Struggling with AI strategy? In this episode of Impact with Digital, your host Jay Tikam introduces the AI SWIFTER Framework, a 9 Pillar strategy designed to guide CEOs, business owners, and executives through successful AI implementation.

This is part two - a follow on from Part 1, where we focused on the AI Hype and noise that leaves leaders overwhelmed and confused about their next step to AI strategy, in a world where everyone is pushing them to do something - rather than do the right thing.

If you are tired of the AI hype and the fear of costly mistakes, or the fear or missing out, this episode gives you a strategic blueprint for building AI solutions that deliver real business value and has the potential to SHIFT your business to new unthinkable heights.

Jay Tikam dives into each of the 9 +1 Pillars of the AI SWIFTER Framework:

• Alignment: Ensuring AI solves the right problem for the right people at the right time when needed in your business.
• Innovation: Driving purposeful AI innovation within your organisation and most importantly, creating an environment for experimentation.
• Skills: Developing the necessary skills for AI Success, & finding hidden talent that may already be lurking within your company.
• Workflow and Adoption: Effectively integrating I into existing workflows and ensuring the solution developed is actually used and proves valuable for stakeholders.
• Infrastructure: Building a robust AI infrastructure to cope with the demands of AI solutions.
• Frameworks & Models: Choosing the right AI tools and techniques.
• Tracking & Learning: Measuring AI performance for continuous improvement.
• Ethics & Controls: Implement AI responsibly and ethically, being mindful of risks and managing reputation.
• Results & Scale: Achieving tangible business outcomes and scaling AI solutions with repeatable modular approach that speeds up innovation and development. Ensuring the AI solution delivers enduring benefits rather than just following a latest craze.

The +1 component is present across all 9 pillars and its known as DISCOVERY. It plays a crucial role as an ongoing process for AI Strategy. Learn how to avoid AI failure and unlock the transformative potential of AI with the AI SWIFTER Framework.

If you're a business owner, a CEO, or an

executive, are you feeling the familiar

2

unease around all the AI hype, the sense

that you need to be doing something,

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yet the fear of making the wrong

potentially costly move can be daunting.

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You're not alone.

5

I speak with CEOs and business owners

regularly who wrestle with this exact

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tension, the risk of inaction versus

the peril of going down a wrong path.

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Or even complete failure

of the AI initiative.

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That pressure resonates with you.

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Then you, me out for the next few

minutes, because in this episode I'm

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going to lay out nine fundamental pillars.

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Think of them as your strategic blueprint

for navigating the complexities of

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ai, allowing you to make confident,

impactful decisions, not just chase

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the latest sexy trend or AI hype.

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Stick with me and discover all

the nine pillars in detail because

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leaving one out may just put your AI

initiative down the path to failure.

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I promise you clarity and

confidence is within your reach.

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Now for those of you who joined me

last time in episode six, part one,

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you'll recall we took a deep dive into

the executive mindset surrounding ai.

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We unpacked what internal

struggles many of you are facing.

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The tug of war between the fear of being

left behind and the very real concerns

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of investing down a dead end path.

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We explored what failure means and

actually looks like some AI projects

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seemingly succeed from a project

perspective, but fail when implemented

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in the real day-to-day business.

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And you'll recall that we identified

eight often overlooked factors that

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can lead to AI initiatives failing

before they even truly begin.

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If you happen to miss conversation.

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Don't worry.

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This episode stands entirely on its own.

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However, if you're curious to delve

deeper into those potential pitfalls,

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I'll include a link in the show notes.

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But today, our focus shifts,

we're moving beyond that initial

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apprehension and stepping firmly

into the path of proactive strategy.

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We're transitioning from

guesswork to decision making.

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To guide us, I want to introduce you to

what I call the AI swifter Framework.

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Now, this isn't just another

trendy acronym to add to your list.

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This is a structured approach, a

set of guiding principles designed

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to bring clarity, confidence.

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A clear sense of direction to your

AI journey, regardless of where

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your organization currently stands.

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However, before we dive into the

specifics of AI swifter, there's

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a more foundational element.

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We need to discuss something that

underpins every single aspect

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of successfully integrating

AI into your business.

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That process is called discovery.

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It's the ongoing radar

that guides everything.

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Think of discovery as your always

on radar in the rapidly evolving

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world of artificial intelligence.

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It's not a one-time task, it's an

ongoing discipline, a continuous

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scanning of the horizon.

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Why is this so crucial?

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Because without this constant process.

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Discovery.

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Your AI decisions risk

becoming purely reactive.

