The Businesses That Learn Fastest Will Outrun the Businesses That Automate Fastest

Session Speaker: Grae Laws, Founder, Beyond Touch Ltd
Theme: Future Facing: Technology and Operations
Date: Summer Trimester, May 2026

This Magma session brought external speaker Grae Laws into conversation with members around AI, data, marketing systems, and operational decision-making. The discussion moved well beyond tools. Members tested the ideas against the realities of running growing businesses, managing teams, handling client trust, and making better decisions with imperfect information.

The session focused less on AI adoption itself and more on what changes when SME leaders begin treating their business knowledge as a strategic asset.

AI will transform everything – won’t it?

Most business owners are already feeling pressure. There are too many systems, too much scattered information, and too many decisions relying on memory, instinct, or whoever happens to know the answer.

Currently, a sales conversation is in one platform, and client history is in another. All while meeting notes are scattered across various inboxes, and marketing data is spread across numerous disconnected tools. As a result, teams end up repeating work because knowledge never really accumulates.

Meanwhile, leaders are reading everywhere that AI will transform everything, but for most SMEs, that message creates more noise than clarity. So they should be stepping back and asking, how do you make the business think more clearly, more consistently, and with less friction?

Big Brain

Grae Laws introduced AI not as a replacement for people, but as a “big brain” for the business.

The important distinction was that this was not about relying on a chatbot to produce clever answers. The focus was on creating structured business intelligence from the information companies already generate every day.

Imagine transcripts, reports, proposals, customer conversations, CRM records, competitor research, websites, reviews, internal documents, and operational notes all becoming inputs into the “big brain”. From that point on, AI helps process, structure, connect, and retrieve that information in ways humans struggle to do consistently at scale.

The emphasis throughout the session was on "helping teams play together" more effectively. The aim was not for AI to replace people, but to reduce fragmentation between tools, departments, workflows, and information sources so the business operates with more continuity.

That became particularly clear in his distinction between “AI with humans in the loop” and “humans with AI in the loop”. Rather than treating people as supervisors of automated systems, the focus was on AI supporting human collaboration, coordination, and continuity across the business.

Grae Laws also described the “big brain” concept as a reflection of the MD role itself. The system becomes a way of  facilitating, coordinating, interrogating, and connecting the collective information of the business so teams can operate with more shared context and less duplication.

Regarding which LLM platform to use. Claude was favoured largely for its enterprise controls, transparency in data handling, and the ability to securely separate client environments.

Less fragmentation

One of the strongest themes in the discussion was that many SMEs are sitting on large amounts of valuable information without any coherent system for using it.

The problem is rarely a lack of data. It is fragmentation.

From the group discussions, it was clear that commercial insight already exists inside businesses, but often in forms that are difficult to connect. Sales calls happen. Teams learn things. Customers share important information. Competitor landscapes are being seen to change. Staff solve recurring operational problems. But very little of this becomes structured organisational memory.

That matters because businesses increasingly compete on how quickly they can interpret change.

But this is difficult when important knowledge often remains trapped inside individuals, departments, or disconnected software platforms. When that happens, leaders can spend a huge amount of energy rediscovering information the business already has.

The “big brain” concept reframed AI as a mechanism for reducing that fragmentation.

For some members, the implications were commercial. Structured intelligence on customers, competitors, and market positioning should lead to stronger targeting, sharper proposals, and more informed strategic decisions.

But for others, the opportunities were operational. Meeting transcripts become searchable learning systems rather than forgotten recordings, and internal documentation evolves continuously, rather than becoming obsolete and repetitive, which will result in briefing efforts beginning to disappear.

SEO and GEO

There was a specific focus on marketing visibility and how it is changing in light of AI.

The session looked at digital trust signals as an example, including Google Business Profiles, website consistency, reviews, and a structured online presence. Whilst SEO is still important, there is now also GEO (Generative engine optimisation) and AI-driven search increasingly rewards businesses whose information is coherent, visible, and continuously maintained.

That shifts from just doing SEO as isolated keyword campaigns to a program focused on building trust by being authoritative and providing structured, verifiably accurate information, which is what ranks better for GEO and therefore visibility on AI platforms.

Leadership implications

As AI systems become increasingly capable of synthesising information, leadership teams need to think much more seriously about the quality, structure, ownership, and flow of knowledge inside the business.

Many SMEs still operate through informal knowledge networks. A founder knows the client history. A salesperson knows the commercial nuance. An operations lead remembers why a process changed two years ago. That works up to a point but eventually, the business becomes too complex for memory-based management.

The “big brain” approach pushes leaders towards a different model.

Instead of knowledge remaining trapped inside individuals, the business gradually builds a structured layer of shared organisational intelligence. Not for the sake of documentation itself, but to improve decision quality, continuity, speed, and consistency.

This creates new leadership responsibilities.

Leaders now have to decide what information deserves to become part of the business memory. They have to establish standards around data quality and accuracy. They have to determine where human judgement still matters most. They also have to manage the cultural response.

Culture

Where there was internal resistance to AI it was described less as technical scepticism and more as fear of replacement, loss of control, or simple overwhelm. The framing of AI as a “team member” or support mechanism was seen as one way of overcoming this issue.

But the discussion also reflected on the following:

As businesses begin building structured intelligence systems, the role of leadership will shift from primarily solving problems personally to designing environments in which better decisions occur more consistently across the organisation.

That is a very different form of leadership maturity.

The operational examples shared during the session reinforced this. Automated meeting scheduling, AI-supported transcription, CRM integration, searchable customer history, structured competitor analysis, and AI-assisted content generation all reduce friction. But the real value comes from creating continuity.

The business stops starting from scratch every day, and over time, the benefits of that compound.

The companies that learn faster begin responding faster. They spot patterns earlier. They onboard people more effectively. They recover context more quickly. Strategic thinking becomes less dependent on individual memory and more dependent on collective organisational intelligence.

The key revelation

Think of AI not as software the business uses, but more as a member of the team. Members began discussing and exploring what could happen when a company's accumulated experience, conversations, documents, decisions, and operational learning become searchable, structured, and continuously improving.

The question stopped being “which AI tool should we use?” and became something much more strategic: what would change if the business could learn from itself?

Several members could immediately see applications inside their own environments, from client delivery and sales preparation through to sector intelligence, operational tracking, training, and strategic planning. The examples varied, but the underlying realisation was similar.

The value was in creating a business that deliberately retains, connects, and uses its knowledge, rather than treating AI as just a tool to be deployed.

Closing reflection

Most businesses treat AI adoption as a technology project.

This session suggested something different.

For SME leaders, the more important focus should be on building systems that help the business think more clearly over time.

The companies that benefit most from AI are unlikely to be the ones generating the most content or automating the most tasks. They are more likely to be the businesses that understand their own information, structure it well, and use it deliberately.

That requires more than software.

It requires leaders willing to treat organisational knowledge as infrastructure rather than a by-product, and once that starts happening, AI stops looking like a collection of tools and becomes much more like an operating model.

If your business had to learn from itself tomorrow, could it?