Industry Voice: AI, Done Differently: How Partners Are Going From Disconnected Pilots to Business Outcomes

From Chaos to Confidence in AI

Artificial intelligence (AI) adoption has exploded across Asia Pacific (APAC) – but business outcomes remain uneven.

A recent KPMG AI Pulse survey found that APAC is spending more on AI and deploying it faster than other regions. APAC is slated to invest, on average, US$245 million in AI over the next 12 months on training, technology, compliance and talent. This investment is above the global average of US$186 million, suggesting that APAC companies are serious about their commitment to AI, especially since many have found meaningful benefits from adoption, such as productivity gains, cost savings, revenue growth and sharper decision-making.

It’s clear that AI ambition remains high, but AI success is not always a given. One in three companies has yet to see AI deliver meaningful business value, according to the KMPG survey.

The problem is not necessarily the AI tools, but how disconnected or incorrectly adopted products can create “AI chaos”.

“AI initiatives usually fail for the same reasons other technology programmes fail – AI just makes the gaps more obvious. Common culprits are unclear use cases, poor data foundations and underestimating security and governance,” says Rob Hunter, Managing Director of IT support company Lanter Technologies.

The key to successful AI implementation, then, is a unified approach to addressing the productivity, data and trust gaps that exist across AI implementation. These gaps are bridged not just by individual vendors alone, but by a distribution partner that nurtures an ecosystem of partners, technical specialists and solutions providers. Partners such as Axiom IT Solutions, ECLEVA and Lanter Technologies benefit from distributors like Crayon, a SoftwareOne Company, as they offer tangible support, which helps them actualise their customers’ AI ambitions in practical, secure and scalable ways.

Crayon asked Industry experts from these companies to weigh in on how organisations can turn AI chaos into clarity. Here is what they said:

AI can only truly transform productivity if enablement is provided

APAC businesses are under pressure to adopt AI, given the growing productivity gap. Microsoft’s 2025 Work Trend Index reveals that 84% of APAC’s workforce lacks the time or energy to do their work, but 61% of APAC leaders say productivity must increase.

But as AI moves from experimentation to operational deployment, businesses are discovering that it is insufficient to introduce new tools into the workflow. Employees need to understand where and how AI can actually be used to augment their productivity in the first place.

“A lot of clients don’t really know what AI can do. They’ve got directives from the board saying ‘We want AI and we want it now,’ but they don’t actually know what they want. AI has to add value and make businesses better – not just be used for the sake of using it,” says Patrick Northcott, CEO of business systems consultancy ECLEVA.

Without a clear AI vision, businesses end up with disjointed attempts at implementing AI.

“AI is not a tool that you buy, and then you will know how to use. We are not just replacing Part A with Part B without thinking about how we should do things differently,” says Lam Pang Ngean, CEO of AI cloud solutions provider Axiom IT Solutions. “We are talking about the future.”

As such, Lam says AI use cases require creativity because it is not just a tool that automates things – it re-engineers entire processes.

This is why education is an important part of AI scalability. Partners such as ECLEVA and Axiom IT Solutions turn to Crayon to help deliver and conduct workshops as part of their offerings so customers understand how AI can be tailored to their business needs.

Platforms like Microsoft Copilot are also useful because they lower the learning curve of AI use cases. Rather than introducing entirely new systems, Copilot embeds generative AI directly into the tools employees already use every day, from email and collaboration platforms to productivity applications and business workflows.

“Copilot works well at scale because it sits inside the Microsoft 365 security boundary and uses the same identity, compliance and permissioning model organisations already rely on,” says Hunter. This lowers friction, speeding up adoption and helping organisations operationalise AI at scale.

However, successful adoption still depends heavily on enablement and change management. While Copilot is easy to use once implemented, actually integrating it into existing workflows can be challenging. Crayon supports IT solutions providers through its technical expertise, conducting advisory workshops and adoption and change management services. Crayon is a Microsoft Copilot Jumpstart Ready Partner, specialising in helping organisations adopt Microsoft 365 Copilot, and a premier Microsoft AI Frontier Partner, recognised for helping organisations leverage AI and cloud technology. This helps partners support customer implementation.

“We implement Copilot with change management built in,” Hunter explains. “We run role-based sessions focused on real tasks like meeting follow-ups, email drafting and document summarisation so AI becomes part of everyday workflow rather than a novelty.”

AI is only as useful as the foundation it’s built on

Many enterprise AI initiatives show promising results – enough to get leadership on board to plan a broader rollout. But only a small percentage make it beyond the pilot, and the culprit is often the context: inconsistent data, siloed systems and fragmented analytics environments prevent AI systems from operating at scale.

“Many organisations try to bolt AI onto messy processes,” says Hunter. “Data platforms that unify structured and unstructured data will become a major enabler for AI.”

When data or systems are fragmented, AI is unable to determine what data it has access to, how to structure and parse that data – for example, determining which data is authoritative or which information is approved – or the rules and policies that govern data sets. Unifying data or using unified data platforms then becomes increasingly important in enterprise AI strategies.

