13 experts from Forbes Technology Council share common mistakes to watch out for when implementing AI.

• Adopting Too Many Tools At Once
• Not Having A Clear Objective
• Not Having A Single Source Of Truth
• Not Analyzing Enough Data
• Incorrectly Structuring Datasets
• Implementing Siloed Solutions
• Not Having The Right Size Team
• Not Doing The Necessary Groundwork
• Assuming AI Is A Catch-All Solution
• Misidentifying Both The Problem And The Best Solution
• Implementing AI For Its Own Sake
• Implementing Solutions Without Sufficient Data
• Thinking AI Is ‘One-Size-Fits-All’

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Amid the growth of the Artificial Intelligence (AI) and big data market, business leaders are starting to realize that AI – like any other business function – requires structured strategy, planning, training, and execution to successfully implement.

Many companies working on digital transformation have amassed huge data archives but lack the ability to extract the information they need to unlock new synergies and growth paths. This bottleneck is visible in most companies I meet. The transition from data collection to fully formed, AI-driven growth strategy is a multi-step process that can appear overwhelming to those without clear guidance. Read More

The year 2021 may well see a turn in the trajectory of AI. As DataRobot notes in its predictions for the new year, “Within the enterprise, we finally expect a wholesale move from ‘Experimental AI’ to ‘Operational AI,’ as organizations must move out of the lab and beyond pure experimentation.” In fact, Gartner is forecasting that 75% of enterprises will shift from piloting to operationalizing AI by the end of 2024, driving a 5x increase in streaming data and analytics infrastructures.

From our perusal of the publications offering predictions in this space we see agreement that AI will be a bigger disruptor to business than the Internet was. Again, we ask: why? The simple answer may be that it offers such a wealth of opportunities to make better decisions, unearth hidden relationships previously unnoticed among critical data, and offer the agility and automation that our speed-obsessed times demand for competitiveness. AI is the new horsepower. Read More

This is the fourth Synced year-end compilation of “Artificial Intelligence Failures.” Our aim is not to shame nor downplay AI research, but to look at where and how it has gone awry with the hope that we can create better AI systems in the future. Read More

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Advances in artificial intelligence (AI) go beyond algorithms—they also include associated methods to aggregate and parse new sources of data and use it to develop new applications in an expanding range of industries. We spoke to subject matter specialists and industry experts to learn about the breakthroughs expected in each of these aspects of AI in 2021. Read More

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These are exciting times for the artificial intelligence community. Interest in the field is growing at an accelerating pace, registration at academic and professional machine learning courses is soaring, attendance in AI conferences is at an all-time high, and AI algorithms have become a vital component of many applications we use every day.

But as with any field going through the hype cycle, AI is surrounded by a saturation of information, much of which is misleading or of little value. … In this post, I will try to provide a few guidelines for writing good AI pitches based on my experience covering the field for several years. This is mainly a guide for the PR people who are writing AI pitches. But it should also serve reporters, who can use it to tell a good AI pitch from one that contains too much hype and too little value. Read More

Algorithmia has talked with thousands of people in various stages of machine learning (ML) maturity and in various roles connected to ML. Following the report we published last year , we conducted a two-prong survey this year, polling nearly 750 people across all industries from companies actively engaged in building ML lifecycles to those just beginning their ML journeys, finding that more than two-thirds of those who responded said their AI budgets are growing, while only 2 percent are cutting back.

• 40 percent of companies surveyed employed more than 10 data scientists, double the rate in 2018, when Algorithmia conducted its previous study. 3 percent employed more than 1,000 data scientists.
• Many respondents said they’re in the early stages, such as evaluating use cases and developing models.
• Many struggle with deployment. Half of those surveyed took between 8 days and three months to deploy a model. 5 percent took a year or more. Generally, larger companies took longer to deploy models, but the authors suggest that more mature machine learning teams were able to move faster.
• Scaling models is the biggest impediment, cited by 43 percent of respondents.

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