The Future of Human-AI Collaboration: Seamless Augmentation
By Efrain Ruh, Field CTO of Europe of Digitate
As artificial intelligence continues to evolve and expand its use among people, a new expectation regarding human-AI collaboration has arose: seamless augmentation. A recent study conducted by McKinsey research found that the number of organizations using AI in one or more functions rose from 55% to 72% in just one year, caused largely by the introduction of genAI. As AI begins to increasingly mimic and adapt to human behaviors, interactions, and thought processes, it can more easily be incorporated into daily workflows. This increasing adoption curve is requiring enterprises to redefine how to best bring human involvement for decision-making and AI oversight without requiring constant check ins.
Still, AI is not yet at a place where it can, or will, replace human expertise. According to Certinia’s 2024 Global Services Index, 80% of firms surveyed reported seeing AI as an avenue to increase efficiency, but less than one-third plan to use AI to reduce headcount.
Where AI does shine is helping people do their jobs better. The key to successfully implementing AI in enterprises is threefold. This includes:
- Make sure your data is organized, accurate and available,
- Define the required guardrails to eliminate unnecessary risks and build greater confidence on AI results and decisions
- Start small to prove value fast and then increase complexity incrementally while you experiment and continue to innovate on new use cases
Data Preparation for AI Implementation
While the promise of seamless augmentation holds immense potential, organizations face several challenges in achieving this transformation. Unlike traditional software, AI systems often seem to operate as “black boxes,” making it difficult to diagnose and rectify errors or undesirable behaviors. This requires the development of new skills and processes to manage and maintain these systems effectively, ensuring they operate within expected parameters.
Additionally, many enterprises still hold their data – the foundation of any good AI model – in departmental silos. This creates islands of intelligence that are disconnected across the enterprise. To fully leverage AI for human gain, enterprises need to implement solutions that are able to collect and provide centralized data processing, so efficiency and profit gains can be fully realized.
The goal for successful AI implementation is to create an environment where AI models can leverage data and learn to make accurate decisions. Then certain manual processes steps can be powered by intelligent automation, letting the machine take the first action. Yet, like any relationship, outsourcing routine tasks to AI requires a certain level of trust and transparency.
When deploying AI, make sure it’s explainable. This allows organizations to audit these systems, verify their outcomes, and build trust with employees, stakeholders and customers.
This is particularly relevant as AI becomes more embedded across diverse functions, such as customer service and supply chain management. Any lack of transparency or visibility can lead to regulatory issues and harm trust among employees and customers. Organizations must focus on developing AI capabilities that offer understandable outputs.
When done properly, advanced AI and machine learning technologies can allow systems to detect anomalies and address issues automatically. Predictive engines further enhance these systems, enabling them to foresee and prevent potential problems. Human oversight keeps these systems in check but frees up individuals to focus on long term strategic thinking as opposed to manual processes.
Ethical AI Governance
As AI systems become more pervasive, they attract increased attention from malicious actors, escalating cybersecurity risks, privacy concerns, and associated costs. Organizations must implement robust security and governance measures to protect AI-driven platforms from potential threats.
2024 research from McKinsey found that 44% of respondents say their organizations have experienced at least one negative consequence from implementing genAI, most often reporting inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.
Governments entities around the world are already working on regulations that allows them to closely monitor and make sure AI development is aligned with societal values. Enterprises need to be mindful that they are implementing consistent standards across their practices. This can be challenging given disparate regulatory practices, particularly on a global scale, can complicate compliance. Governments and industries must work together to create clear and actionable regulations that allow companies to innovate while adhering to ethical standards.
Take for example the EU AI Act, a new legislation recently enforced as of August 2024, introduces a regulatory framework that will see its full impact unfold over the next 24 months. However, much of the legislation remains ambiguous, given that the AI landscape and its applications are still rapidly evolving.
As we wait to fully understand the potential harms AI can cause, there are important parallels we can draw from the implementation of the GDPR (General Data Protection Regulation) which came into effect in 2018. Such similarities can be leveraged to ensure we are ready. In its early stages, the GDPR was met with significant uncertainty. Many businesses were unsure about what practices were compliant, and it took nearly five years before substantial penalties, such as the €1.2 billion fine against Meta were enforced. This delay in enforcement reflects the complexity of interpreting and applying such sweeping regulations.
Over the last two years, however, we’ve seen a marked increase in penalties for non-compliance with GDPR across Europe, signaling that regulators have developed clearer enforcement mechanisms. It is likely that the EU AI Act will follow a similar trajectory—initial uncertainty followed by more decisive regulatory action as the technology matures and the authorities gain more experience in monitoring compliance.
Enterprises can proactively prepare their organizations to best meet upcoming regulations by evaluating the current landscape and implementing anticipated guardrails.
Innovating Within Set Guidelines
AI development should focus on creating transparent, trustworthy systems that provide value requiring less intervention and oversight. Still, this approach requires robust governance, a commitment to transparency, and a focus on building confidence in AI technologies.
Gartner predicts that by 2027, more than 50% of the GenAI models that enterprises use will be specific to either an industry or business function — up from approximately 1% in 2023. GenAI adoption is becoming specialized, customizable and unavoidable.
Organizations must prioritize building resilient, adaptable systems that elevate human capabilities and generate meaningful value. By doing so, businesses can create a technological landscape where AI and humans collaborate effectively, rather than one where people fear AI’s capabilities could make them redundant.
AI solutions should be able to integrate seamlessly in your day to day operations, and by making it easy to interact with and by increasing trust on AI, we can promote collaboration the same way we collaborate with our colleagues at the office. Our objective must be building these solutions around the end users, making them more productive in the office, instead of trying to replace them.
Efrain Ruh is an AIOps expert and CTO of Digitate, a leading provider of AI-based solutions for IT Ops. With over 15 years of experience in the IT industry, he has a strong background and expertise in IT Ops, Cloud Computing, AI/ML, Big Data and intelligent automation, as well as relevant certifications such as ITIL, PSM, and SAFe Agilist. In his current role, he provides technical leadership and consultancy to prospects, customers and partners, which involves staying abreast of emerging technologies, industry trends, and best practices.