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You might find yourself chasing the

latest headlines, blindly following vendor

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pitches, or even worse, being swayed

by consultants, pushing solutions that

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aren't truly aligned with your needs.

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Discovery provides you with a

space to observe, to thoughtfully

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analyze, and to lead with a sense

of calm and informed perspective.

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It eliminates the fear of missing out

because discovery provides concrete

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information and turns feelings of

overwhelm into calm, reassurance, and

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shows up real opportunities of ai.

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Ultimately, it ensures you are

well prepared when AI genuinely

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merges as as the right solution for

your unique business challenges.

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All the angles to the

framework I'll cover today.

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How do you implement

this discovery process?

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It doesn't need to be overly complex.

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Establish a simple, consistent

system for regularly scanning

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industry news, keeping tabs on your

internal initiatives, monitoring

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your moves, and staying abreast of.

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Pay particular attention to emerging

disruptors in your market and keep

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an eye on new technologies, AI

models, and emerging methodologies.

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For now, simply gather this

information, file it away.

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The initial goal is just

to learn and understand.

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Maybe you can assign a small team

or even rotate the responsibility

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to champion this discovery process.

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Perhaps create a shared

document or a simple dashboard.

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The key is consistency.

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It needs to become an ingrained

habit within your organization.

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What exactly are you looking

for in your discovering scans?

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Keep an eye out on the following.

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Number one, emerging

AI tools and platforms.

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Number two, innovative AI-driven business

models that are gaining traction.

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Number three, practical and impactful

AI use cases relevant to your industry.

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Number four, the ever-evolving

ethical considerations surrounding ai.

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Crucial regulatory changes

in the AI landscape.

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And number six, importantly pay

attention to both the successes and the

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failures of AI implementation by others.

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There are valuable lessons

to be learned from both.

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So what happens if you neglect this

foundational discovery process?

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Quite simply, you risk

becoming a victim of hype.

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You might end up investing in

systems you don't truly need.

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Or worse, you live with a constant

state of mental anxiety, unsure

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of what to do, who to trust, and

ultimately missing the strategic

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path that's right for your business.

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So who is responsible for discovery?

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While everyone in organization

plays a role in being aware of d.

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Designate a few passionate individuals.

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Perhaps those natural enthusiasts who

love technology and digital innovation.

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They can be your dedicated radar watchers.

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These champions can help guide the

rest of the organization, inspire

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curiosity, and foster a culture of

AI awareness within your workspace.

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Discovery is what provides you

with that crucial breathing room in

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the often turbulent waters of ai.

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It transforms potential chaos

into confident decision making,

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and it's a discipline that will

serve you exceptionally well in

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every AI related conversations and

decisions you make moving forward.

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You'll be informed, you'll speak with

conviction and your choices will be

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rooted in objective understanding.

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So, all right, now that we've

established the critical importance

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of having your radar constantly

scanning, let's delve into the heart

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of our discussion today, the nine

pillars of the AI Swifter framework.

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Each of these pillars is intentionally

designed to help you build AI solutions

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that deliver genuine sustainable

value, creating a tangible shift

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into your in your business, and

potentially unlocking opportunities

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you haven't even considered yet.

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It's important to understand

that these pillars aren't

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necessarily a linear process.

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Rather, they represent the

essential foundational elements.

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That should inform, inform every

AI initiative you undertake

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within your organization.

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So starting with the A in ai swifter

alignment at its core alignment is

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about ensuring your AI efforts are

directed at solving the right problems

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for the right people and in the right

way, ensuring it truly serves your

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overarching business objectives.

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Stakeholder needs.

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Often AI initiatives are technology led,

going down a path propelled by hype,

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implementing solutions not aligned with

the business goals or stakeholder needs.

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The alignment component

tackles this challenge head-on.

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When implemented, you are 100% certain.

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That you are building, that what you are

building is needed in your organization

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and is most likely to move the performance

needle if implemented correctly.

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Why is this so critical?

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Because AI that isn't strategically

aligned risks becoming a mere sideshow

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flashy technology that doesn't

address any meaningful business

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challenges or provide real value.

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It's a tail waacking the dog analogy.

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How is the alignment pillar implemented?

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The process of alignment begins with

identifying your stakeholders and

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understanding how they play their

part in your organization's ecosystem.

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As I've already highlighted in previous

episodes, it's helpful to categorize

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stakeholders into three key groups.

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First, those you serve,

as you will remember.

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Those are your shareholders, your

customers, or the communities you impact.

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Second, your enablers, your

staff, your internal teams, your

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partners, and the key vendors.

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And third, your influences.

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These are your board of directors,

regulatory bodies, and your investors.

