At the start of every single year, I think it's.
Important. To provide what we believe is
going to be the trends that. Shape the next 12 months of AI.
2025 was a year of huge improvement in what?
AI can actually deliver for people.
However, I think. Expectations are still.
Outstripping reality, 2026 is going to be another year of huge
progression, and in today's episode, we're going to talk
about the top. Trends that are going to shape
the world of AI this year, there's no doop with Jack
Harton. Hope you enjoy the show.
So we're actually 21 days into January.
I was going to take a couple of. Weeks off over Christmas to have
a break. Then was moving house.
Lost a bunch. Of things.
And even today, we're actually recording.
I've still not got the, let's say the studio to, so to speak,
set up. The actual SD cards are
somewhere still living in boxes from the Move, so I'm going to
apologise me wearing a silly headset because actually it was
probably the best microphone I could possibly get I think.
Hopefully. We're going to use an AI
software to make it sound as good as we possibly can.
So now anyway. Moving on to.
Trend number. One which I'm going to describe
in. A couple of words which is.
Differentiation for companies. So ultimately, right now,
differentiation 12 months. Ago in the AI space used.
To look a. Lot different.
To what it. Looks like today because
fundamentally the way users interact.
With AI is just converging on the exact.
Same pattern everywhere. Really.
You know every company building an AI product is.
Essentially a chat bot. You know, you type something,
the AI responds. There's maybe interactive
elements that pop up and appear. You know you've got Amazon's ad.
Management software's the exact same as Google.
'S and you know every single. Interface just starts to look
quite similar with unique. Parts of branding and.
Colours and vibe as well. But mostly it's a chat interface
and you know it makes. Sense there's over. 1 1/2
billion people. Using AI every single day right
now. And you know that that.
Expectation is transferred to every product.
So everyone, naturally. Is becoming a chat.
Bot and it's. Therefore, quite difficult as
organizations, as people to go. What is it that makes this
company unique Then? You know, if everyone looks the
same and it's an agent doing tasks for people, what makes
them, you know, unique? So what's happening is really
interesting. Questions being posed, which is
OK, therefore. What isn't isn't
differentiation. That's going to be a big
question of this year. My belief is.
It's not the interface anymore, it's 12 months ago strategically
companies building. New chat interfaces and
experiences that was. Strategic, clever work.
Now it's just. Not again, because building
better chat interfaces is really important, but doesn't
differentiate. Anybody you know, everyone has
to be solving that exact same problem the exact same way.
And you? Everyone has it, every user
expects it, so it becomes table stakes, so therefore it raises.
An interesting question if the. Chat interface or the interface
layer that used to be for companies, a place that offered
massive differentiation. You know, big companies like
let's say Salesforce or HubSpot have ecosystems and training
programs and certificates for becoming a master at my
software. They used to literally monetize
friction of having lots of things and features on a page
because a salesperson could easily point it in and say,
look, we do everything and it doesn't.
Matter that you don't know how because we've got big training
programs to make. You an expert?
So that used to be differentiation because you used
to. Look.
Complex, whereas now everything is a.
Chat interface and an agent orchestrates and just takes all
those actions. So therefore it's a really
interesting one because. The question becomes, where does
competitive advantage therefore come from and.
This is where I'm going to go to trend #2 which?
Is where companies are going to start investing effort.
So trend 2. Broadly is is where?
Companies are redirecting. Their efforts towards what let's
describe as their unique intelligence.
So what? Do I?
Mean by intelligence? So the intelligence.
Is about unique ways of solving customer problems in your
specific domain. So for example, if you're
building, let's say. For example, a company that is
selling sales software. Which is maybe they're going to
expose. Unique intelligence, which is
the ability to, say, predict which deals might close based on
five years of deal history. And maybe showing patterns of
historic. Alternatives and competitors and
behaviours from salespeople that have led to more deals being won
or less deals and that being exposed for an agent and a user.
So that's intelligence. So if it's a logistics platform,
so it helps people get packages from A-Z.
Maybe they can. Analyse millions of shipments.
Across every region in the world.
And then expose that to the customer through AI or if your
customer support platform for example.
It's understanding exactly. Which?
Issues really need human. Intervention based on historic
data. So that's what we mean by
intelligence. And that's where.
Companies, we believe. Over this next period is going
to be. Trickling down on.
Because if they expose more and more and more of that.
That is extremely. Unique and something no one else
has. Rather than spending all that
time on the chat interface, it's just about exposing that.
Value to. Customers and users.
So yeah, I think that's going to be a massive trend of companies
trying to. Expose that very unique
intelligence, rather. Than think through.
Long user journeys and experiences anymore because
ultimately. The chat interface all looks.
The same. It's the outcomes an agent.
Can deliver for a person and how clever those outcomes are.
That is what delivers value in the future anyway.
We're going to go to trend #3. Trend #3 is that.
AI conversations are about to. Become immensely visual.
So yeah, AI conversations are moving from like text.
Based to. Really visual, interactive.
Experiences. So you've seen this with Chatchi
BT? So Open AI and Chatchi BT
released integrations into Spotify, Expedia, Instacart, and
essentially the Spotify playlist renders in a conversation.
