By Lionel Moyal, Co-founder of Growthguru.ai

Across IT industries, companies are exploring ways to embed Artificial Intelligence (AI) into core operations, not just as a feature, but as a foundational capability that drives insights, decision-making and efficiency. This trend is reflected in recent data that shows that in 2025 88% of organisations reported regular AI use in at least one business function, up from 78% the previous year – underscoring AI’s growth in driving operational value.

In a complex partner or reseller ecosystem, AI is also being leveraged to provide actionable intelligence across marketing, strategic alignment and sales pipeline management. Resellers are experimenting with multiple AI models – from OpenAI, Anthropic, Google and xAI – each selected based on their strengths in analysis, reasoning, image generation or voice capabilities. However, as generative AI moves beyond experimentation and hype, the key question for IT businesses is: how can AI deliver measurable growth and ROI?

And the answer is simple: through discipline.

Turning AI experiments into business outcomes

For many, the journey is still experimental, but the companies that are seeing measurable outcomes are those that have identified specific business problems and have applied AI to deliberately solve them.

Take a co-marketing opportunity in the channel as an example - AI can map every action to a measurable result, helping resellers or distributors track campaign performance, partner engagement and revenue outcomes. However, the shift from experimentation to impact is having clarity on the outcomes desired. It’s about realising how to efficiently utilise “new technology” to create new possibilities for real tangible impacts.

While ambition and tangible impact are there, along with experimentation, the environment itself is not without its own barriers particularly in terms of skills shortages and lack of workforce readiness for true AI integration. With the country ranking 63^rd in AI development in the world – a ranking that reflects a country's development in the landscape, its attractiveness for AI investments and readiness to integrate AI into public services - it’s clear we have a long way to go. It is also for this reason that many businesses are considering partnering with platforms that have done the groundwork and can not only solve their AI business challenges for optimum growth, but also solve the industry challenge which include a shortage of skills.

Creating actionable insights

But choosing a platform can be difficult. Not all AI models are created equal; they have individual strengths suited for specific scenarios and so it’s about selecting the right tool for the right job with enterprise-grade security and reliability as the foundation being key. Moreover, it’s about users not getting generic AI output, but rather getting contextually relevant, role-specific intelligence that they can act on immediately.

Measuring AI’s impact: the metrics that matter

And this is exactly where many organisations get stuck. They measure AI adoption rather than AI impact. Adoption metrics only indicate the number of people using the tool whereas impact metrics reflect on whether a business is actually moving.

Key indicators to be tracked to ensure success in a partner ecosystem for example could include:

• number of partners successfully onboarded
• volume and quality of co‑marketing campaigns executed
• depth of partner engagement with AI tools
• improvements in Microsoft programme alignment
• incentive programme utilisation
• pipeline generated through GTM activities

• partner differentiation in competitive markets

When these numbers move together, it’s not just about AI adoption, it is about AI creating real, commercial and competitive momentum across the partner channel. And this shift from measuring usage to measuring outcomes leads to a bigger conversation: how organisations should think about AI investments and ROI.

Lessons for organisations considering AI investments and ROI evaluation

The biggest mistake organisations make is treating AI as an IT investment rather than a growth investment. When AI sits in the technology budget, it gets evaluated on cost efficiency. When it sits in the revenue or strategy budget, it gets evaluated on growth. That framing changes everything - from the use cases they prioritise, to the metrics they track, to the stakeholders who champion the initiative.

Additionally, do not underestimate the value of context. AI is only as good as the data and the business logic you feed into it. Generic AI gives you generic answers. If you want AI that understands your partner ecosystem, your incentive structures, your competitive positioning, you have to build that context in. That is the hard work, and it is also where the competitive moat gets built.

Organisations are choosing AI platforms with built-in industry context. These tools understand common business processes, programmes and engagement models, which means teams don’t have to start from scratch. Whether you are a reseller, an Independent Software Vendor (ISV) or a distributor managing a partner channel, you can avoid reinventing the wheel and more so, you can scale more efficiently.

What AI means for scaling operations and staying competitive

Scaling a business has traditionally been limited by two things: the capacity of its people and the quality of its decision-making. AI does not replace either, but it dramatically extends both.

Tasks that once required hours of manual research, documentation review or preparation can now be completed in minutes. With AI systems providing contextual insights, consolidating information and surfacing priorities, teams are able to walk into engagements better informed, more aligned and ready to act. The result is a shift from reactive to proactive operations - enabling individuals to manage larger portfolios of work with greater consistency and confidence.

In complex business environments - where incentive structures, market signals, partner dynamics or compliance requirements constantly evolve - AI is no longer a “nice-to-have.” It is becoming the operational backbone that helps organisations track the right information, anticipate change and make faster, higher‑quality decisions. Companies that integrate AI strategically gain a compounding advantage: they free up human capacity, reduce friction in execution, and strengthen their ability to scale sustainably.

And as AI continues to move from experimentation into core operations, the organisations that invest early and thoughtfully will be the ones best positioned to grow, differentiate and compete in increasingly dynamic markets. In the end, AI is not simply another technology wave - it is a new operating model.