Since 1956, when it was introduced as an academic discipline, AI has generated both optimism and disappointment. However, it has been on a relatively upward projection since 2000, finding particular vigor in leveraging statistical approaches to machine learning, which in turn has rendered many previously used tools and schools of thought obsolete.
As the field of AI continues to innovate, and machines and systems become more capable, technological solutions that used to be considered as futuristic AI, like optical character recognition, have become routine — effectively losing their “AI” status. Other technologies yet to be conquered — like driverless cars, and the artificial re-creation of human speech — are still being developed as AI.
Many futurists have talked about the dangerous possibility that AI machines will eventually take control of humanity and destroy the world. Even though most of these prognostications mix speculation and superstition, this school of thought has persevered — consider the news of some successful Turing Test exercises.
However, many AI researchers and scientists have refuted this stance, saying that, ultimately, AI is simply a very effective tool for processing, analyzing and comprehending massive amounts of actual human data.
One thing is certain: Direct or indirect AI adaptation for businesses and individuals will continue to proliferate. And the journey is going to be exciting, as we continue to unravel what is possible with this promising technology. AI-based solutions are already being used to tackle pressing problems in business to change the business world. Here are a few ways it’s doing that:
1. Making truly autonomous processes possible
While most AI technology for businesses still needs a substantial degree of human supervision and intervention, companies are working to develop technology with human-like cognitive ability, enabling AI systems to truly be trusted to take on autonomous tasks with minimal supervision.
This technology has long been a focus of research in the world’s most cutting-edge research programs. Southern California AI disrupter Beyond Limits, for example, is adapting technology utilized by NASA to perform something like the unmanned space mission to Mars. These types of AI solutions can tackle complex problems in energy, transportation and medicine, by offering lightweight AI that isn’t reliant on massive infrastructure.
Such AI has the processing speeds and cognitive ability that can parallel an actual human agent’s reasoning and problem-solving process.
2. Ushering in the era of “big intelligence”
The convergence of better programming through high performance computing (HPC) and big data means that computers can now crunch a massive amount of data and use AI programs to learn from and process that data into useful, actionable business insights.
Dubbed by hardware companies like PSSC Labs as “big Intelligence”, the servers and storage harnessing the power of these new computing platforms are allowing companies to process consumer and market data coming from an astonishing array of sources.
Real-time or near real-time processing means businesses can keep constantly up to date with better information, better recommendations and more predictive power, to optimize business decisions.
Timing is an important factor for any business, as there is a natural ebb and flow determined by many factors, including location, season, holidays and adjacent industries. Grasping this cadence is essential for business owners to succeed.
However, even years of experience doesn’t guarantee that any business can accurately assess staff, stock and other timing-related needs accurately. A sudden boom in a particular city, for example, might prompt more traffic to certain businesses than they can handle. Poor weather could dampen the holiday rush in tourist towns, resulting in lower staff needs.
For example, Ximble is looking to roll out features within its scheduling platform that use AI and machine learning to analyze traffic and retail data for businesses to create automated and optimized staff schedules. Similar features will likely emerge from inventory-management companies, as well as delivery, fleet management, and other similar business SaaS providers.
4. Automating lead generation
The cost of customer acquisition via lead generation is a critical part of every business. For many businesses, customer acquisition requires extensive cold-calling, trial and error and exploration of multiple channels. Artificial intelligence promises to not only increase the relevance of lead-generation campaigns, but also dramatically reduce their cost, in both time and money, by automating and learning from simple user inputs.
By incorporating tag words, a rudimentary search for leads can automatically be implemented. Sugarbot is one of a handful of startups trying to crack this code. As the user within the business makes relevant selections from the results, a lead-generation system incorporating AI can learn, make adjustments and produce continually better and more relevant results within seconds.