an end-to-end process such as Customer to Cash, Source to Pay, Plan to Make, Hire to Retire, and it makes sense to align Master Data initiatives with these process leaders for context, efficiency and buy-in.
Common Master Data Integrity issues fall into a number of areas;
- Duplication – deliberate or planned (yes, there can be bona fide reasons for duplication), impacting customer experience, asset utilization, process efficiency, errors & re-work, leakage of revenue, cash, cost and balance sheet integrity. Often duplication is not immediately apparent but implied by common data. It is not uncommon to see up to 20% duplication in active domains.
- Quality, Completeness & Integrity – completeness and accuracy of Master Data critical to effective process operations such as Tax/VAT IDs, Bank Details, Agreed Payment Terms, Country of Origin, Industry, Export Licencing, Sanction Checks etc. Up to 50% impact is common.
- Inactivity – Key Business Partners, People, Places and Things that appear dormant, with no activity over a defined historical period, indicating poor asset utilisation or candidates for inactivation/archival. Up to 85% is not unusual and may represent an untapped business opportunity.
- Activity – over a defined historical period that drives or impacts customer and product experience, assets, liabilities, P&L, cash and working capital. This is the core of current economic impact.
At least one of the four key, current, tectonic shifts above is affecting your business, maybe more.
We cannot afford to keep “kicking the can down the road”.
We need to talk about Master Data . . .
We need to take action and work on Master Data.
Our transformation initiatives will have a greater chance of success and our end-to-end processes will deliver on their promise of enhanced business value and P&L impact.
We can get “Data Ready”, at speed, for AI-enabled Transformation.
Thanks for reading . . .
Are We “Data Ready” for AI-enabled Transformation?