Artificial intelligence and machine learning have come a long way in recent years, with solid business cases, powerful algorithms, vast compute resources, and rich data sets now the norm for many enterprises. However, AI managers and specialists are still grappling with seemingly insurmountable organizational and ethical issues that are hamstringing their efforts, or even sending things down the wrong path.

That’s the conclusion of a recent in-depth analysis that looked at the pressures and compromises faced by today’s AI teams Read More

How to build reliable and useful machine learning systems

Machine Learning projects are known to fail frequently, according to Gartner 85% of all AI projects fail and even 96% deal with problems. When it comes to new technologies a high degree is normal, but these numbers are alarming. That might be that requirements for machine learning are not met, no value is added or the model did not make it to production for engineering reasons. Often it is possible to identify potential problems beforehand. This article is about early identification of such pitfalls based on my experience in applied machine learning for the last five years. Read More

Organizations need diverse teams, executive sponsorship and fewer proofs of concept for the strongest AI programs.

Nearly half of CIOs say they now employ AI or intend to within the next 12 months. But how to make AI a core IT competency still eludes most organizations. Boards of directors, CEOs and customers want to use AI to power real improvements in customer and employee experience.

“It might sound counterintuitive, but do as few proofs of concept (POCs) as possible.” Read More

Developing artificial intelligence and analytics applications typically involves different processes, technology, and talent than those for traditional software solutions. Executives who possess a solid understanding of the basics can ensure they’re making the right investments in their tech stacks and teams to build reliable solutions at scale. We’ve created an interactive guide to help. Read More

#strategy

Machine learning elevates the importance of people using the data. More data enables more automation, which requires more leadership.

… The big difference in a machine-assisted company is that instead of dictating decisions downwards, leaders use quantitative data-driven analyses to shape their qualitative decision-making.

Synthesis is a supremely human skill, same as storytelling. Read More

Most companies developing AI capabilities have yet to gain significant financial benefits from their efforts. Only when organizations add the ability to learn with AI do significant benefits become likely.

Only 10% of companies obtain significant financial benefits from artificial intelligence technologies. Why so few?

Our research shows that these companies intentionally change processes, broadly and deeply, to facilitate organizational learning with AI. Better organizational learning enables them to act precisely when sensing opportunity and to adapt quickly when conditions change. Their strategic focus is organizational learning, not just machine learning. Read More

Today, every technology startup needs to embrace AI and machine learning models to stay relevant in their business. Machine learning (ML), if implemented well, can have a direct impact on a company’s ability to succeed and raise the next round of funding. However, the path to implementing ML solutions comes with some specific hurdles for start-ups.

Let’s discuss the top considerations for getting ML models production-ready and the best approaches for a startup. Read More

In recent years, it’s become increasingly clear that Artificial Intelligence (AI) startups can scale to become $1 billion-plus companies. When it comes to innovation at the early-stages, there is a pressing need to differentiate between hype and actual potential for scale and impact. Today, many startups claim to be innovating through the use of AI. Whilst some succeed, others fail to deliver upon their promise. How does one go about cutting through the noise and identifying the AI startups that have the most potential for scale?

Ask four key questions:

• Is the company solving a high-value use case in a specific domain?
• Does the team have deep domain expertise along with access to unique datasets and other assets?
• Does the team have deep technical, AI, and data expertise?
• Does the team have a commercial balance with expertise in selling and working with enterprises?

Read More