Existential threat
A few months ago, a disruptive event occurred in industries around the world, causing disruption and displacement. Aspirants position themselves as leaders, and unsuspecting dominants rush to catch up so as not to be left behind. The event was merely a response to a pressing question that companies had or had not asked:
Progressives
A friend of mine launched an LLM to monitor regulatory changes so that he could be the first to comply; Despite everything, bank CDOs May be imprisoned for data breaches. Doctors employ AI image recognition to reveal conditions undetectable to the human eye and make surgical decisions in real time brain tumor. Using satellite imagery, insurers employ artificial intelligence to estimate relative value devastation for disaster victims and withdraw ACH payments without having to visit homes.
Those who were already implementing AI technology before the explosion of generative AI have an advantage over recent market entrants, who have an advantage over those who are still trying to determine how they will respond. Those who are just joining the revolution must quickly understand and overcome the barriers, some of them organizational, others technical.
Barrier No. 1 – Stash
Generative AI was built for the cloud, but for many companies, especially those in regulated industries, the most confined data remains securely on-premises and under lock and key. Therein lies the puzzle. Context is crucial to the effectiveness of language models, but many CDAOs are rightly concerned about exposing private data, their most valuable asset, to train models in the cloud. Even if privacy could be ensured, there would still be a concern that data could be inferred from model results.
Without fundamental data as critical context, companies will only train models that know little about it and therefore do little for it. Instead of providing a game-changing competitive advantage, models will only be able to achieve efficiency.
: Spend millions of dollars and several years to build your own LLM in a stash.
: Focus on machine learning algorithms to solve predictive and prescriptive challenges. Train models securely in the vault and employ the results to make sharp decisions and gain a competitive advantage. This solution facilitates quick wins for AI as the generative AI market matures to provide industry-specific language models to execute in the stash.
Barrier #2 – Data
(data availability, data management and data quality)
If your data is already highly secure, how available is it to generate strategic business value? Is it integrated with all environments? Is it managed, meaning you have control over it and that insights can be reliably generated from it? Have data resources been standardized to promote common interpretation? If the data is fragmented, if the data is not managed, if the multitude of non-standard data resources allows for variable interpretations, you will train AI models to be just another opinion, a minority report that is indefensible. One CDO who spent over 2 years on a generative AI journey rightly joked that AI doesn’t make magic.
The good news is that newfangled data platforms can aid overcome this barrier very effectively. The bad news is that the people and process elements associated with data management and data quality take time and effort. It’s a multi-year journey. I hope you’re on your way.
Barrier No. 3 – Artificial intelligence-based culture
CDAOs love to talk about a data-driven culture. Delivering data and analytical insights that impact a company’s bottom line and bottom line is a challenge in itself, but data-driven enculturation is much more challenging, and a culture of generative AI would be not only exponentially more tough to achieve, but indispensable. more purposeful.
Here’s what I mean. The connotation is that data analysis for decision-making is becoming an integral part of mission-critical workflows across the enterprise, but generative AI doesn’t just aid make decisions, it makes them. It creates. And this means that culture will not only need to understand data to make sound decisions, but will also need to be able to question the veracity of the decisions made by models. To achieve this, leaders will need to understand the technology and the models themselves, which means an education that data technicians will exchange for close involvement in validating and selecting the most appropriate business processes to automate using Gen. AI.
Continue to realize your data-driven cultural aspirations by continually improving your data literacy. Make them highly effective decision-makers with your analytics products so that success depends on them. Elevate the thinking of business leaders and individuals who are more data-driven and data-savvy. Invite them to your POCs to examine and validate the results of machine learning algorithms.
None of this will be straightforward. Revolutions rarely happen.