Published on November 20th, 2017 | by Emergent Enterprise0
5 Reasons Businesses are Struggling with Large-scale AI Integration
[avatar user=”floatee” size=”1″” align=”left” /] E-E says: It is becoming increasingly obvious that artificial intelligence is going to infiltrate virtually every part of our lives. The opportunities for business will grow quickly as AI is used to increase productivity, save money and monitor employees. Yes, keep an eye on employee behavior and actions. But is business ready for AI? For effective, appropriate and even legal applications of artificial intelligence in the workplace, companies will need to have the right policies, budget, technology and strategy in place. Is your organization ready for AI? Share thoughts below.
Artificial intelligence is an important vehicle for companies looking to automate processes, reduce the cost of operation, or fuel innovation. Despite the positive influence AI-supported activities have on business, a successful implementation won’t happen overnight. First you need a complete understanding of your business goals, technology needs, and the impact AI will have on customers and employees. The majority of employees face challenges or concerns relating to AI adoption, and that needs addressing.
The implication of successful AI adoption is far reaching for businesses undertaking full-cycle digital transformation, which places equal emphasis on automation, innovation, and learning. While employees may experience trepidation at the prospect of AI reshaping or eliminating day-to-day tasks, their productivity could actually increase because more of their time can be directed toward activities that produce value-driven business outcomes. No matter the role or the business unit, AI, automation, and machine learning are changing how work is performed.
As AI becomes pervasive, companies must face challenges head-on. Executives will need to consider the following five areas as they progress with digital transformation and move to invest more heavily in AI.
1. Legacy infrastructure
The adage “out with the old and in with the new” rings true for decision makers who are assessing whether their current infrastructures are intelligent enough to support today’s technology. AI-supported activities require ingestion of vast amounts of data; thus, infrastructure must be agile and scalable. Traditional structures like software-defined infrastructures (SDIs) aren’t necessarily the best option. While SDIs provide flexibility, the structure is limited by the source fixed source code and the administrator who is writing the scripts. More sophisticated AI algorithms and intelligence systems require smarter structures like AI-defined infrastructure (ADIs) and cloud-based networks that can quickly expand based on business needs.
Moreover, while neural networks have existed for decades, only now is massive computing power available at a reasonable cost, which in turn has helped increase the number of layers in these networks. Each layer adds more intelligence but also consumes enormous computing power, which used to be prohibitively expensive. More layers mean better outcomes.
2. The skills gap
AI is generating a demand for new skill sets in the workplace. However, currently, there is a widespread shortage of talent that possess the knowledge and capabilities to properly build, fuel, and maintain these technologies within their organizations. The lack of well-trained professionals who can build and direct a company’s AI and digital transformation journeys noticeably hinders progress and continues to be a major hurdle for businesses.
To mitigate this, businesses should look inward and enforce on-the-job training and reskilling. For example, LinkedIn just announced it plans to teach all its engineers the basics of using AI. With the proper staff powering AI, employees are able to focus on other critical activities and boost productivity creating a large ROI. If an enterprise’s digital transformation goal is for AI to become a business accelerator, it needs to be an amplifier of its people. It’s going to take work to give everyone access to the fundamental knowledge and skills in problem-finding and remove the elitism around advanced technology, but the boost to productivity and ROI will be worth it in the end.
3. Ethical dilemmas
While AI is still in early stages, ethical concerns abound. Both proponents and detractors of AI (Elon Musk most famous among the latter group) have focused on who wins and who loses when AI grows more prominent in business and daily life. A recent study that sought to better understand how AI and automation technologies are driving full-cycle digital transformation in various industry sectors found 62 percent of enterprises felt that a successful transition to AI-powered processes and workflows requires stringent ethical standards.
It’s critical that businesses develop guidelines and rules as adoption takes place. An ethical framework with buy-in from leadership will ensure products and services, processes, and employees are treated appropriately with respect to how AI is adopted, used, and expanded. Having moral standards or systems in place assures issues such as unemployment, bias, and inequality are carefully scrutinized as AI is added to the corporate environment.
4. Data abundance and availability
AI algorithms cannot properly execute without access to data. The more data available, the more accurate and effective AI will be. As systems evolve and more connections between networks, devices, and processes arise, colossal amounts of structured and unstructured data can be accessed.
Before deploying AI, IT teams and data scientists should collect, clean, and label datasets for machine learning algorithms to ingest to improve AI applications. Filtering through these large amounts of data is no small feat considering 80 percent of organizations’ data is unstructured. The better an organization can clean up its data, the sooner it can improve accuracy and expand use of the data. Over time, AI and machine learning will become smarter about analyzing data and making discoveries quickly that can positively affect businesses’ bottom lines.
5. Budget concerns
Deploying AI effectively takes a vast amount of time, resources, and budget. While AI cuts costs in the long run, it typically requires significant investment at the start. Enterprises are investing millions of dollars, and companies of other sizes invest substantial sums ranging from tens of thousands to hundreds of thousands. However, running extensive projects with unstructured data could cost your organization up to $500,000, so costs are comparable.
Businesses that haven’t yet allocated budget for AI should start small by manually auditing the organization to streamline processes and free up employees’ bandwidth. This allows decision makers to clearly see which systems aren’t utilized effectively and which areas could benefit from technology down the road.
The future of business requires artificial intelligence. But AI is also the future of innovation. AI needs its human creators to succeed in order for the technology to become more useful. While some have already adopted AI applications, others are still lagging, which is understandable considering the challenges businesses face during this process. However, once these barriers are overcome, enterprises will finally see how AI can drastically revolutionize businesses, improve processes, and increase employee productivity at scale in the coming years.