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Each shareholder group has their

unique needs and aspirations that

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can be fulfilled by AI solutions,

but more importantly, you must

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understand how solving problems of one

stakeholder group impacts the other

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two groups that we just spoke about.

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So, for example, to implement

an AI client portal.

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You'd need to provide staff with

tools to upload information and

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step in where AI can't provide the

information required by your clients.

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Going even further, you need AI

driven performance dashboards

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showing the influencers, in other

words, the executives that the

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AI solution is paying off and is

positively driving business growth.

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Cost reduction.

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What do you need to do to get

alignment of AI solutions to

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stakeholders and business needs?

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You effectively understand

and achieve alignment.

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You need to start with stakeholders,

categorization I just told you about.

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Identify stakeholder needs through

interviews and focus groups or

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surveys to gather direct insights.

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Carry out stakeholder journey

mapping to visualize the experience

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you are aiming to improve.

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Note I said stakeholders,

not just customers.

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You need to factor all three stakeholder

groups we've just covered earlier.

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So map out stakeholder needs, problems

and aspirations on an impact effort matrix

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to help prioritize initiatives that will

give your business the greatest results.

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The most optimal investment

of time and capital.

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So what if you skip this crucial

step of alignment, you might

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very well end up building a

technically brilliant AI solution.

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You may even build it in record

time and super efficiently

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within budget estimates, however.

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You will build an AI solution

that ultimately no one needs or

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even worse, no one actually uses.

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You'll most likely end up building

a solution looking for a problem.

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It's therefore no surprise why more than

85% of AI initiatives actually fail.

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Alignment is a North Star guiding your

AI led transformation based on the needs

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of your business and your stakeholders.

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So who's responsible for

the alignment pillar?

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While your leadership team and business

analysts will likely take the lead in

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this area, it's absolutely vital to

involve end users early in the process.

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Listen attentively to feedback from

across your entire organization.

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So let's talk about innovation.

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The first I in the AI swifter

framework, in our case, innovation

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isn't that shiny buzzword heavy.

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Outta this world kind of innovation

that's all about being first to market.

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What we're interested in here

is purposeful innovation.

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Nothing groundbreaking, but

something new to your organization.

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Innovation that's rooted in business

value and solves something real,

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not just chasers the latest trends.

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Why does this matter?

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Because without a structured approach to

innovation, you're left with two extremes.

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Either your businesses or you waste

time, spinning wheels on disconnected

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side projects that really go nowhere.

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So how do you build innovation

that actually works?

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You have to start by creating

safe spaces to test ideas.

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No one will want to innovate in

an environment where they get

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fired for trying out new things.

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Fail.

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Ultimately encourage bottom up

input, not just top down vision.

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Find ways to capture the ideas from

the frontline, from people who see the

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opportunities and the problems while

serving stakeholders on a daily basis.

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You can run small pilots with

room to fail, followed by

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reflection and adaptation.

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That's how you get real

insights and shift.

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What could the innovation

process look like?

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Remember the discovery

process I spoke about.

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This is an ongoing process and it'll

give you new ideas inspired by AI led

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business models and new technologies

that show you what's possible.

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Discovery is the foundation to

innovation because it gives you

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ideas of possibilities that you

won't even have thought about.

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Don't stick to your industry.

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Explore new AI technologies and business

models emerging in other industries

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that has got nothing to do with yours.

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So innovation starts with brainstorming

in a safe space where ideas from all

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levels of the company can flow freely.

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No hierarchy.

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No politics and no egos.

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If you're brainstorming online,

use whiteboard tools to capture

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ideas from remote teams.

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Make it a fun and creative process.

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Remember, innovation shouldn't

just be for the sake of innovation.

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You are innovating to solve a pressing

problem or delivering on the needs

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of one of your stakeholder groups.

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Not just customers.

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Remember also that by going through

the alignment pillar, you'll be finding

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innovative ways to solve a problem that

is surfaced as a priority, one that will

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deliver the highest return on capital and

time invested ideas don't only come up in

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well structured brainstorming sessions.

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They are spontaneous.

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It can come from anywhere.

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Maybe when one of your team is

dealing with an irate customer.

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Maybe when your employee is frustrated

by a complex and unnecessary process.

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Or maybe when managers can't monitor

the performance of a business, or

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maybe it comes to you as the leader

in your business when you are in.

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You really need to develop a mechanism

to capture ideas where they strike.

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This is in addition to the

formalized brainstorming process.

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For example, keep suggestion

boxes around your office.

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Develop a dedicated email

address to capture ideas from

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across the organization or.

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Develop a web form to capture

these innovative ideas.

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Reward people that come

up with great ideas

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after ideation.

