Navigating the AI revolution: A roadmap for managers and companies

In an AI-driven workplace, people and AI agents will collaborate. Image: iStockphoto/metamorworks
- Preparing for a future workplace driven by artificial intelligence (AI) goes beyond technology adoption – it must enhance human potential.
- Leaders will need the skills to navigate this transformative era and must master the art of leading teams in which AI agents and people collaborate.
- Certain skills will foster collaboration in the age of the AI-driven workplace and this will create exciting possibilities in the Intelligent Age.
Imagine a world where managers can effectively oversee 1,000 direct reports. Some are people working in adjacent offices or scattered across the globe, while the rest are artificial intelligence (AI) agents that are intelligent, ever-evolving and working 24/7.
This is not a distant vision but the emerging reality of the AI era. And it offers immense opportunities and challenges for organizations embracing digital transformation. The urgency to adapt is clear and companies that fail to prepare will find themselves left behind.
The enterprise AI market opportunity has already surpassed a trillion dollars. But by 2028, the market for AI services is projected to reach $609 billion, with a compound annual growth rate (CAGR) of 21.4%, according to Gartner. The market for AI technology is also expected to soar to $512 billion by 2027, growing at a CAGR of 31%.
The race to leverage AI is not just about deploying technology, but doing so effectively, at scale and securely.
Overcoming AI challenges
To stay competitive, organizations must begin planning for the transformative impact of AI on workforce management. Here are the top five actions that C-suite leaders and frontline managers must take to succeed in the AI-driven workplace:
1. Move quickly to adopt AI now
The pace of AI innovation is staggering, with new solutions emerging almost daily. Enterprises that invest in AI now will eclipse competitors that hesitate.
Consider Klarna, the fintech leader in "buy now, pay later" services. The company achieved an adjusted operating profit of $66.1 million in the first half of the year, a significant turnaround from a $44 million loss in the same period a year ago. It said this success was partly due to leveraging AI to optimize costs. This shows how early AI adopters can position themselves for exponential growth.
2. Collaborate – with technology and each other
AI is reshaping team structures and workflows. The integration of agentic workflows –processes where AI collaborates seamlessly with human teams – will redefine how tasks are managed.
Take the telecom industry, for example. Major telecom companies handle hundreds of millions of customer calls monthly, with about 20% related to billing inquiries. Of those, 80% involve requests for detailed explanations of bills, amounting to nearly 100 million interactions daily.
By implementing AI architectures, these companies can ingest billing data into domain-specific large language models (LLMs) or small or specialized language models (SLMs), that are trained to process this information. These models can then handle inquiries via websites, chat systems or call centres. As a result, employees are freed up to focus on higher-value tasks.
3. Prepare your team
We are in the early stages of AI in enterprises, also known as the infrastructure build-out phase. And as leaders map out their AI strategies, it’s a bit like flying the plane while building it: Even if the plane is not ready, you have to find a way to fly.
Some companies are forming "tiger teams" to experiment with and implement AI initiatives. Others are running parallel teams, with one focused on traditional workflows and the other on AI integration. A dual approach allows for a smooth transition as AI-powered systems become operational. The ultimate goal is to "flip the switch", enabling organizations to realize AI-driven workplace outcomes with minimal disruption.
4. Unleash your data
Data is the fuel for AI, but it’s often fragmented across different systems, formats and jurisdictions. In Uniphore’s 16 years of integrating AI technologies into global enterprises, no company’s data has ever been entirely ready for AI. This kind of bottleneck hampers efficient AI adoption and scalability.
Most global enterprises were not designed with AI in mind. Data remains siloed in disparate systems, making it difficult to harness its full potential. CEOs and CIOs face a critical decision: Spend years rebuilding infrastructure for AI or adopt solutions that enable immediate acceleration without disrupting existing systems.
This is where architectures that enable companies to store data across on-premise systems and in the cloud provide immense value. Data agents can connect to different data stores and virtually make them one without moving data. And, in order for the LLMs to learn, the data needs to be copied, but new cloud systems can eliminate the need for data replication, enhancing security and reducing costs while enabling scalable AI deployment.
5. Work from a technology blueprint
An effective AI strategy must be fully integrated across existing systems and aligned with organizational goals. This requires a clear technology blueprint that outlines objectives, skill requirements and resource allocation.
Achieving this transformation demands thoughtful planning and collaboration across all levels of the organization. A well-designed blueprint ensures alignment, prevents surprises and keeps teams focused on measurable outcomes.
Building an AI-driven workplace
History has shown how technology reshapes industries — from mainframes and personal computers to the internet and cloud computing. AI is no different.
Every industry and job will be impacted, though the degree will vary. Call centres, for instance, may see workforce reductions, while AI solution providers will experience rapid growth. Managers overseeing 1,000 AI agents across industries could soon become the norm.
This shift highlights the need for reskilling initiatives led by governments and private sectors. Education systems must prepare workers for AI-centric environments to avoid societal imbalances and economic disparities.
Historians will remember this era not for the most advanced AI tools, but for leaders who developed the policies to ensure AI was used responsibly and inclusively. By embracing AI with foresight, organizations can drive innovation and create a future where technology enhances, rather than replaces, human potential.
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Eric White and Nitin Jain
February 12, 2025