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Without clarity in metrics, it’s impossible to do meaningful experimentation. Experiments allow AI PMs not only to test assumptions about the relevance and functionality of AI Products, but also to understand the effect (if any) of AI products on the business. Don’t expect agreement to come simply.
While generative AI has been around for several years , the arrival of ChatGPT (a conversational AI tool for all business occasions, built and trained from large language models) has been like a brilliant torch brought into a dark room, illuminating many previously unseen opportunities.
This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers. Counter claims?
MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. the monitoring of very important operational ML characteristics: data drift, concept drift, and model security).
Our mental models of what constitutes a high-performance team have evolved considerably over the past five years. High performance back then generally focused on delivery — a contrast to previous generations of IT where business and IT alignment was an issue, and teams struggled to deliver with waterfall project management practices.
Cloud maturity models are a useful tool for addressing these concerns, grounding organizational cloud strategy and proceeding confidently in cloud adoption with a plan. Cloud maturity models (or CMMs) are frameworks for evaluating an organization’s cloud adoption readiness on both a macro and individual service level.
Rick Boyce, CTO at AND Digital, underscores how a typical IT project mentality toward DevOps can undercut the CIO’s ability to deliver on businessobjectives. CIOs may mistakenly underinvest in practices that improve user experiences, increase alignment with business stakeholders, and promote a positive developer experience.
So, to maximize the ROI of gen AI efforts and investments, it’s important to move from ad-hoc experimentation to a more purposeful strategy and systematic approach to implementation. Here are five best practices to get the most business benefit from gen AI. This may impact some of your vendor selections as well.
While many organizations are successful with agile and Scrum, and I believe agile experimentation is the cornerstone of driving digital transformation, there isn’t a one-size-fits-all approach. CIOs should consider technologies that promote their hybrid working models to replace in-person meetings.
IT’s mission has transformed — perhaps so should its brand Another approach I recommend is to rebrand IT and recast its mission to modernize its objectives, organizational structure, core competencies, and operating model. These objectives are not new but go beyond IT’s traditional operating responsibilities.
Key strategies for exploration: Experimentation: Conduct small-scale experiments. Clear success criteria: Ensure exploitation delivers incremental business value. It shows the importance of eliminating investments that no longer align with core businessobjectives. Use minimum viable products (MVPs) to validate concepts.
Failure to align technology capabilities with business goals can result in a wasted investment in technology that doesn’t support businessobjectives. Foster a culture of innovation: Digital transformation requires innovation and experimentation, and thus a culture for embracing new technologies and ideas.
After transforming their organization’s operating model, realigning teams to products rather than to projects , CIOs we consult arrive at an inevitable question: “What next?” Splitting these responsibilities without a clear vision and careful plan, however, can spell disaster, reversing the progress begotten by a new operating model.
Improving employee productivity and collaboration is a top businessobjective, according to the 2023 Foundry Digital Business Study. They are expected to make smarter and faster decisions using data, analytics, and machine learning models. Here are their top tips. Caution is king, however.
Belcorp operates under a direct sales model in 14 countries. As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.
He plans to scale his company’s experimental generative AI initiatives “and evolve into an AI-native enterprise” in 2024. This vision represents a fundamental shift, positioning AI as an integral part of our business fabric rather than just an add-on. That’s the case for Yi Zhou, CTO and CIO with Adaptive Biotechnologies.
Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platforms assist with a multitude of tasks ranging from enforcing data governance to better workload distribution to the accelerated construction of machine learning models.
It’s no secret that training AI models is an energy-intensive and large dataset-dependent endeavor and, to corroborate this, researchers at the University of Massachusetts Amherst performed a lifecycle assessment on large, widely accepted AI models trained on the vast datasets needed to achieve accuracy.
Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.
As they look to operationalize lessons learned through experimentation, they will deliver short-term wins and successfully play the gen AI — and other emerging tech — long game,” Leaver said. Determining the optimal level of autonomy to balance risk and efficiency will challenge business leaders,” Le Clair said.
Transformational CIOs continuously invest in their operating model by developing product management, design thinking, agile, DevOps, change management, and data-driven practices. Focusing on classifying data and improving data quality is the offense strategy, as it can lead to improving AI model accuracy and delivering business results.
In todays digital economy, businessobjectives like becoming a leading global wealth management firm or being a premier destination for top talent demand more than just technical excellence. Enterprise architects must shift their focus to business enablement. The stakes have never been higher.
Rosen sees a lot of experimentation without a clear sense of direction, from companies that don’t have a clear idea of what AI projects will match their business needs. AI projects should align with genuine businessobjectives and focus on delivering measurable results that support long-term strategic goals.”
Here are six steps for CIOs leading this evolution in their digital operating models. In addition, CIOs must consider how to realign program managers, project managers, and the project management office (PMO) to a different operating model. Said another way, not everyone will get what they want, creating detractors.
If a CIO can’t articulate a clear vision of how technology will transform the business, it is unlikely they will inspire their staff. Some CIOs are reluctant to invest in emerging technologies such as AI or machine learning, viewing them as experimental rather than tools for gaining competitive advantage.
According to Gartners 2025 Leadership Vision for Enterprise Architecture , several key missteps are preventing EA from delivering the business impact it should. The Solution: Enterprise architects must redesign their operating models to support federated decision-making. Heres what EA professionals are getting wrongand how to fix it.
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