AI with a human touch: committed to success
There’s been an inevitable buzz around artificial intelligence in recent years, with commentators quick to highlight the positive and negative aspects of AI technology. Businesses have been quick to adopt AI and reap the promised benefits of innovation, efficiency and productivity, with research from IBM’s 2024 ‘Global AI Adoption Index’ revealing that over half of businesses surveyed (59%) are researching or implementing AI and plan to accelerate investment in this area. But the rush to gain a competitive edge has also been held back in some sectors by one major concern – will AI adoption wipe out certain human roles?
This is a misconception: by understanding that AI is not an entity, but a tool that can only work as well as the people who enable it, organizations can deploy AI more effectively and with far more control. We only need to look at the recent, high-profile failure of McDonald’s’ AI-based ordering rollout. The fast-food chain announced that it would be discontinuing its AI drive-through solution in over 100 restaurants due to multiple food order errors, which then required human intervention on approximately one in five orders to fix the issues. As this case demonstrates, AI has its limitations and human skills remain key: anyone looking to use AI to replace workers will face numerous challenges. Instead, they should embrace the technology as a support system for simple tasks, streamlining repetitive processes and relieving the burden on end users.
Here are some areas of focus for organizations considering AI implementation:
CTO at Coherent Solutions.
AI doesn’t have to be revolutionary; it has to be practical
Often, organizations jump too early to implement new technology as part of a transformation journey, only to find that they overspend on a technology that doesn’t deliver a return on investment six months later. This is usually because they are overambitious or underestimate the journey ahead.
Rather than investing millions in the empty promise of improved productivity, organizations must first sit down, set realistic goals and evaluate how AI can support their people and how AI can be integrated into their business objectives.
Conducting a thorough process analysis before embarking on an AI project is essential to evaluate which part of the company’s AI will perform best and deliver the greatest value. Whether the goal is to improve internal processes or roll out to external users, questions like “who will use this capability?”, “at what scale does it need to operate?” and “how do we measure success?” set fundamental expectations and help keep projects focused.
Sometimes this means automating a single process, such as customer service queries. This small-step approach is essential to ensuring and measuring success. But AI won’t benefit all departments or all operations, especially if a process is running at too small a scale to justify the investment. Questioning need and value from the start with an initial assessment is the most efficient approach to any transformation, ensuring that AI investments make sense and are almost certainly worth the effort.
Likewise, defining success requires forward planning. Once the most effective AI use case has been identified, it’s critical to establish baseline metrics for productivity and quality. This provides a clearer picture of the true impact of an implementation on business operations, allowing AI investments to be assessed, validated, and lessons learned captured for future projects. AI isn’t going away, but many people are right to be cautious. If an organization wants to implement the technology at scale, fostering a culture of demonstrating success on a smaller scale will go a long way toward a smooth transformation. If AI is truly going to make life easier for end users, it’s critical to focus on the practicalities and have a solid plan.
Understand and advance AI maturity
Once the foundation for AI success is in place, it’s critical to map objectives against an accurate picture of an organization’s AI readiness. As a new and powerful technology, AI requires skills and the ability to manage and protect complex infrastructure. Map the organization’s technology foundation by assessing the existing technology stack. Can the current software handle the demands of AI integration? Are there any compatibility issues or technical weaknesses that need to be addressed during this project?
But this isn’t just a technical question: there’s a crucial human element to AI maturity. Create a matrix of the skills and knowledge of both IT professionals and the team that will be working directly with the new AI program. It’s common to have gaps here, AI is of course a remarkably new technology for enterprises. But if users don’t know how AI works and how to use it within their roles, any investment is virtually useless. Invest in building their technical confidence up front and ensure they fill any gaps with adequate training or recruitment.
The final factor to consider here is process discipline. Ensure that incoming AI implementations are met with well-defined processes to handle data collection, management, and model development. Without all of these elements in place, organizations could be looking at a recipe for disaster.
Safety and quality come first
Every new technology introduces a security risk to an organization, sometimes by complicating data protection, introducing code vulnerabilities, or expanding the existing attack surface. Some weaknesses may not become apparent until the technology is in use, but during the development phase, security should be continually built into the program and assessed with regular code reviews. Once discussions begin about launching a new product or program, organizations should also discuss their strategy for building and deploying it securely. This is especially important in the case of AI, which introduces complexity and handles masses of potentially sensitive data. Organizations developing AI models for their business or purchasing AI solutions from partners must remain diligent about the quality of the training data.
In addition to data quality controls, organizations should develop customized AI usage policies to not only meet government or industry regulations, but also address the specific needs of the business. A comprehensive set of end-user guidelines and safeguards will significantly reduce AI misuse. These policies, combined with proper training and process discipline, ensure that organizations can reap the many benefits of AI while mitigating cyber risks.
As AI sweeps through the technology industry, it can feel like the dawn of a new era. But organizations need to remember the fundamentals of implementing new technology – AI is no different. By taking the time to truly analyze needs and plan projects to truly benefit the business, any organization can set itself up for success from the start.
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