According to Workday’s co-founder and co-CEO, Aneel Bhusri, clean and reliable data plus applicable context are necessary for AI to deliver on user intent. To support this philosophy, Workday announced Illuminate AI technology, which allows customers to streamline tasks, support employees and transform business processes.
CHROs need to be curious and proactive in exploring cutting-edge tools and strategies—such as Illuminate—that can transform their companies’ operations.
Recruiter Agent: Building on recently acquired HiredScore capabilities, this agent automates hiring processes, sources passive candidates and recommends top talent.
Succession Agent: This agent facilitates planning by identifying future leaders and providing personalized development plans for high-potential employees.
Workday Optimize: This tool identifies process bottlenecks and inefficiencies—such as delays in onboarding—and helps resolve them automatically.
the analyst stressed the need for HR teams to adopt a problem-solving mentality by utilizing productive tools that address challenges effectively
Process automation is an increasingly popular tool for accelerating process execution, reducing errors and increasing efficiency
Process automation is handed off to technical experts who are well-versed in the tools available but aren’t necessarily concerned with the underlying process knowledge
Process documentation is a picture of the ‘now’ of your business activities. It’s the understanding of why things get done as much as what those steps are
In order to effectively update the tool, it would need to be pulled apart at the seams and examined in order to find the truly meaningful parts, then those elements stitched back together in a new, more streamlined solution. That’s a lot of work that shouldn’t need to be done, because someone, somewhere, already did it once. They just didn’t record it.
Assumed knowledge may be common sense to everyone in your process initiative now, but there’s no guarantee they’ll be the ones revisiting the procedures and practices in years to come
KM plays a critical role in supporting the rollout of generative AI. Organizational information and data on which an AI model is trained must be accurate, up-to-date and well organized. The adage of ‘rubbish in, rubbish out’ still applies. KM processes ensure the integrity of AI model data inputs, impacting the outputs. For AI and machine learning to work well in organizations, they must be implemented using KM principles.
Effective KM allows companies to leverage their knowledge assets to respond quickly to market changes and customer demands
The shift to remote working following the recent pandemic has created a need for improved knowledge sharing and collaboration across geographically dispersed teams. KM is essential for facilitating digital collaboration and creating opportunities for connection in the digital workplace
Additionally, the focus on AI has further highlighted the importance of KM in leveraging technology to enhance business processes.
AI and machine learning are set to play a pivotal role in the future of KM.
The integration of AI in KM systems will also enhance search capabilities, enabling more precise and relevant information retrieval.
The shift towards cloud-based KM systems will continue, offering scalability, cost-effectiveness and ease of access
Organizations will prioritize identifying, mapping and retaining critical knowledge to mitigate the risks associated with employee (and freelancer) turnover and knowledge loss.
As KM systems store and process sensitive organizational information, data privacy and security will become top priorities
The first is data privacy. These large language models [LLMs] can learn from your data. And if your employees are putting sensitive documents into external consumer services, they’re exposing them to the world because the model will be trained on that data.
The second is customization. A huge priority for us is the ability to tailor a model and the system around it to a specific enterprise’s architecture, systems, and data platforms—and doing all of that integration to make it fit together seamlessly.
Finally, our platform is not locked into one cloud. If you’re a CTO or a CIO and you’re buying technology for your company, you really don’t want to get stuck in one proprietary environment.
I think what has helped us succeed in the enterprise world is the fact that we’re only focused on enterprise. We’re not trying to build a consumer service at the same time as we’re trying to build this enterprise platform. We only do one thing. And that’s enterprise.
So you need to prioritize access to the global market and speaking the native tongues of people worldwide.
One of the barriers enterprises face when adopting this technology is around training. People need to become familiar with the technology for it to be productive.
The second is privacy. There are real and genuine concerns around data leakage, exposing your data, and employees using consumer services that don’t protect data.
I think the next big thing in AI is going to be agents, or models that can integrate into the systems of an enterprise
At the moment, the model can just respond to your commands. And if you have a question, it can give you an answer. But in the future, the model’s going to be able to do much more and actually carry out tasks on your behalf.
this is all about building “something that actually adds value across the business” – this doesn’t mean not being innovative or coming up with creative solutions, but about making sure that HR is truly designing for the business.
when the HR team builds anything “we map it out as if it were a new feature of TravelPerk”.
we do user acceptance testing and co-create with the community to make sure we’re building something that adds value. We learn, test, iterate and then roll out”.
What worked for less than 100 people won’t necessarily work in future,” so “we have to make sure that the business grows with your people”
“We’re investing in manager development and effective leadership development, because they have a disproportionate impact on the rest of the business.
“We’ve done that by automating some of the mundane tasks that they do in the background, which means they can spend more time offering our customers a much better human experience.”
