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Research & Innovation

Research That Actually Means Something

Most industry research is written for people who already agree with the conclusion.

GoSmarter covers academic research, industry studies, and data-backed analysis that is relevant to metals manufacturers. The question is always the same: does this help a production manager make a better decision?

Topics include:

  • AI adoption in manufacturing: what the adoption data shows, not what vendors claim
  • Scrap reduction and yield research: measured outcomes from real facilities
  • Workforce and skills studies: the human side of automation
  • Industry 4.0 implementation research: what separates the pilot projects from the deployments that stick
  • Environmental impact studies: scope 1 and 2 emissions in metals production

Where research findings have direct implications for how GoSmarter builds its tools, that is noted explicitly. Where the evidence is mixed or the sample sizes are small, that is noted too.

Good decisions come from clear data. That is what these posts aim to deliver.

Removing AI bias for better decision making

It is difficult to deny that humans make biased decisions. Unconsciously we all make choices that are based on prejudices and flawed associations. This bias that we introduce to our business decisions can trickle through entire organisations, from recruitment to market segmentation. AI, with its lack of consciousness, human experience and gut feelings, has the potential to remove bias from businesses, and yet all too often AI is found to exhibit the same biases - link no longer works that we do.

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How AI in marketing is enhancing B2B sales

At the end of 2018, Salesforce - link no longer works reported that adoption of AI by marketers had grown by 44% last year, and that adoption rate is unlikely to slow anytime soon. With marketers showing "extensive interest" - link no longer works in exploiting AI for their roles, more and more tools are becoming available to support companies on their journey to smarter marketing. AI is transforming the way companies market their products and services to other businesses, streamlining processes at all levels of the sales funnel.

Read More: How AI in marketing is enhancing B2B sales

How can you attract the best AI talent from a limited pool?

According to research by MMC Ventures - link no longer works, demand for AI talent has doubled in 24 months, faster than the talent pool can keep up. As of 2019 there was one AI professional for every two available jobs, so building a team of AI developers for your organisation requires both focused recruitment and a sound retention strategy.

Read More: How can you attract the best AI talent from a limited pool?

Let your business strategy drive AI adoption

To reveal the tactics and behaviours of companies that are getting the most out of AI, MIT Sloan Management Review and Boston Consultancy Group undertook a survey of more than 2500 executives alongside 17 expert interviews in their 2019 report, Winning with AI - link no longer works. One of their findings was that while 9 out of 10 respondents saw AI as an opportunity for their company, the perceived risk of AI is on the rise, with 45% of respondents reporting perceived risk from AI (compared to 37% in 2017).

Read More: Let your business strategy drive AI adoption

Decoding the hype around AI

As the powers and capabilities of Artificial Intelligence (AI) expand and evolve, the same cannot be said for the general understanding of the topic. This has resulted in AI becoming a blanket term that gets misused and thrown around for all things, including things that it’s not. People also have very unrealistic expectations of what AI can do leading in some cases to fear and paranoia over things like potential world domination, in others, disillusion when the AI doesn’t perform to the high standards they were hoping.

Read More: Decoding the hype around AI

Can AI outperform medical professionals in diagnosis?

Last year the Guardian - link no longer works reported that AI is 'equal to humans in medical diagnoses' when interpreting images, referring to a study published in Lancet Digital Health. The study revealed that AI 'deep learning' systems were able to detect disease 87% of the time and correctly gave the all-clear in 93% of cases (the equivalent success rate in healthcare professionals is 86% and 93%). This means that AI in healthcare is on track to support medical professionals, leading to faster, cheaper diagnoses and drug development. This will allow healthcare professionals to achieve more with their time and help more people.

Read More: Can AI outperform medical professionals in diagnosis?

7 tips for building a data culture that will strengthen your business

Data analytics has taken off but not everyone is on the same page. While some companies are already making waves with data science, others are still struggling with the basics. Curating a healthy data culture is ever more important now to prevent the gap from growing between those who are embracing analytics and those who are lagging behind.

Read More: 7 tips for building a data culture that will strengthen your business

Project management: Are you backing the right AI projects?

As an executive with an influence over whether your company implements AI and which projects it embarks on, there’s a lot of pressure on you to be successful. The future of AI within your company could rest on you on how your chosen projects perform.

Read More: Project management: Are you backing the right AI projects?

Do you really need big data to start using data science?

All businesses generate data. Even the smallest business has access to hundreds, if not thousands, of interesting data points that they could explore. But it is not uncommon for business owners to think their data is small, inferior and not yet worth analysing. This is where they are wrong every time. Starting small is the best thing you can do, so we say, the time to start your first data science projects is now.

Read More: Do you really need big data to start using data science?

How to get AI to work for your business and enhance operations

Enterprise cognitive computing is the application of AI to enhance business operations. It has a wide range of applications including call handling, fraud detection and maintenance scheduling. ECC systems automate repetitive tasks and improve efficiency through fast search and information processing.

Read More: How to get AI to work for your business and enhance operations

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