Tools like Microsoft Fabric provide organisations with a single environment that brings together data integration, analytics, governance and AI-ready infrastructure. By consolidating fragmented data environments, businesses can create more consistent AI outputs while accelerating access to organisational insights.

“One of the main reasons for using Fabric is having a consistent data model across multiple data sources, which means you can ask questions across multiple disconnected systems and actually get meaningful answers,” says Northcott.

Implementing a tool like Fabric, especially if businesses are starting with inconsistent data and siloed systems, has a steep learning curve. ECLEVA runs Microsoft-funded workshops on how businesses can define the architecture of their Fabric solution. Crayon helps partners access Microsoft funding through its status as a premier Microsoft partner, acting as a strategic intermediary so partners can access such resources.

Unified platforms help organisations shift from isolated experimentation towards scalable AI operations. It would eliminate the need to rebuild data environments for every new use case. Instead, a common foundation can support analytics, automation, Copilot experiences, and future AI agents over time. Without clean, accessible and governed data, even the most advanced AI models struggle to deliver meaningful business value.

Trust is what allows AI to scale

As with any emerging technology, AI adoption introduces new risks around sensitive data exposure, regulatory compliance, access management and increasingly sophisticated cyber threats. APAC businesses cited cybersecurity and data privacy concerns as the biggest barrier to AI implementation, with many businesses slowing or pausing deployments because of it, according to the KPMG survey.

That means security can no longer be treated as a separate layer added after deployment, but integrated directly into the architecture from the very beginning. “If you’re going to put AI over the top of your data, you need the right security and monitoring protocols in place to make sure that data is safe,” says Northcott. This is at the heart of responsible AI adoption; innovation must come with the promise of keeping customer data safe.

Microsoft’s Copilot, for example, has built-in security features that prevent sensitive information from leaking beyond its environmental constraints. “Businesses use Copilot because they understand the need for security. In business, we need to ensure data is contained within the company infrastructure and doesn’t leak out,” says Lam.

Businesses can also employ a Zero Trust strategy, which operates on the principle of continuous verification, ensuring that users, devices and systems are authenticated before accessing critical resources. Rather than assuming trust inside the network perimeter, organisations adopt a “never trust, always verify” approach that strengthens resilience across increasingly distributed environments.

Crayon implements both a Zero Trust approach and responsible AI principles into every stage of its strategy. This means that its AI solutions services have deep technical expertise grounded in proven frameworks for governance, security and ethical deployment.

Businesses scaling AI initiatives must balance between innovation and governance. Strong security frameworks not only reduce risk but also accelerate AI adoption by increasing organisational confidence around compliance, governance and data protection.

“There’s also a trust and risk component: people worry about confidentiality, accuracy, and who is accountable when AI is used. Without clear governance, organisations default to either doing nothing or doing scattered experiments that don’t connect to a coherent vision,” says Hunter.

AI scale requires an ecosystem, not a licensing solution

Industry experts agree that AI is evolving at lightning pace, and it’s difficult to determine exactly how the technology might change in the future. As such, businesses need more than just software licenses or standalone tools to navigate the increasingly complex AI landscape. They need an ecosystem capable of guiding strategy, supporting implementation, enabling adoption and managing ongoing and uncertain change.

“Keeping up with the changes is one real challenge for the industry because there’s literally new stuff coming out daily. You used to have to learn something and it might last you three years or more. Now, you learn something and it’s dead – it’s changing the next week. So having a partner like Crayon that has the resources to help you keep up with those changes and continuously educate partners, I think, is vital,” says Northcott.

Distributors like Crayon enable solutions companies to achieve customer goals through Microsoft expertise, commercial support, platform capabilities, enablement services and escalation pathways delivered through its partner-focused, value-led Distribution Done Differently approach.

“A distributor like Crayon supports us by helping with licensing strategy, commercial options, and access to vendor programs,” says Hunter. “This allows us to focus on design, delivery and adoption for customers.”

That collaboration extends beyond commercial support into joint workshops, customer engagements, solution development and go-to-market initiatives. “Crayon has been very supportive, especially when we go to market,” says Lam. “We need partners that can complement us so we can grow the business together and also benefit our customers.”

The reason partners and IT solutions companies work so well is that they are aligned in their vision. “We have a common goal,” Lam says. “AI adoption.”

AI, done differently/ From chaos to confidence

The AI conversation in APAC is entering a new phase. The focus is no longer on whether organisations should adopt AI, but how they can operationalise it effectively, securely and at scale. Or in Lam’s words – finding the confidence to seek and actualise an AI vision.

“We want to create curiosity into confidence. Confidence means I have confidence with AI – I dare to use AI for my company. It’s only when customers have that confidence that AI will scale,” says Lam, and that means going through a process of trying, learning, fine-tuning and figuring out what works.

As businesses move from AI experimentation toward operational scale, success will increasingly depend on ecosystems that combine technology, governance, data readiness, security and adoption expertise into a unified strategy. Because ultimately, AI transformation is not just about deploying tools. It’s about helping organisations re-engineer how work gets done.

To learn more about how Crayon supports Microsoft AI transformation across productivity, data and security, visit Crayon’s Microsoft solutions page.

Read the original article on CRN Australia and Asia here.