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The idea must translate into AI

prototypes that can be tested.

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Of course, only if this makes sense

and passes the should we do it test.

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Maybe you can run a design sprint around

a specific and well-defined problem.

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Host internal innovation

contests like hackathons.

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Alternatively, you can prototype

quickly, share the solution with real

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users and listen to their feedback.

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Ask them how the prototype can be

improved to make their lives better.

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There are many resources and books

out there on the innovation process,

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so I won't cover it here, really,

but what if you skip the step of

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innovation in the AI swifter framework?

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When innovation isn't guided by your

strategic objectives and stakeholder

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needs, you'll end up chasing novelty

and hype instead of progress.

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You pursue innovation for

innovation's sake rather than

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for improving your business and

how you serve your stakeholders.

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When going down the wrong type

of innovation, you burn time,

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budget, and goodwill on things

that don't really move the needle.

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Okay, so who should be

responsible for the process of

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innovation in your organization?

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Naturally, the r and d

team comes to mind, right?

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If you run a small business,

you'd think it's the job of the

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CEO and the other executives.

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However, innovation should be

everyone's job from the front line.

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To leadership because the best

ideas often come from where

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the problems are felt the most.

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In business, innovation

can't happen in a lab.

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Ideas can come from anywhere, and

you have to have a system to capture

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and quickly evaluate new ideas.

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Okay, so let's now turn to S

in the AI swifter framework.

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S stands for skills.

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People, most people will be

forgiven for thinking that

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AI is a technology challenge.

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We associate AI with data methods

like large language models or buying

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and deploying super fast computers

or installing internet of things,

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sensors that hook up to AI systems.

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But make no mistake, AI

is powered by people.

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People build it.

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Deploy it, question it, use it, and

clean up after it when things go wrong.

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So why focus on people?

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Because people make AI happen

and it's essential that you

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have the right mix of skills.

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You definitely need deep technical skills,

but you also need skills that motivates

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and empowers humans to use the AI systems.

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As you already know, this is often

referred to as change management skills.

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You also need project management

skills, people who can teach others

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how to use AI systems, experts who

understand risks and the ethics of ai.

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Process improvement specialists,

et cetera, et cetera, et cetera.

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So how do you get these skills on board?

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Train up your people, staff and

executives hire new skills if you are

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able to attract them in a competitive

market, outsource or bring contractors

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on board to fill a temporary demand.

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How do you approach the skills and people

component of the AI swifter framework?

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You could start with a simple

capability map, taking stock of the

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skills you already have on board.

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Remember, you may have an AI

expert hobbyist working in a

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completely non-AI related business

function, totally being wasted.

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When considering your AI goals, you

have to find these people and make sure

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they are using their skills effectively.

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After all, it's a win-win.

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They will be much happier in the job

while you have just acquired an AI

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expert or inspiring expert without

the pain of hiring or training.

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Once you know what skills you have

on board, now you can identify the

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gaps, gaps, gaps based on where

you are today against what AI

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initiatives will be in place in future.

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This can only happen once you know what

AI solutions you are going to prioritize

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and implement, and this comes from work

you've already done in the alignment and

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innovation pillars we've already covered.

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You'll need to figure out how to build

or acquire these skills in a market

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with a demand for ai, technical and

soft skills currently outweigh supply.

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Yes, this can be challenging and you

will need to find new ways to attract

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top talent away from your competitors.

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The Skills and People Pillar is

all about building a team that

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not only understands the tech,

but also the context it lives in.

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What happens if you don't develop

the skills and people pillar

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of the AI Swift framework?

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You may end up building

or buying a system.

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No one is equipped to implement in

the business implementation stalls.

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Risks escalate and the expected results

don't materialize without the right

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people, it makes little sense to embark

on an ambitious AI system build things

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will just come to a grinding halt until

you can get those skills on board.

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In whichever way you need

to get them on board.

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If you forgot everything I just told you

about skills, remember this one thing.

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You may already have an AI expert in your

company that you don't even know about.

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People pursuing AI qualifications

in their private life building

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hobby AI projects and side hustles.

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Find them and develop

an AI career for them.

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So who owns the skills and people

pillar of the AI swifter framework?

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Again, it's not only the

responsibility of the HR department

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to find and develop AI skills.

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This really is a team sport.

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Anyone leading people play a role here,

whether it's HR department heads, or the

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Learning and Development Department, or

its employees recommending their friends.

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Possibly also their family.

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Let's now turn to the W in the AI Swifter

Framework, workflows and Adoption.

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This pillar is all about the use of AI

systems that are built and deployed.

