We will likely now all agree that the latest generation of AI has emerged as a game-changer. Its potential is undeniable and prevalent across every sector, industry, and use case. Yet, many enterprises remain stuck in the "Proof of Concept" (POC) phase due to various barriers, testing the waters but never diving in. It's time to move beyond this and build real-world applications using AI.
The Magic of AI is Already Proven
Countless evaluations across industries have showcased the transformative potential power of AI. From predictive analytics to natural language processing, the capabilities of AI have been displayed across various sectors. The magic isn't in proving AI's potential anymore; it's in harnessing it for tangible business outcomes.
AI: A Strategic Imperative
Let's begin by highlighting some crucial statistics that underscore the significance of AI in our industry. According to IDC’s AI StrategiesView 2022, within the next two years, firms will prioritize AIOps (16%), augmented intelligence (15%), and discovery and analysis applications (14%). These findings emphasize that AI isn't merely a passing trend; it's a strategic imperative shaping the future of our field.
Furthermore, IDC's Worldwide Artificial Intelligence Spending Guide (August 2022) projects that global spending on AI is set to surpass $301 billion by 2026, with a notable 5-year compound annual growth rate (CAGR) of 26.5%. This figure serves as a compelling testament to the vast potential that lies ahead.
Why POCs Haven’t Brought About the Promise of AI To Enterprises
A POC operates in a controlled environment, often devoid of real-world production challenges. It can't truly highlight potential drawbacks or issues that might arise in a full-scale application. Consider:
- Sensitive Data: Handling real-world data, especially sensitive information, requires robust security measures, which a POC might not account for, causing another barrier before full-scale implementation is possible.
- Scaling: A POC might work perfectly for a small dataset but falters when scaled up. Real-world applications need to handle vast amounts of data seamlessly.
- Training and Adoption: A successful POC doesn't guarantee business benefits. For AI to truly add value, it must be adopted widely across the organization, integrated into workflows, and embraced by employees, with a low barrier to entry around training for users and stakeholders.
Unlocking Economic Value: Strategies for Scaling AI Projects
Overcoming barriers so businesses can scale their AI projects is where enterprises will realize the economic value. The 2019 survey by MIT Sloan Management Review and Boston Consulting Group is a stark reminder of the gap between AI investment and tangible business gains[1]. The reason? A reluctance to move beyond the pilot phase.
To transition from pilot projects to realizing economic value through AI, companies need to follow a strategic approach. First and foremost, they should concentrate on creating momentum by showcasing the tangible benefits that AI can bring to the organization with significant and rapid wins, not POC. These early successes can serve as compelling evidence of AI's potential and rally support for larger initiatives.
Moreover, it's essential to focus on the organization by using these initial wins as guiding lights to prioritize AI projects. This ensures that resources are allocated to the most impactful areas, preventing aimless experimentation.
Lastly, to drive sustainable adoption and overcome resistance to change, it is crucial to convince employees by demonstrating how AI can deliver real benefits. When employees witness AI in action, improving processes and enhancing their work, they are more likely to embrace and integrate AI into their workflows. By following these steps, companies can move beyond the pilot phase and harness the economic value that AI has to offer.
Case in Point: Legal Operations
Let’s take a real case example to illustrate this based on our experience at Hanzo working with large enterprises with complex litigation and investigation needs. In legal operations, the early assessment of potential risks and quick preparation for a case is critical, and it requires scanning through a large number of documents, conversations, etc. The new generation of AI can help legal teams here by making one of their most repetitive and tedious tasks almost disappear. Unlike previous methods based on traditional AI, there's no need to grapple with complicated jargon, training, or new methods.
The right approach is not to do yet another POC but to directly create value with a solution that's primed for immediate integration with existing workflows. This speeds up decision-making for investigations and litigation, helping leaders manage risks better.
Moreover, it paves the way for extending its application to other processes, laying the foundation for a more ambitious AI expansion as a strategic lever in your firm.
Seizing the AI Advantage: Legal Tech's Imperative for Transformation
In the ever-evolving landscape of legal technology, the time for theoretical discussions has passed. It's clear that artificial intelligence (AI) is not just a concept but a practical tool with real-world applications. While Proof of Concept (POC) projects have their frustrations, there's a palpable demand for AI solutions that deliver tangible results. AI has already demonstrated its transformative power in various industries, and within the legal domain, it promises rapid insights, streamlined data management, cost efficiency, and enhanced productivity. Embracing AI as a strategic imperative is essential for the future of legal tech, allowing enterprises to navigate a path toward practical growth and success. In the next post, I will explore where to start with practically implementing AI.
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