Or you can do learning. Through the big learning
platforms straight into track GBT and their platform appears
inside the chat interface. So that what's becoming a trend
really is that the chat interface.
Wants to render beautiful user interface components from
different platforms because fundamentally again.
The more outcomes and agent. Can deliver, the more valuable
it is, and so for all companies they want more.
UI. Components deliver more outcomes
in their chat. Because if you think about,
let's say. I want to select a playlist that
might have taken a conversation that lasted 10 minutes suddenly.
The user interface. Visual component appears in the
chart and you just select your playlist and press go.
So you want to book a flight. Instead of reading a description
of flight information, you just have a comparison of flights in
a visual format that has ticks and crosses.
For example, just as you would get on a normal website that's
straight from ChatGPT or straight from a conversation.
So you can bet. £1,000,000 that. Claude is about to do the same.
Microsoft, Apple, when they actually get their act together.
A slightly technical diversion here is that the model context
protocols, so MCP protocol. So MCP is actually expanding
what it enables you to do. So actually you can provide UI
components inside of that very soon, which means that as one
vendor to another or an organization, you can then let's
say you sell your maps software, you can not just put your maps
information into an MCP, you can put your maps user interfaces
into an MCP and then other platforms might go to surface
that up through a conversation. So yeah, that's.
The three trends on. Let's describe it as kind of the
interface part, so where the AI interface is changing.
I want to make a change of direction and move on to I guess
trends on how. Agents are built.
So trend #4. Of this list I think.
Agent reasoning is. Going to start to really
squeeze. And compete.
Rigid workflows or even clever workflows from the likes of
Zapier NAN. Lindy.
So the very big popular platforms for AI automation.
And I think really there's a. Big debate going on online all
the time. Which is you see a lot of
people. Sharing their great NAN workflow
that saved ME3. 1000 hours of time.
People see that, they play with it and it works, It makes
complete sense, and for many tasks it makes a lot of sense to
use that. But as a result, people assume
that's the way agents should be built and LED, and that's where
the fundamental debate comes in and you've got the likes of Lang
Chen. CEO who really complain that a.
Open AI release yet another workflow builder.
You've got anthropic complaining.
About the exact same. Because they don't.
Believe workflows are the future and so.
A lot of. Companies default to workflows
because they feel like they're able to.
Be in control, so I. Know this agent is going to do
this, then it's going to do that.
But often when? It comes to real world
complexity for, let's say, deploying workflows.
They're going to break because a human person wants to go in 100
different directions. And that's where agents who can
reason and create a plan like you.
Might see on cloud. So it creates a specific plan,
it asks itself questions, it ticks itself off on a plan and
then uses information to shape that plan.
That's what actually a workflow is doing.
But you're trying to build a workflow and try and plan for
hundreds of scenarios, whereas an agent can do that and change
it on the fly. So yeah, Zapier.
NAN and tools like that. I definitely feel attractive
because you know, you can drag and drop things, you can select
arrows, you can define logic for an agent must do this and then
that. You can literally see what's
going to happen. See, as I said, but life.
Tasks. And humans just don't like
following that sequence of steps, so.
If I wanted. To do a workflow, I might go and
set a step for saying. Check for duplicates and if
found. An issue?
Refund them, then check cancellations.
And if you find an issue, retry. Check the payments, but if
there's an invalid prompt for update.
But the duplicate. Charge was.
Actually an upgrade the cancellation.
Would then fail because there's a pending charge.
So everything becomes. Interconnected.
The workflow can't handle that level of interconnectedness and
chaos. It becomes very difficult.
Whereas agent reasoning can assess.
A full situation. Have a conversation with the
user. Identify the three issues.
And know that they're. Related because it is able to
analyse more information. And create a plan and again it
can. Change its plan and then check
in with the users Say is this good or bad?
See, I think a massive trend will be moving towards agent.
Reasoning because people who love cloud code and how powerful
it is and how effective it is and it's something we're mindset
tripling down on. Because I ultimately.
We see the value in that. It's where we're just releasing
new technology where I can look over millions of rows of data
and create a plan for how to. Click into very.
Complex questions and. Find the right data to help it.
Answer those questions. And give real value to users.
So trend 5. Is.
Again, within the theme of how agents.
Are built. And specifically, this theme is
how I think Building Agents is going to move to.
More conversational interfaces. And I think that's what's.
Going to get widespread adoption.
So I think it's going to move from quite technical
configuration or even. Workflows or even.
Code to configuration. Via conversations, you know
describe what the agent should do and the system should just
generate the configuration and this might expand who builds
agents. From maybe 2%.
Of people and knowledge workers to, you know, 50 percent, 60%.
Because right now, building an agent.
Requires quite a few technical skills and thinking a lot of the
time, even if you try and simplify it.
You know, Zapier and other similar tools.
Make you think like a programmer.
You have to understand variables and data types and condition
logic and error handling and code first.
Approach. Requires actual programming.
And the issue there is that. An agent's very rarely one and
done. You're then going to.
Have to maintain it and improve. It and change.
It So yeah, what I think is going to start emerging.