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It's about embedding the AI systems,

IT workflows, and adoption by users

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across all stakeholder groups.

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In my experience, this is often the

most overlooked part of AI initiatives.

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'cause even the smartest tech in the

world is useless if no one uses it.

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Executives and decision makers

often get caught up in promise of

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technology thinking it'll deliver

the promised results even if the

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system is right for the business.

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Implemented with precision, it makes

no difference to the business if

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such systems are not actually used.

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So think about this.

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How could an organization

end up going through the cost

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and effort of implementing a

system that's not even used?

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What's going on here?

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This situation can easily happen

when AI initiative start with a tech.

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Leaders are often mesmerized

by technology and it's promise.

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You buy it and implement it, but

not because there was a need for it,

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but because you believed that the

promise sold to you by the vendor.

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Go back to episode one where I talked

about an AI chat, Bott example.

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The bank had a predominantly

older customer base who didn't

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want to use the chatbot.

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They prefer speaking to a real person.

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Also, in that example, when chatbots

start giving wrong answers, staff

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will obviously discourage their

customers from using these chatbots.

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A key missing component here is the

alignment step in our AI swifter

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framework, which was ignored.

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Really, the AI system implemented without

stakeholder needs assessment risks.

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Why does adoption matter so much?

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Because shelfware doesn't deliver

any benefits, let alone return on

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investment adoption of AI systems.

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In other words, AI systems that

are actually used by the target

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stakeholder groups is what turns

potential and promise into performance.

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So how do you get it right?

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Simple focus on the alignment pillar

in the AI swifter framework, and

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make sure the AI system is addressing

a genuine desire or aspiration

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of a specific stakeholder group.

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Then design it with a user in

mind and not for them without

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any involvement from them.

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You've gotta watch what people

actually do, not just what they

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say they do when using a system.

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Are there too many steps in

the user journey that gets

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them to abandon the AI system?

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Is the user interface

clunky and confusing?

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Are the colors on the app

or webpage overpowering?

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All of these things really do matter.

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You must co-create workflows that

fit into the reality of your users.

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You start with a prototype,

but then get feedback from as

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many actual users as possible.

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Look out for problems, listen to

their feedback and suggestions.

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Ask them how the AI

solutions could be improved.

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Iterate, test, get feedback, and repeat

the cycle until you get a system which

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users are unanimously happy with.

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Of course, you can't please everyone

and naturally some users will still

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dislike certain features of workflow.

395

That's just part of the process.

396

What are some ways or

practices to ensure adoption?

397

Let's just explore a few

tools and techniques.

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These are only examples, really.

399

Number one, journey mapping and testing.

400

Number two, shadowing

actual users on the ground.

401

See how they use pilot systems,

learn what works and what doesn't.

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And number three, prototyping tools in

real environments with real users, not

403

just in boardrooms or innovation labs.

404

In some cases, even in the AI

system, is beneficial to the user

405

and co-created with their input.

406

Users may avoid using it deliberately

because of a fear that the AI system

407

is going to take away their job.

408

Some may just resist change

because they like doing things

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either have always been done.

410

What if you don't focus on the workflow

and adoption pillar of the AI framework?

411

You won't get the intended stakeholders

using the system, a wasted investment

412

of time, money, and effort.

413

You can lose trust for the

users when a system is forced

414

on them without their input.

415

People work around systems

instead of working with it.

416

People resist the change, and AI

becomes just another failed project.

417

The who should lead this

workflow and adoption.

418

Ideally, you give this responsibility

to a digital transformation

419

lead or a change manager.

420

However, success depends on close

collaboration with operational leaders

421

to align workflows with day-to-day

realities with HR people, teams for

422

using training, training, change,

communication, and behavioral change.

423

Frontline staff for systems that

are intended to be used by customers

424

and other stakeholders like joint

venture partners or suppliers

425

IT or product managers to ensure

the technology is implemented

426

with usability in mind.

427

Change management champions in

every business unit can certainly

428

go a long way to encouraging

widespread adoption of the AI system.

429

Let's now move on to the second I in

AI swifter framework infrastructure.

430

This is in effect the nuts

and bolts of the AI system.

431

Your infrastructure is the foundation

and it includes a component

432

such as computer resources.

433

You need high performance computers that

can scale as AI technologies advance.

434

A more practical solution could

be to use cloud computing with

435

platforms like AWS Azure and Google.

436

They offer on demand access to vast

computer resources, which you can

437

scale up or down as you require

without having to invest in buying

438

and building the system yourself.

439

Many highly regulated industries will

often need to invest in their own service.