“With AI, we’re thinking about how it can power our teams in the back end, but still allow humans to make the right decision in the front end.”
When this happens frequently, the HRBP unintentionally becomes more of an HR workflow admin assistant.
To get things on track and empower HRBPs to grow into the strategic role you hired them for (and what they came on board to do), look to:
accept and encourage them to become business consultants, not just advisors or general admins, and support them in developing strong relationships with business leaders and the rest of HR
build the level of HR business partner capabilities they need to do that
organize their roles in new ways, and communicate clearly how you expect them to operate and contribute.
Why? Because by its very definition, Systemic HR transforms HR from a siloed service provider into an integrated, consultative function that tackles a company’s most pressing business challenges.
According to our research, only 11% of companies operate a truly Systemic HR function,
However, to achieve this, you must be prepared to both pose and find answers to questions such as:
What are my new-style HRBPs’ specific accountabilities?
What does success look like?
How will our newly-energized and skilled-up HRBPs interact with managers and leaders?
This new approach to the HRBP also centers on supporting their participation in cross-functional projects so as to develop a deeper understanding of its multiple business units and achieve a truly holistic view of the organization.
Their paper identifies two phases, investment and harvesting, in the life cycle of a historically transformative technology
Such an evolution has been has been pithily captured in what’s become known as Amara’s Law: We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.
AI has been overestimated since it emerged as a promising area of computer science research in the 1950s with the aim to develop intelligent machines capable of handling a variety of human-like tasks. But after a couple of decades of unfulfilled promises and hype, AI went through a so called AI winter of reduced interest and funding that nearly killed the field in the 1980s.
AI was reborn in the 1990s as a data-centric discipline. Instead of trying to precisely express as software human-like intelligence, the field embraced a statistical approach based on searching for patterns in vast amounts of data with increasingly powerful supercomputers and sophisticated algorithms
The WSJarticle points out that questions are being raised “about whether AI could become commoditized, about its potential to produce revenue and especially profits, and whether a new economy is actually being born.
“That difference is alarming, but what really matters to the long-term health of the industry is how much it costs to run AIs.”
“What’s the number one factor governing the pace of technological change?,” he asked. Experts often cite R&D spending or a country’s net brain power, — “the fallacy that all it takes for the next big thing to transform our lives is for it to be invented.”
We’re all susceptible to this one kind of tech B.S. “Tech is, to put it bluntly, full of people lying to themselves. As countless cult leaders, multilevel marketing recruits, and CrossFit coaches know, one powerful way to convince people that following you will change their life is to first convince yourself.”
“The last tech bubble gave us some deeply unserious ‘innovations’ like Web3 and the metaverse. But it also gave us a fourth industrial revolution, powered by the mobile internet, automation and artificial intelligence, the impacts of which will be playing out for decades to come.”
Creating and rolling out new tech without guardrails is a recipe for a world in which tech is as likely to supercharge our worst impulses, as it is to enhance our lives.
“The problem is that the current level of investment — in startups and by big companies — seems to be predicated on the idea that AI is going to get so much better, so fast, and be adopted so quickly that its impact on our lives and the economy is hard to comprehend. Mounting evidence suggests that won’t be the case.”
81% reconnaissent exiger plus de travail de leurs équipes ces dernières années, mais 96% des patrons attendent de l’IA des gains de productivité (donc une forme de soulagement de la pression sur les équipes). Problème : 47% des salariés ne savent pas comment s’y prendre et 77% déclarent que l’IA leur fait perdre du temps (donc une pression accrue).
100% des sondés sont confus sur ce qu’est une IA et ce qu’elle peut faire pour eux
L’IA est plus une idée qu’une technologie, c’est le principe d’utiliser des systèmes informatiques pour simuler l’intelligence humaine, notamment pour des tâches cognitives comme l’analyse, la déduction ou la création de contenus
Le Parlement Européen parle de SIA, de « systèmes d’intelligence artificielle » qui est une approche lexicale bien plus pertinente :
L’IA désigne la possibilité pour une machine de reproduire des comportements liés aux humains, tels que le raisonnement, la planification et la créativité.
L’IA permet à des systèmes techniques de percevoir leur environnement, gérer ces perceptions, résoudre des problèmes et entreprendre des actions pour atteindre un but précis.
