Summary
Here we explore how AI is reshaping organisations in 2026 through conversations with Alan King (AI-Your-Org, ITAA.ai, The Global AI Association) and Hariss Amin (eCommerce - Hunter Douglas Inc.). Learn where AI delivers real value, what leaders get wrong, why governance matters, and how to build an AI strategy without wasted spending. Includes event details for our January 2026 session on AI adoption for product and CX teams, plus practical guidance on starting or scaling your AI journey.
Working with AI, not just using it
A conversation with Alan King and Hariss Amin
Read time: 6 minutes
In January 2026, I'll be speaking with two people who've been implementing AI inside organisations rather than debating it from the sidelines. Alan King helps boards and senior leaders build AI strategy through AI-Your-Org, ITAA.ai and The Global AI Association. Hariss Amin leads AI and CX Digital Products across 11 countries at eCommerce, Hunter Douglas Inc., where AI delivers commercial outcomes at scale.
I met with both separately to explore the same questions: What must organisations focus on in 2026? Where are the real risks? Where is AI already delivering value?
Here's what they shared.
When AI stopped feeling like hype
Alan remembers the exact moment. He'd spent years testing early chatbots that hit a wall. They were clever, but predictable. One afternoon, he tried ChatGPT 3.5. Within minutes, he was in a conversation that held its thread.
"Two hours disappeared. It felt like stepping ten years into the future."
For Hariss, the shift was quieter but more significant. AI stopped being a separate system and started removing obstacles where customers got stuck. Calls became shorter. Journeys improved. Repetition for teams has been reduced. It wasn't a project anymore; it was part of the operation.
What to focus on in 2026.
When I asked what organisations should do if they feel behind, their answers came from different angles but pointed to the same problem.
Hariss sees organisations buying tools before they've defined the problem. The pressure to "do something" leads to wasted spending.
"I've seen companies sign up for five or six different AI tools without any scope defined. A year later, they realise four bring no value."
His approach: map the problems first, prioritise properly, run structured pilots, and only then choose the technology.
Alan looks at the organisation’s structure. He argues that AI changes the hidden rules determining how teams operate, how decisions are made, and how work flows through the business.
"AI isn't a plug-in. It changes everything underneath. If you just attach it to legacy structures, you won't keep up with companies building AI into the foundations."
For him, the first task is strategic. Leaders need a clear picture of what their organisation should look like in five years, and how AI supports that shift. Without this, teams experiment in isolation and progress stalls.
This is why we built our AI Discovery Mapping workshop. Most teams know they need to move, but don't know how to move with purpose.
The sceptical executive
I asked both how they'd speak to a leader who still sees AI as optional.
Alan was direct,
"If leaders think AI is optional, they're not looking outside their own walls."
Markets are moving. Competitors are restructuring. Smaller teams are adopting automation because they've always had to work efficiently. Waiting for certainty isn't neutral. It's a strategic decision with consequences.
Hariss sees it through customer expectations. Service speed, quality, and availability: all are being reshaped by AI. Customers won't wait longer because an organisation hasn't adapted. The pressure builds on teams, and without a plan, the rush to catch up becomes harder.
Dangerous assumptions
When AI projects fail, the cause is rarely the model, as it is usually the assumptions surrounding it.
People assuming AI arrives with all the knowledge it needs is one belief Hariss witnesses repeatedly.
"A lot of people think AI will solve everything without giving it anything. But unless you train it, it can't answer those questions."
This is where teams discover their documentation is outdated or inconsistent. AI exposes the gaps they've lived with for years. Closing those gaps takes effort, not enthusiasm.
Alan points to governance, or the lack of it. He's seen organisations launch pilots with no clear owner, no boundaries, no monitoring plan. Mistakes only become visible when customers receive something unexpected.
"I see organisations piloting AI without real thought. No guardrails. No ownership. It's rarely the AI's fault when it goes wrong."
Both agree: you need structure, ownership and regular review.
Where AI is already working
Hariss described a typical customer service call before AI. Customers navigated long menus, waited for agents, and repeated details multiple times. Agents searched several systems before answering simple questions.
His team redesigned the flow. They removed the IVR, automated validation steps, and gave agents what they needed instantly. Minutes came off every call. The experience became faster and calmer for customers, more efficient for the business.
Alan talked about knowledge-heavy teams. Scanning long documents, comparing clauses, flagging inconsistencies: this work took hours. AI does it in minutes, leaving specialists free for analysis and judgement.
What stands out in both cases is the simplicity. These aren't futuristic systems. They're well-scoped, well-governed, and designed around real user needs.
The sectors under pressure
Sectors built around reading, writing or analysing information will feel pressure earliest. Legal work, compliance analysis, advisory functions: these will change shape quickly.
Roles based on physical presence, empathy or hands-on care will adopt AI more slowly, as support rather than replacement.
Alan noted the pace,
"The internet was fast, but this is different. We may have five to ten years to adapt."
That compressed timeline makes gradual change harder. Leaders will need to make decisions earlier and with less certainty than they're used to.
Where we go next…
Across both conversations, a pattern emerged. AI is useful where obstacles slow organisations down. It improves speed, quality and cost when applied thoughtfully. The challenge is moving with purpose and doing it responsibly.
Organisations that progress fastest share the same habits: they pick meaningful problems, know where their data lives, build in guardrails, and bring leadership along. They see AI as a long-term strategy, not disconnected tools.
Event Details:
383 x Canvas Events - Smarter, Safer AI for Product Teams
January 2026 | 10 - 11 am (UK)
Speakers:
- Sukhi Dehal, Founder & CEO of 383 & Canvas Conference
- Alan King, Founder of AI-Your-Org, CEO of ITAA.ai and Strategy Director for The Global AI Association
- Hariss Amin, Global Head of AI & CX Digital Products, Hunter Douglas
What we'll cover:
- Embedding AI throughout product development
- Identifying and prioritising valuable use cases
- Aligning AI with governance and strategy
- Building momentum without unnecessary risk
- How our AI Discovery Mapping Workshop creates clear adoption pathways
Why attend:
If you're unclear on AI's role in your organisation, uncertain about practical applications, or unsure how to start safely, this session gives you structure and next steps
Frequently Asked Questions
Q: Do we need an AI strategy before attending this session?
A: No. The focus is on clarity and practical starting points, not maturity level. You'll leave with a clearer sense of what your AI strategy should include.
Q: Is this relevant for non-technical teams and non-product roles?
A: Yes. AI affects operations, service design, governance, data management and leadership culture. The content applies to CX teams, operations leaders, senior executives and anyone responsible for organisational change.
Q: Will you cover AI governance and risk management?
A: Yes. Governance failures are one of the biggest barriers to AI adoption. We'll cover responsible implementation, safe pilots, how to avoid reputational damage and what guardrails look like in practice.
Q: Do I need technical knowledge to understand the content?
A: No. This session is designed for senior leaders, product owners, CX teams and innovators. The emphasis is on decisions, strategy and business outcomes, not technical implementation or code.
Q: Are the examples based on real AI implementations?
A: Yes. Both speakers share examples from large, complex organisations where AI is delivering measurable value, including specific cost reductions, time savings and customer experience improvements.
Q: How do we identify which AI use cases are worth pursuing?
A: We'll explain the approach behind our AI Discovery Mapping workshop, which helps teams evaluate opportunities, prioritise based on impact and feasibility, and build realistic adoption roadmaps.