440

Where they can keep the data locally and

have the computer power on tap instead

441

of sharing it with others in the cloud,

442

you'll need data storage

and management solutions.

443

AI models learn from a vast amount

of data that need to be extracted,

444

cleaned, and stored in a systematic

way so that it can be retrieved easily.

445

Data, after all is the

lifeblood of AI systems.

446

Whether it's your data or external

data available in public libraries.

447

AI also needs high speed networks

and connectivity load as systems

448

need to quickly upload and download

data to perform their functions.

449

Such data transfers must be possible

across different types of systems.

450

Most importantly, the network

over which data travels.

451

Must be secure, protecting sensitive

data and systems against hackers.

452

So the AI system will need

sophisticated security and compliance

453

infrastructure that are kept up to date.

454

Why does this infrastructure

pillar matter?

455

Because your AI initiatives

will be limited by your infras.

456

If your hardware can't cope with

the demands of AI solutions,

457

whereas if your infrastructure

fails or doesn't function properly,

458

you don't get, you don't get AI

transformation, you get AI chaos.

459

And with AI's ability to automate

things can get out of hand very quickly.

460

Putting your organization, your

customers, and other stakeholders at risk.

461

So how do you avoid that?

462

You start by assessing what you

already have, what systems can

463

integrate, what need replacing,

what's your data quality really like?

464

Remember, all those gap analysis is

based on work you've already done in

465

the alignment and innovation pillars.

466

These two pillars are foundational and

they ensure you are building an AI system.

467

Designed to meet stakeholder needs

that shift the business to new heights.

468

We won't go into the technicalities

of AI infrastructure.

469

There's already quite a bit of

information that is already out there.

470

Vendors will be pleased to inform

you about the hardware so you

471

can get information from them.

472

Maybe we can dedicate a future episode

of this topic or better get some

473

infrastructure provider guests to go

through all the infrastructure components

474

and the technology stack you will need to

consider when building your AI solution.

475

So what's the risk if you skip

this infrastructure better?

476

Well, your AI system just won't work

no matter how well they're designed.

477

System flaws and security breaches will

expose you legally and reputationally.

478

So who owns the hot

infrastructure component?

479

Fortunately, this is one area you

don't want everyone involved, just

480

IT experts, your IT team, your data

architects and your cybersecurity

481

teams will take the lead.

482

If you don't have these teams, you can

outsource or bring incredible freelancers.

483

They're not back offers.

484

They're mission critical.

485

In this case, definitely rely on

vendors, but only once you are clear on

486

what AI systems you are implementing.

487

And most importantly why as an

executive or owner of the business,

488

you don't need to understand the

nuts and bolts of infrastructure.

489

However, you need to have a grasp.

490

Of the basic infrastructure needed for

AI and keep abreast of new developments.

491

Make sure you have a strong IT

and hardware team, whether they

492

are in-house or outsourced.

493

So next we moved the F in the AI swifter

framework, frameworks and models.

494

Now we're talking about the engine under

the hood, the powerhouse of AI systems.

495

This is where the models live.

496

The logic that drives your AI

talking, we are talking about

497

algorithms, techniques, and software

tools to build and train AI models.

498

It's essentially the brain of the

AI system while infrastructure

499

was the hardware and data.

500

Here we focus on the software briefly,

we've got the following AI frameworks.

501

Number one, machine learning

frameworks and libraries, like deep

502

learning frameworks like TensorFlow.

503

PY Torch and Kara.

504

Classical machine learning libraries

like T Natural Language processing

505

libraries like hugging face transformers,

data processing frameworks for

506

model preparation like Apache Spark.

507

The core techniques used to

learn from data by AI systems

508

include supervised learning.

509

Unsupervised learning, reinforcement

learning, computer vision

510

processing, and generative models.

511

Again, we won't delve into

the technicalities of models,

512

methods, and frameworks here.

513

Remember, this is a means to an end and

not the starting point of AI systems.

514

As an executive or owner of

the business, you only have

515

to understand basic concepts.

516

Keep on top of developments.

517

Why does this pillar matter?

518

For obvious reasons, the frameworks

and models component is crucial.

519

Think of them as sophisticated

construction kits designed

520

specifically for building ai.

521

It's like a Lego brick for building a toy.

522

AI frameworks can accelerate

development because of prebuilt

523

components, libraries and function

524

complex.

525

Make AI development more accessible.

526

They also ensure you are following

standardization and best practices.

527

AI models and methods, on the other hand,

translate data into insights and actions.

528

AI methods help computers learn

patterns and relationships.

529

They also enable generalization.

530

What does this mean?

531

The AI.

532

To apply learned knowledge

to novel situations.

