By Michael Allen, CTO at Laserfiche
Artificial intelligence (AI) and machine learning (ML) advancements have encouraged the development of breakthrough technologies. The most notable and recent development is generative AI, which powers ChatGPT and other prominent apps like Midjourney, DALL-E and Bard.
The potential for generative AI is vast and ever-expanding, and our everyday use of technology will change as a result. AI can enable us to automate tasks, take greater control of our workloads, and increase our productivity whether at home, school or work. To take full advantage of AI’s benefits, however, organisations must now start to employ AI at scale and impact enterprise-wide change.
Implementing AI at scale
Implementing AI at scale will require the use of AI across the enterprise to streamline operations, improve decision-making, and react faster to changes. Here are a few examples of business-critical processes that can be made more efficient and agile using AI technology:
- Customer acquisition, service, and strategy
- Product development and service delivery
- Financial analysis and capital management
Some of the challenges of deploying AI at scale include the creation of sophisticated data management frameworks that cover all components of a data lifecycle, and efficiently deploying AI solutions across platforms.
A content services platform is already foundational to an organisation’s business-critical content and processes, managing data lifecycles including capture, storage, integration, and disposition. These platforms are also often integrated with other core technologies to support the flow of information enterprise-wide, making it the ideal context in which to use AI and deploy it at scale.
Using AI within content services has the potential to uncover new ways to deliver business value, by accelerating the speed of business; improving monitoring and analysis of process data; creating new business capabilities and innovations; reducing error; and enabling the organisation to keep up with rapidly evolving markets.
The use of AI at scale: Considerations
Doing anything at an enterprise scale comes with a set of considerations that should be thoughtfully evaluated, but AI tools pose some new and unique challenges. AI technology processes vast amounts of data, creating increased risk of the intentional or unintentional exposure of sensitive and proprietary enterprise data. AI tools are not perfect: They can produce biased or incorrect responses to prompts, potentially leading to biased or unethical decision making. Therefore, with AI, security, privacy, and ethical considerations come first and foremost.
Data transparency: Data transparency must form a cornerstone of any development and operational process. To facilitate the responsible deployment of AI, businesses must employ a risk-and-benefits framework rooted in leading industry practices for assessing risk and protecting sensitive information to assess the benefits and risks of AI. In conjunction with data privacy and security initiatives and codes of ethics, a strong framework can guide development and use of AI technologies in its products in a responsible manner.
Security: Enterprises and technology vendors must implement multiple technical safeguards to minimise the risk that inappropriate use of AI may pose to sensitive data. Additionally, centralised security policies that limit access to data as privacy and security policies permit will be essential to giving administrators fine-grained control over their data.
Adoption: Another important consideration—and possibly the most challenging—is how teams will adopt AI across the organisation. AI at scale will require organisations to break down the silos that are currently separating data as well as people and processes to adopt new ways of working and create a company culture that embraces change and innovation. Using AI to transform the enterprise will require new skills, restructured business processes and new ways of communicating so that everyone knows what is changing, why it is changing, and what expectations are across teams and departments.
What will organizations accomplish next with generative AI?
Organisations are already embracing a new era of productivity and technology, and its potential is only starting to be realised.
According to McKinsey, generative AI and similar technologies may be able to automate enough work tasks to free up as much as 70% of a worker’s day. It predicts that by 2030, half of today’s work activities might be automated.
It’s understandable why interest in AI is increasing. But it will take time for its full, seamless integration, and there will be more obstacles to overcome along the road. Today’s business executives must understand the potential and risks of generative AI to overcome challenges, stay one step ahead of rivals, and be among the first to maximise productivity gains.
