Investment and interest in Artificial Intelligence (AI) is now white hot as we close out the second decade of the 21st century. Interestingly, AI is now almost a passé term. Machine Learning and advanced pattern recognition is now gaining notoriety as the “new” approach to apply the concept of AI, having been successfully applied to many problems in academia and industry.
Several use cases have been identified delivering legitimate Return on Investment. These recent advances can be attributed to the presence of greater access to more data, coupled with new, more powerful computer hardware and processing power. For example, predictive analytics is in use by large industrial companies to predict future equipment failure to avoid unplanned downtime, helping these organizations achieve significant cost savings and up to 30% lower maintenance costs.
AI has now come of age. Behaviors can now be predicted once sufficient data has been collected, to then consistently predict and suggest responses and next steps, anticipated to deliver rewards to those who understand AI, and who recognize the role that it can play in today’s modern organizations. These concepts can now even be applied to the sales cycle.
Given the significant innovation now part of this field, I thought it might be helpful and interesting to present a summary of definitions, learnings, AI capabilities and what the industry pundits are forecasting. This knowledge should then help you to better take advantage of Artificial Intelligence, Machine Learning and advanced pattern recognition to potentially distance yourself from the competition.
- AI is not a specific single technology – it is best described as a loosely defined term that can refer to several technologies operating together, including Machine Learning, Algorithms, Natural Language Processing, Neural Networks and Deep Learning. One example of a sales productivity measurement application that leverages AI is Prodoscore, which uses the combination of the first three technologies (Machine Learning, Algorithms, Natural Language Processing) to visualize productivity improvements.
- AI is driving new tools and practices – concepts that were nearly impossible to conceive until within the last year or two, including its use in anomaly detection, cybersecurity, securities trading, medical diagnostics, customer satisfaction and in DNA sequences classification, just to name a few.
- AI functions best and has its greatest impact – when technologies are narrowly focused on well-scoped problems.
- Machine Learning has become the main driver of AI adoption today.
- AI can be embedded in business process – helping to bring surprising insights to light. Firms in the insurance industry (one example is GEICO) have made significant strides through the application of machine learning into production processes.
- AI is one of the Top 5 Investment Priorities for CIOs today
- Preconceived myths about AI can stall adoption – as a technology concept that has been around for over 50 years, baseless fears, cultural anxieties, misinformation and myths need to be dispelled before an organization can truly objectively evaluate what role AI might or might not play. The importance of this concept can’t be overstated.
- Researchers predict AI will create net new jobs starting in 2020 – it is expected that by this year, AI will break through the threshold of generating more jobs that it takes away. The next job gain / (loss) from AI by 2025 is forecast to exceed 2 million net-new jobs. Not all industries will be impacted equally; those with greatest gains are forecast to be: healthcare, public sector and education are forecast to see continuously growing job demand.
- Industry analysts forecast AI augmentation will become a big business – current forecasts estimate that AI could generate $2.9 trillion in business value by – and recover 6.2 billion hours of worker productivity by the end of 2021.
- The Impact on AI will be wide in scope – it is forecast that by 2022, one in five workers engaged in mostly nonroutine tasks will rely on AI to do their jobs.
- AI-enabled decision support is the greatest contributor to business value creation – this capability already overshadows AI process automation and is expected to continue to do so over the next 10 years.
- Major trends are pushing AI forward – such as communications via Natural Language Processing; deeper, broader integration within existing applications are becoming more widespread.
- Tangible ROI can be achieved with AI – provided focus is placed on the business problem AI solves, and use cases pointing to how it can be solved.
- A wide range of companies have begun their AI initiatives – vendors are rapidly introducing new AI features in existing products. Capital investments are leading to a tremendous explosion of new AI startups. AI isn’t new, but the expanded hype we see today is new. Leading industry research firms revealed that inquiries about AI have tripled in the last 18 months.
- AI and Machine Learning require a willingness to experiment and fail – as a new and evolving technology and concept, experimentation is needed by organizations to test these capabilities when applied to real business problems. Agile approaches to AI will make the biggest gains. Prodoscore does this for you, so you don’t have to.
- Augmented Analytics is the future of Data Analytics – this discipline leverages machine learning and natural language processing to yield accurate, actionable insights.
- AI will ultimately have an increasing role in industries with worker shortages – as a strategy to amplify knowledge in situations where workers are limited. Human expertise and skills are a major limiting factor for AI, given its reliance upon acquiring knowledge from those who understand a topic, or the processes required to complete a task. With no one or no data to “learn” from, AI will stumble. But, once AI has been launched and knowledge baselines have been attained, then AI can greatly improve productivity.
- New AI opportunities exist in voice applications – significant revenue opportunities exist for vendors that pilot voice recognition and command-based applications.
- Fusing AI and IoT technologies will lead to significant new opportunities – this combination has the potential to catalyze new digital value creation (e.g. GSuite, Salesforce and Vonage, among others).
- AI and Machine Learning technologies are actively driving digital transformation – these innovative techniques are driving efficiencies, greater adoption and improved employee experiences across many different industries.
AI, like so many other technologies, has the potential to improve our lives, increase efficiency and expand the scope of what a worker can accomplish in a single day. Or, it could be used in nefarious ways to create unsustainable business practices, or to support criminal targeting of victims to steal from.
It is up to us as a society to decide what is acceptable, and what isn’t. But, without some experimentation and willingness to better understand what the true opportunity of AI is, we’ll never know what we might have been missing.
About Your Guest Blogger:
Gordon Benzie is an accomplished marketing and communications strategist with over 20 years of experience at both global enterprises and startups developing and executing programs that drive results. As an avid technologist, Gordon has a deep understanding of today’s digital transformation, and how its enabling technologies are impacting industrial and engineering companies. Contact Gordon through LinkedIn.