533

In other words, demonstrating

intelligent behavior.

534

Different AI models and methods can

address different and diverse problems.

535

So it's crucial to choose the right model

or method for your specific problem.

536

Frameworks, models and methods allow

you to explain how your AI work.

537

If you can explain the output,

you can trust the model.

538

Your stakeholders can have

faith in their outputs, but how

539

do you choose the right model?

540

Pick the simplest tool

that solves the problem.

541

Aim for models that are testable,

auditable, and explainable.

542

Firstly, understand the

problem and tasks at hand.

543

Consider what are you trying

to achieve with the ai?

544

What type of data do you have?

545

What are the inputs and

outputs requirements?

546

How accurate do you need the model to

be, et cetera, et cetera, et cetera.

547

Then explore available model types,

like classical machine learning,

548

deep learning, natural language

processing, and time series models.

549

Once you've chosen the model,

consider what data sets are needed.

550

Consider how you prepare the data

and how you deal with missing data.

551

Then go back to the infrastructure pillar

and decide whether you have that computer

552

power to run these complex models.

553

Remember, you can buy or

rent computer power, as I've

554

already spoken about before,

555

as an executive or business owner.

556

You need a working knowledge

of frameworks, models,

557

and methods used for ai.

558

More importantly, you must understand

the limitations and risks because

559

you have an oversight responsibility

on what your team produces, whether

560

they're in-house or outsourced.

561

As a leader, you have to educate

yourself and make sure you stay

562

up to date on AI mechanics.

563

Nothing too deep here.

564

A high level understanding

that's all you really need.

565

You must ensure there are governance

mechanisms in place to evaluate

566

the accuracy of the models.

567

What's even more important is to assess

the risks of bias, transparency of the

568

models and maintainability, meaning

the output remains relevant over time.

569

The model doesn't become corrupt as

it gets more data and learns on it.

570

While your framework, models and

method must be smart, they must

571

also be ethical and responsible.

572

What if you choose a framework, method, or

model poorly, you will invite unintended

573

consequences like biases, discrimination,

and losses that your stakeholders suffer.

574

You can't have a brain that is corrupt or

manipulative, so I stress again, strong

575

governance around AI is so, so crucial.

576

You've got a bold challenge, mechanisms

and auditability into the AI solution.

577

The governance mechanism must anticipate

and quickly identify problems and weak

578

points and rapidly put a stop to AI system

deployment if the risk assessment fail.

579

Okay, so let's explore

who's involved here.

580

Data scientists and AI experts

naturally, but you also need to involve

581

wider teams like compliance officers,

legal teams, ethical working groups.

582

It's a cross disciplinary effort,

not a solo data science show.

583

Remember, the most important

controlling mechanism is.

584

That's going to stop disruptive AI

systems from being deployed and that

585

keeps check on outputs regularly,

making sure outputs are not corrupt and

586

don't lead to unintended consequences.

587

Now we come to the T in the

AI Swifter framework, the T

588

tracking and learning pillar.

589

AI isn't a launch event done and dusted.

590

It's a continuous improvement effort.

591

Success doesn't happen at a go live.

592

It happens over time through

learning and adaptation.

593

AI is developing rapidly and you as the

executive or owner and your team must

594

always be on a steep learning curve.

595

You need to know about new

developments, but you also need

596

to understand new regulations.

597

Putting guardrails on

AI system developments,

598

this is critical because if you

don't measure, you don't learn.

599

And if you don't learn, you

can't scale or course correct.

600

If you don't keep up with developments,

you will be outcompeted by those

601

that follow the latest trends.

602

If you don't keep up with guardrails, you

may end up on the wrong side of the law.

603

So

604

how do you build tracking and

learning into the process?

605

I'll define KPIs, key

performance indicators.

606

Track usage data.

607

Talk to stakeholders, watch

for outliers and edge cases.

608

Keep an eye on latest developments,

some tools that can help

609

us tracking and learning.

610

Include dashboards, internal audits,

surveys, and bullet database to

611

keep track of latest developments.

612

Remember that discovery is a foundational

pillar that is relevant at every

613

pillar of the AI swifter framework.

614

Use what you learn to

continuously improve.

615

What happens if you don't get this

tracking and learning component,

616

or simply you are rescaling

something that isn't working?

617

Or worse?

618

Thinking something works

when it doesn't really work.

619

Your AI system becomes irrelevant

over time and you lose your

620

competitive positioning.

621

You could also end up on

the wrong side of the law.

622

Definitely something you

don't want to get into.

623

The who leads this pillar?

624

Naturally, your analytics team

play a crucial role, but tracking

625

and learning must be embedded at

every level in your organization.