Les systèmes dotés d’IA sont capables d’adapter leurs comportements (plus ou moins) en analysant les effets produits par leurs actions précédentes, travaillant de manière autonome.
e paradoxe actuel : tout le monde est d’accord pour dire que l’IA va révolutionner notre quotidien personnel et professionnel, mais personne ne s’embête à correctement définir l’objet et la nature de cette révolution.
l’approche symbolique qui repose sur des règles et référentiels permettant de créer des modèles logiques que l’on retrouve dans les systèmes experts qui permettent d’automatiser des tâches à faible valeur ajoutée (ex : triage de messages) ;
l’approche statistique qui repose sur des caractéristiques et associations pour décrire des données et créer des modèles discriminatifs que l’on retrouve dans les outils d’aide à la décision servant à analyser des grands volumes de données (ex : classification) ;
l’approche probabiliste qui repose sur la distribution de probabilités et de suppositions pour créer des modèles génératifs qui sont au coeur des modèles de langage utilisés pour générer des contenus ou donner la parole aux chatbots.
Dans les deux premières approches, nous parlons de technologies à usages spécifiques, car les règles et référentiels utilisés pour créer les modèles sont spécifiques à un domaine (ex : banque, assurance, santé…) ; tandis qu’avec une approche probabiliste, nous parlons de technologies à usages généraux, car les modèles sont créés à partir de données d’entrainement très variées
La réalité que l’on omet de nous présenter est que nous sommes face à un marché extrêmement fragmenté avec des modèles avec des éditeurs de toutes tailles et ambitions, et des briques technologiques plus ou moins prêtes à l’emploi qui sont quasiment impossibles à comparer.
Il y a d’un côté un démonstrateur technologique (l’équivalent d’un concept car) servant à exposer le savoir-faire de son éditeur qui est un tout petit laboratoire de recherche domicilié en Californie, donc qui ne doit rendre de comptes à personne ; et de l’autre des produits censés répondre à des besoins immédiats à la fois pour les particuliers et pour les entreprises (l’équivalent d’une voiture de série), édité par des entreprises cotées en bourse et surveillées de très près par les régulateurs du monde entier.
Comparer ChatGPT et Copilot ou Gemini revient à comparer le dernier concept car de Renault (Trezor) avec la Clio (le modèle de voiture censé être le plus accessible pour Mr & Mme tout le monde et pour les professionnels).
La première grosse différence vient donc de la finalité de la solution : ChatGPT est un démonstrateur, avec une version payante pour rentabiliser les frais de R&D et d’infrastructure, tandis que Copilot, Gemini ou Firefly sont des produits (cf. The AI Future Is Already Here, It’s Just Not Productized Yet).
Voici donc trois distinctions fondamentales qui empêchent de comparer de façon rigoureuse les IA entre elles :
la taille et la nature de l’éditeur (laboratoire de recherche vs. multinationale cotée en bourse) ;
la finalité de la solution (démonstrateur technique vs. produit grand public ou professionnel) ;
la localisation et les droits d’accès (services en ligne vs. assistant installé en local).
Il n’est donc pas logique de comparer modèles et services, car ils correspondent à des finalités différentes avec des usages plus ou moins sophistiqués.
Je pense ne pas me tromper en écrivant que l’IA étant un domaine vaste, complexe et pointu, l’évaluation d’un service ou d’un modèle génératif devrait être laissé aux soins de professionnels, et non aux journalistes ou experts auto-proclamés.
A McKinsey & Co study assessed more than 600 firms that had recently undergone digital transformations. It found that just 20 percent of the companies achieved more than three-quarters of the revenue gains they had anticipated
One-third of business leaders consider digital transformation to now mean “continuous reinvention” rather than episodic improvement.
“I think the most surprising thing that businesses learn when undergoing digital transformation is that technology plays a secondary role in it,” says Vaclav Vincalek, virtual CTO and founder of 555vCTO.com.
In digital transformation, technology is the easy part – changing organizational culture is the true challenge
Successful digital transformation demands proactive change management, yet many business leaders underestimate the extent to which their employees fear change,
Many focus on individual aspects of digital transformation but ensuring all business functions are interconnected is key to compliance,”
“Consumer expectations are higher than ever in the digital age. Companies must use data analytics not just to understand but to anticipate client wants and customize interactions.”
it’s also crucial to consider the employee experience, says Hartley. “Having an internal focus helps with getting buy in from all members of the organization, which can be a vital differentiator when it comes to return on investment.”
“Many business leaders expect a smooth, predictable transition, but the reality is often messy and filled with unexpected pivots.
“Technologies that seem cutting-edge today can become obsolete within months.
“Many transformations use red, green and yellow status reports to track progress. However, project reporters may sometimes fudge numbers to avoid delivering bad news.”
Last month, Bloomberg reported that Wells Fargo had fired more than a dozen employees in May for faking active work—specifically through the “simulation of keyboard activity.
In a new survey conducted by background check platform Checkr, nearly half of the 3,000 respondents said they would consider taking a pay cut in exchange for their employer not tracking their online activity
More than half of those surveyed—56%—said they believed their employer was monitoring their activity during the workday.
Criticsargue that tracking software can enable performative productivity, rather than true productivity, and that it can also be a reputational risk for companies
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