AI can optimize business operations, enhance customer targeting, and increase profits.
A new wave of artificial intelligence introduces larger changes to businesses than ever before. Around the world, companies have already incorporated machine learning algorithms into their daily operations to identify patterns, enhance customer targeting, and increase profits. In this overview we will discuss the current AI adoption rate, future prospects, a variety of business impacts as well as barriers preventing companies from capitalizing on (benefitting from) new technologies.
AI has breached the material world. Whereas a few years ago the power of artificial intelligence was harnessed mainly for advanced analytics, microtargeting or predictive maintenance, now AI-powered systems enable pattern recognition and real-world mobility, as well as speech recognition and natural language processing. Processing power and data growth over the years has lead to a steady broadening of real-world AI implementation.
While most companies see the potential of AI-powered solutions for business, only 23% have incorporated the technology in a meaningful way, according to BCG. Large companies like British Petroleum, Airbus, Infosys, and Wells Fargo have already implemented AI solutions and reaped their benefits. Another 23% report to have launched one or more AI projects. Over 50% of the enterprises have not adopted the new technology, and 22% among them do not plan to do so in the near future.
Still, the annual research and development investments exceed $30 billion, and the projected ROI is expected to reach trillions of dollars. Internet giants are heavily investing in AI talents and technology. Research by Paysa revealed that Amazon is investing $228 million in the new AI positions, with Google ($130 million) and Microsoft ($75 million) lagging behind. The three companies offer AI-powered cloud services and expect them to drive the growth of their global platforms.
According to Venture Scanner, just in 2017 over 1,300 AI startups were funded raising a total of $38.6 billion. These startups are spread across 73 countries, with almost 900 headquartered in the USA. Some EU states, Australia, India, and China are not far behind.
Despite the disparities in the current AI implementation and investment, future expectations are consistently high across industries. According to a BCG survey, executives believe that artificial intelligence will have great impact on both their processes and offerings. Technology, Telecom, and Consumer sectors are leading the charge, with Public Sector representatives being the most cautious in their forecasts.
Operations-wise, enterprises predict the AI-powered solutions to bring significant improvements in:
– Information technology;
– Manufacturing, supply chain management, and R&D;
– Marketing, sales, and customer service;
– Strategic planning, financial management, and accounting.
A simulation by McKinsey Global Institute demonstrates that the companies that fully adopt AI technologies will double their normal profits by 2030 at the expense of competitors. A global-scale extrapolation estimates the AI adopters’ profit at $1 trillion by 2030, which makes up 10% of the current profit pool. Analysts at McKinsey suggest non-adopters might lose profit and ultimately fail without joining the AI race.
While 80% of the businesses believe AI to be a strategic opportunity, over 40% expect the technology to turn into a risk. Executives realize that artificial intelligence will offer sustainable competitive advantage and reduce costs, but also increase competition among both incumbents and entrants.
Investment Requirements, Costs, and ROI
The cost of AI project development and implementation varies from several hundred dollars to several million dollars depending on its complexity, use case, industry, and available resources. Data collection and structuring, human resources, and vendor costs – these are the three primary expenses for machine learning projects.
AI development and implementation require significant time investments. According to research by Deloitte, the average time before launch is 12 months. Experienced data scientists and well-structured data can reduce the implementation timeline to three months, while new concepts that require data sourcing can take up to three years to develop.
The AI project’s ROI depends on the business’ success in overcoming major development and investment challenges. According to M-Brain, these include:
– Data collection, storage, and processing tools and methods;
– Selection of the vendor with a required level of AI expertise;
– Laws and regulations compliance, especially for Finance and Healthcare;
– Technical team and toolset selection for project implementation.
ROI of the majority of machine learning projects in the first years ranges from 2 to 5 times the cost of development. Business impact of AI implementation projects is estimated between $250,000 and $20 million. Considering the nature of AI systems, the long-term ROI can grow exponentially and exceed billions of dollars in a few years. Walmart and Netflix are among the companies that expect the growth of revenue to surpass $1bn.
AI adoption stimulates business growth and revenue increase through creating a competitive advantage and improving business strategies. AI-powered market intelligence, pattern recognition, and customer segmentation tools ensure personalized and targeted customer engagement. Business outcome predictions enable real-time fine-tuning of forecast models, thus promoting corporate growth and securing higher profits.
Increased efficiency achieved by AI adopters reduces routine business expenses. Machine learning algorithms enhance process, labour, and value chain efficiencies. However, one of the challenges arising with AI adoption is the integration of human employees and artificial intelligence programs.
For manufacturers, AI offers significant capital savings. By optimizing production inputs businesses can minimize production costs. Predictive maintenance is another way to reduce expenses by monitoring asset conditions and estimating optimal inspection schedule and remaining lifetime. AI-powered quality control boosts product yield, limits warranty costs,
and maintains superior product quality.
Machine learning implementation has already delivered strong financial and business outcomes in five major areas:
1. Detection and prevention of security breaches throughout the Internet, mobile and IoT devices;
2. Customer-oriented business process optimisation and personalization;
3. Detection and prevention of transaction, identity, and health fraud;
4. Detection and review of regulation and contractual compliance;
5. Research into new AI applications, including autonomous driving and precision medicine.
Despite the high expectations and potential business benefits, most companies have not incorporated AI-powered technologies apart from tinkering with chatbots. There are multiple reasons for this disparity on technological, operational, and financial levels that stem from the lack of understanding of the costs and requirements of AI adoption.
Enterprises delay the AI adoption because of ambiguous business cases and profits. Without a clear understanding of business benefits among senior management, competing investment priorities win over machine learning initiatives.
According to a review by Narrative Science, even among businesses that plan to deploy AI, 42% cannot estimate the timeline for adoption. Moreover, 31% of businesses do not track AI implementation ROI or see no return on investment. As a result, many AI projects get stuck in pilot stages, while product deployment could drive the expected business results.
Without identified business cases, most top managers do not support the transition to new business models through machine learning implementation. Moreover, the growth of information security breaches causes AI security concerns. Large data volumes required for AI training and operation could be leaked by a cyberattack. Personal, financial, and biometric data breaches and modifications can lead to catastrophic consequences on a global scale.
Additionally, reliance on AI-powered systems diminishes corporate resilience. Automation bias reduces the critical view of machine learning solutions and can engender the loss of critical personal and professional skills among employees and management.
Even the most sophisticated machine learning algorithms require massive volumes of data to mature and to offer significant business value. Enterprises that quickly benefit from AI adoption are those that have accumulated enough historical data. High-quality data must be well-structured, available without breaking privacy regulations and unbiased. Negative data is often more valuable than positive, as it enables the AI to predict future issues.
Silo-stored data is difficult to integrate during the AI training process. Without an established analytical infrastructure, data collection and preparation is a more complicated and time-consuming process than the selection of the machine learning algorithm.
The fierce competition over AI experts is driving up the salaries and draining corporate budgets. Pioneers of machine learning adoption build the AI-savvy workforce through on-the-job and formal training, as well as hiring university graduates and specialists from other organizations. Outsourcing AI-related skills is most popular with businesses that cannot afford to invest heavily into machine learning, and wish to adopt it with minimal expenses.
Microsoft, Google, Apple, Amazon, Facebook, and other international corporate giants have started investing in AI technologies early and now are reaping the benefits of this decision. Progressive thinking companies convert AI expertise directly into revenue by providing services to other enterprises or enhancing internal operations. The cost of delaying an AI adoption could ultimately push the lagging companies out of the market. Despite the high initial investment, lengthy implementation period and internal barriers, AI possesses the power to transform businesses across all industries.
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