626

This is essential feedback loop making

you confident that your AI system is

627

always on the right path and following

cutting edge development curve.

628

Now we come to the penultimate

pillar e Ethics and control.

629

AI has power.

630

And the saying goes,

power needs guardrails.

631

I've already touched on this when

explaining other pillars, but let's zoom

632

in on the vital pillar of the AI swifter

framework, which is ethics and controls.

633

Why do ethics matter so much in ai?

634

'cause one ethical failure and wipe

out years of brand equity and get you

635

into hot waters with regulators or

face lengthy and expensive lawsuit.

636

Trust takes times to build

and seconds to destroy.

637

So how do you embed ethics?

638

Start with a clear ethical

framework and principles.

639

Define your company's core

ethical values and translate

640

them into ai ethical principles.

641

These might include fairness,

transparency, always doing the

642

right thing, accountability.

643

Privacy and security.

644

Develop AI ethics policies based on your

values, regulation, and best practice.

645

Establish an AI ethics committee or

an officer if you're a small business.

646

Hiring in an ex AI expert that

comes in every now and again to

647

make sure everything is on track.

648

From an ethics perspective, I've

already mentioned the importance of

649

a strong governance framework, which

must be developed and documented.

650

You need a structured approach with

clear roles and responsibilities

651

and decision making process for AI

development, deployment, and monitoring.

652

Embed ethical considerations

at every stage of the process.

653

Conduct regular audits and

reviews for ethical risks, biases,

654

unintended consequences, and

compliance with laws and policies.

655

So what happens if you ignore this?

656

You open the door to unintended

consequences that results in lawsuit,

657

media backlash and regulatory

heat, which I've already covered,

658

and who's responsible

for ethics and controls.

659

It's gotta be embedded in every

pillar of the AI source framework.

660

Assigning responsibility for.

661

Outcome to specific stages

and roles throughout the AI

662

implementation lifecycle.

663

Risk teams and legal and compliance

will play an important role, but

664

here is where leaders must step up.

665

They set the tone at the top, they

lead by example, and they have to have

666

a strong governance framework that

anticipates unintended consequences.

667

Surfaces them rapidly deals with

them, so that risks are minimized.

668

And finally, we come to the R in the AI

Swifter Framework, results and Scale.

669

This is where the rubber meets the road.

670

Proof of concept is nice, but

proof of value, that's where

671

real transformation begins.

672

If you go through all the

effort of building an AI system,

673

it's got to deliver real.

674

Tangible results, not

just today, but ongoing.

675

AI solutions must be sustainable.

676

You also want these solutions to

scale so that they give you more

677

return on investment over time,

results and scale is the end game

678

because AI has to scale to matter.

679

It can't stay stuck in

one pilot after another.

680

Remember, pilot purgatory discussed in.

681

So how do you scale with confidence?

682

Modularize your solutions.

683

Make sure the modules are standardized

and accessible to other teams.

684

Remember, build AI like Lego blocks.

685

Once time and effort goes into innovating

and building one component, make sure

686

it's available and can be used by other

teams who are building other AI solutions.

687

Now you can develop and deploy AI

systems fast because you don't need to

688

go back to the drawing board every time.

689

You can just take what's

working and build on it.

690

This gives you a huge head start.

691

The modules must be hosted on a

digital platform that developers

692

can get controlled access to.

693

However, document everything,

how it works, how it's deployed,

694

what worked, what didn't work.

695

Toolkits that others in

the organization can reuse.

696

Here are some of the practical

assets you should be developing.

697

Internal written playbooks, videos

that guides videos that gives guidance

698

on the assets and how to use it.

699

A digital platform with reusable

modules of code governance

700

frameworks for scale and oversight.

701

What have you skipped this?

702

Well, quite simply, we'll be stuck

in pilot purgatory celebrating small

703

wins, but never changing the game.

704

Who drives scale?

705

Again, this is everyone's responsibility.

706

Everyone involved has to develop

things that can be reused.

707

Then document what they have done to make

life easier for someone who will use the

708

AI related asset, develop instructional

videos and modules, delivery team, program

709

managers, and executive sponsors must

drive scalability in everything they do.

710

So that's the AI Swift framework, a

grounded, structured way to navigate

711

AI in your business, not just with

hype, but with real practical clarity.

712

We are building an AI readiness

scorecard to help you assess yourself

713

across the nine pillars so you know

where to start, what to strengthen,

714

and where the real risks are.

715

I include a link in the show

notes to express your interest in

716

completing this when it is ready.

717

I'm your host, Jay Tick.

718

This is impact with digital.