Oru Mohiuddin
Oru Mohiuddin (Research Director, European Enterprise Communications and Collaboration)

Often, I hear that the business communications market will witness more consolidation because it’s overcrowded, meaning some vendors will either exit by divesting their operations or merge with another. There is some weight to this speculation, but by leveraging AI, vendors can reinforce their positions in the market.

When I say “leveraging AI,” I don’t mean just adding new features, but also overcoming some of the hurdles that are holding back end-user adoption. To make their AI strategies a success, it is necessary for vendors to understand and address these challenges.

Key Challenges for Businesses Trying to Gain the Full Value of AI

  1. Data silos are inhibiting AI’s full potential.

Our data reveals that businesses are at different stages of their AI journey, and the majority are still exploring, trying to determine use cases and how to extract value. Actual adoption will be determined by AI efficacy, which depends on backend data and AI training.

One challenge that limits the full value of AI is data being locked in disparate sources. Businesses often use separate systems for customer databases and CRM and utilize multiple communication and collaboration channels, creating data silos that hinder AI’s comprehensive system view.

Furthermore, these systems have different deployments, some on-premises and others in the cloud, making silos even harder to overcome.

Aggregating the different data sources, cleaning and structuring data, and training AI are the first steps in developing AI efficacy. Businesses need a connected IT stack, but this begs questions such as: Should solutions come from a single vendor who can provide a unified system in a common environment or a provider who is able to integrate different systems into a unified stream of data flow?

Many businesses are not able to adopt a unified solution from a single provider, because their investments are locked into existing IT systems and workflows. Even if systems are at the end of life, migration may not be an option due to the deep integration of workflows and strong relationships which the businesses may share with their existing providers.

Our data shows that businesses have concerns about migrating to new systems. Among other things, they are uncertain about the ultimate ROI the new system will deliver.

  1. Data privacy is a concern.

How much data should be exposed to AI is an issue for end-user organizations as well as government regulatory agencies.

Among the many benefits of data integration is the enablement of AI to drive personalized and targeted service in customer support, as AI can view and summarize customer information from different systems. This can range from listing a customer’s name and intent on agents’ desktops to providing buying history to support agents’ upsell efforts based on customers’ past preferences.

There is, however, a fine line between personalized service and violating a customer’s privacy. While some elements of personalization can feel special and help to create a sense of emotional connectedness, it’s important not to pass the point where knowledge about the customer creates a sense of invasion.

Another concern is the need to move operations to the cloud, as most AI is cloud based. Some businesses may be unable to do this due to regulatory compliance requirements for customer databases to remain on premises. For example, healthcare providers must keep patient data on premises to maintain security and privacy .

Additionally, some businesses are concerned about losing control and visibility of their IT infrastructure and customer databases by moving them to the cloud. Data sovereignty is also a factor, as some governments (including those in Europe) require businesses to keep data within the country, forcing AI solution providers to base their solutions in local datacenters.

Strategies to Overcome Challenges Impeding AI Adoption

  1. Base AI capabilities on integration and training, but also customer privacy and data governance.

Efficacy is critical for driving end-user adoption. This involves connecting different data sources and enabling closer and more far-reaching integrations within the ecosystem.

To this end, the market is witnessing a shift from SaaS-based to platform-based solutions, which allow integrations across different systems and third-party providers. Vendors also need to ensure compatibility with different IT environments, including integrating on-premises with cloud environments to enable business in different stages of their DX journey to access AI benefits.

The next step should be cleaning and structuring data, which can be overwhelming for businesses with particularly large/extensive data pools. This could present a business opportunity for professional service providers and systems integrators.

AI innovations should also incorporate guardrails to prevent misinformation, since AI is only as good as the data behind it and the training it receives. There should always be an option for human intervention to monitor and rectify AI-based results. Customers should be able to opt for how much of their information they want to be exposed to AI and other parties.

AI should be available for businesses who are mandated to run their operations on premises. And communications and collaboration solution providers should develop partnerships with other service providers that have widely distributed or local datacenters, thus making it possible to base their solutions in datacenters stipulated by data sovereignty requirements.

  1. Handhold customers during the process.

Developing suitable solutions is just one part of the story. It is also necessary to educate customers who are still familiarizing themselves with AI. Our data reveals that many businesses are unsure about use cases and how AI will fit into their overall operations. It is therefore necessary to drive conversations with relevant business leaders.

IDC data shows that while IT is still an integral part of sales discussions, business leaders are becoming more involved. Deploying AI is not just about new technology but changing business culture around it involving managers, and employees at all levels.

Making AI successful within organizations requires hand-holding through the process, from education to implementation and change management.

  1. Pay close attention to pricing.

Pricing is another important consideration, and pricing trends are wide ranging. Some vendors include AI features in their solutions without charging extra, while others offer them as add-ons at an additional price.

Not surprisingly, customers’ willingness to pay for AI solutions varies, depending on their perceived value versus price. While the unique and sophisticated functions AI can perform will influence pricing strategies, common features like meeting summarization and message composition are likely to face commoditization.

A federated approach to AI is helping to keep costs down and make it possible to offer AI as part of overall communication solution bundles. In the long run, it is likely that basic GenAI-based features will be part of overall communications solution bundles.

  1. Work closely with partners for go-to-market motions.

Go-to-market strategies are also crucial, particularly in Europe, where much of the IT communication stack is sold through partners, some of whom need training to communicate effectively with relevant business stakeholders. Channel partners should be incentivized for go-to-market initiatives, including monetary incentives and ownership of commercial relationships.

Vendors need to provide high-touch support during the sales process but leave ownership to channel partners post-sale. Market dynamics require vendors and partners to work hand-in-hand.


Succeeding in the business communications market involves more than introducing new AI features. It requires ensuring data efficacy by connecting different data sources, effectively training AI, and implementing guardrails to prevent misinformation.

Vendors need to understand where businesses are in their AI journey and customize solutions to meet their needs, including integration with unique IT environments and compliance with data governance and security requirements. Educating businesses about AI benefits and helping them implement AI in a compatible IT environment is essential.

The partner community plays a crucial role in the go-to-market process, and success will necessitate close collaboration between vendors and partners.

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