For agent building is very conversational studio
interfaces. It's something we're actually
about to start launching as AV two of our agent.
Building Google AI. Studio is now moving towards
that where you can. Configure these experiences.
Through pretty. Minimal.
Interface, really, so soon you'll be able to say I just
want an agent that monitors support tickets, figures out you
know, which ones are urgent and just reach them to the right
person. And if a ticket sits there for
more than 24 hours, escalate it. That's how we think and in terms
of instructions and the system should just know how to do that
and build that trend 6, I think. Agent testing is about to become
a major. Area of focus for all companies
because the problem. Often isn't AI.
Capability and This is why people gravitate, we think to
workflows the. Problem is confidence.
Confidence that agents are going to make good decisions
repeatedly in scenarios. Actually kind of matter.
Large scale AI LED training and testing is going to be what
builds that confidence because traditional say software testing
of a feature doesn't work Agent. Testing is quite different
because. Behavior isn't, let's say,
deterministic. Give an agent a support.
Query. It might handle it in five ways
over 5 separate occasions, even though it should be consistent.
Multiple. Approaches could all be correct,
but slightly different, which makes us all as humans feel
quite nervous. It's like a human.
And so the answer really for. Us is I think we're at mindset
and what we're going to expect us to see everywhere in the
world very soon, which is that often ahead of things by, you
know, three to seven months is scenario based testing at
massive scale using AI to evaluate everything.
So you. Describe the scenarios that
represent real scenarios or situations.
Define success criteria based on judgement and use AI to.
Test itself and. Test AI.
And I think this is all. Going to connect to ROI as well
because I think people are going to move from say vibes of AI
deployments to real ROI and impact of performance.
So that's how agents are. Billed as a theme set.
I think let's move on now to another theme which.
Is how work. Changes.
As a result of all of this and as part of this.
Let's go into. Trend 7, which is I think.
Knowledge work is. Going to start.
Shifting from doing. To now managing because all of
these things that we're going to start to see is going to.
Simplify agent creation. But make it more powerful
because workflows and complex interfaces is not what's going
to deliver wide scale, let's say, adoption amongst the
workforce, especially in knowledge work.
So I think all of these things are going to start culminating
in knowledge workers moving from say 1 or 2% or 8% of people
working really fast to let's say 50, sixty, 70%.
You're starting to see this now with Anthropic launching Cloud
Co work. Which is essentially.
Trying to give the experience of Cloud Code to.
Non-technical people because the number of non.
Technical people using cloud code again, trying to build and
provide an agent LED experience with a series of tools like I
was describing. No workflows and making it
really simple to manage for them.
So I think knowledge work is going to shift from.
Doing tasks to managing agents and therefore testing frameworks
and simplicity is going to be really important within this
theme, I think. Trend A is.
Going to be. The year.
Of agent manager. As a result.
And the reason I say agent management, not full autonomy is
because I don't think full autonomy is close.
I think we're quite a while away from that.
So I think that's why. Agents are so.
Powerful versus, say, a workflow.
Because an agent will. Do a series of tasks and then
check in with you, ask the user to make that judgement call,
make strategic decisions. But the agent will start to
increasingly handle more and more of the execution because
it's simpler just to provide agents tools and agents to
reasoning. So yeah, I think This is why
it's going to be so. Powerful The a lot.
Of these trends are just going to culminate in some of these
things because ultimately. Human judgements going to be.
Still really important, but agents begin to do powerful.
Things with powerful reasoning. Is going to be widespread across
many different products, not just Chatter, BT and cloud.
It's something that we're providing all of our customers.
For example, the ability to have reasoning agents and the ability
to run large scale testing on your agents and AI experiences.
Another big theme I think we're going to start to see as Tread 9
is forward deployed vibe coding. So.
We we shouldn't actually. Use the word vibe coding because
it almost, let's say, lowers the perceived value of using AI as a
coding tool. But anyway, there's a there's a
new role that's emerging more and more.
Which is people in departments who come.
Back who combine domain expertise with AI building
capabilities essentially. So not necessarily professional
engineers, but domain experts who can build solutions to
problems so these. People know really how to use
AI. Tools and the domain experts and
they forward deploy them to solve big problems and not get
it to let's say full production or ready for everyone to work on
and or to deploy to customers or launch to customers.
What we are seeing is they may have to solve big knotty
problems because maybe they're expert coders and they have
domain expertise or they're just good at AI tools and have
fantastic AI and domain expertise.
And the reason domain expertise is important is ultimately they
have a lot a lot of context to make good judgement calls and
solve big problems for companies.
And that could be. Ideas for new customer products?
It could be solving big problems for teams and essentially
building tools and systems using AI and then eventually handing
it over to the rest of the team. So these are the trends that we
think will. Define AI in 2026.
The interface is experiencing the biggest revolution since
command line interfaces. Companies will be investing more
and. More in their own data to
provide as unique intelligence to their users and customers and
ultimately. You'll see more.
Deployments than ever throughout 2026.
So that's it for this week in this episode.
I hope you found it useful. Next week we'll have a proper.
Microphone, camera and setup. But yeah, we'll see you next
week.