AI is being increasingly incorporated into organisations’ strategies, to streamline workflows and improve communication. That being said, a report from earlier this year found that 42% of companies have abandoned or scaled back most of their AI pilots due to data, integration, and governance challenges. In order to reap the benefits of AI-driven solutions, businesses should modernise legacy systems, take a hybrid deployment approach to implementation, address human+AI workflow approach, and carefully manage the ethical and privacy considerations of AI in the workplace.
The challenges created by legacy tools
Many enterprises still rely on outdated Private Branch Exchange (PBX) systems, legacy contact centres, and fragmented collaboration tools, creating significant barriers to modernisation.
Older applications were not designed for today’s flexible work modes, including remote, office, and mobile, and typically lack omnichannel capabilities. As a result, employees must switch between multiple tools and channels, wasting time and creating friction in customer interactions.
Beyond productivity, outdated systems also pose serious risks. They lack modern encryption algorithms and security monitoring, making them attractive targets for cyberattacks and leaving organisations exposed to compliance and regulatory gaps. At the same time, maintenance, service, and support costs continue to rise as these applications age, placing a growing burden on IT and operational budgets. In short, legacy environments not only limit current performance but also act as a brake on future innovation and competitiveness.
Communication bottlenecks as a barrier to AI-driven solutions
For modern AI applications to deliver value, they require seamless access to real-time data. Unfortunately, legacy systems typically often lock data away within internal proprietary formats or siloed databases that AI is unable to access, impairing it from reaching its full potential and often leading to fragmentation.
Consider a customer service team. With a modern system, AI can transcribe calls in real time, analyse sentiment, and recommend next best actions to the agent. With legacy infrastructure, call data may be siloed or only available after the fact, preventing AI from adding value during the live interaction itself. Similarly, without modern APIs, chatbots and virtual assistants struggle to retrieve information from back-end systems, leading to frustrating dead ends for customers.
Outdated infrastructure also lacks the processing power and real-time connectivity that is required for advanced use cases such as speech-to-text, multilingual translation, and workflow automation. Without integration, automation capabilities are hindered, leaving employees burdened by repetitive tasks and limiting the potential for AI to reduce manual workloads and to capture efficiency gains.
To unlock the full potential of AI-driven solutions, enterprises must first overcome these communication bottlenecks head-on by modernising data access, upgrading infrastructure and embracing open integration.
Approaching communication system modernisation
When enterprises look to modernise unified communication applications and transition from legacy systems, they should consider taking a hybrid approach. Rather than enforcing an entire overhaul and complete move to the cloud, it’s important to look at hybrid solutions consisting of a mix of on-premise applications and private and public cloud solutions. This model delivers the best balance of continuity and innovation.
Key principles include:
- Hybrid Cloud Deployments: Rather than taking a rip-and-replace approach, businesses should consider maintaining and modernising existing on-premise and private cloud PBX/UC/Contact centres while layering in modern collaboration applications from the public cloud. This ensures that enterprises are able to evolve without any major disruptions.
- APIs and SDKs: It’s important to regularly update and develop APIs and SDKs to connect legacy systems with modern applications and collaboration services. This type of flexibility helps businesses gradually adopt new capabilities without causing significant disruptions to their day-to-day business.
- AI enhancements: It is possible to enhance existing communication platforms with AI-driven capabilities such as visual voice mails, virtual assistants, intelligent agents, flexible workflows and real-time transcription. Cloud-based large language models (LLMs) can integrate with smaller language models (SLMs) deployed locally, reducing the need for costly infrastructure overhauls.
- Security and compliance upgrades: All enterprises must prioritise security, data protection and regulatory compliance by implementing security standards such as end-to-end payload and data encryption as a best practice. This can help avoid compliance fines and ensure that all products adhere to the latest regulations in certain industries as well as regional standards.
This pragmatic, incremental path allows organisations to modernise with confidence, reducing risk, preserving continuity, minimising disruption while maximising value from both existing assets and emerging technologies.
AI transforming business communication: the next major advancements
AI is reshaping unified communications by automating routine tasks, improving efficiency and strengthening security. Capabilities like voiceprint-based user authentication, continuous vulnerability assessments and thorough system security audits strengthen enterprise protection. Meanwhile, Agentic AI and virtual assistants are evolving, with organisations equipping them with various communication capabilities to manage complex queries, automate responses, support scheduling and streamline information retrieval.
At the same time, AI-driven innovations in speech-to-text, real-time multilingual translation and sentiment analysis are transforming contact centres, boosting agent productivity and elevating customer satisfaction. LLMs further expand the possibilities by generating analytics reports, summaries and legal and compliant communication letters, highlighting the growing role of generative AI for enterprise content management.
In each case, AI’s value extends far beyond cost savings. It creates richer, more empowering experiences for both employees and customers.
Ethical considerations for AI in the workplace
While AI offers transformative potential, enterprises must embed ethics at every stage of deployment. Transparency, fairness and explainability are essential. AI should serve to enhance and empower human capabilities, not to replace them.
Organisations should establish AI governance policies, invest in employee training and maintain human oversight to build trust and drive adoption. Leaders must also ensure safeguards against misuse, such as deepfakes or data manipulation, while creating feedback channels to measure AI’s impact.
Equally important is addressing workforce concerns. Employees may fear displacement and job security when AI tools are introduced. By proactively addressing these concerns, positioning AI as a supportive assistant rather than a replacement, and providing upskilling opportunities, leaders can foster a culture of trust and innovation. Clear communication from leadership is key. If managers demonstrate confidence in AI tools, employees are more likely to embrace them.
Building a future-ready enterprise
By overcoming legacy challenges, adopting a hybrid approach to their modernisation strategy and addressing ethics and adoption principles from the start, enterprises can unlock the full potential of AI-driven communications. The payoff goes beyond efficiency gains, enabling a workplace that is more resilient, collaborative, and secure.
Organisations that act more decisively will not only maximise the return on their AI investments but also stay ahead of competitors and build communication systems truly fit for the future of work.
