Five Best Practices from AI Native Leaders

Mike J. Walker

Sep 17

7 min read

At Columbus AI Week, I had the opportunity to be a speaker on a question I hear almost everywhere from executives: How do we move beyond AI experiments and create an AI-native business that delivers real value?

That question matters because organizations have not been sitting on the sidelines. They have invested heavily in AI, launched pilots, hired vendors, and put copilots into the hands of employees. Yet many executives are still asking the same uncomfortable question: Where is the

business impact?

The problem is not a lack of AI activity. In many cases, it is the opposite. Organizations have accumulated experiments without building the business discipline, architecture, operating model, and governance needed to turn those experiments into repeatable outcomes.

My message was straightforward:

AI-native does not mean adding AI to every process. It means redesigning how the business creates value, makes decisions, and executes using AI as a core capability.

Here are the five musts I shared with the audience.

#1 Act Decisively on the Universal AI Blockers

When AI initiatives fall short, leaders often assume the model was not good enough or the vendor chose the wrong technology. Sometimes that is true. More often, the root causes are much less glamorous: integration complexity, poor data, weak program leadership, unclear business cases, and gaps in workforce capability.

These are not isolated technical problems. They are enterprise dependencies.

If every AI project has to solve identity, data access, integration, security, model selection, monitoring, and legal review from scratch, the organization will never scale. Each pilot becomes its own custom science project. Costs rise, timelines stretch, and the business loses confidence. The answer is not to stop experimenting. It is to solve the recurring blockers once and make the solution reusable.

That means creating shared patterns for:

Accessing trusted business data
Connecting AI to existing workflows and systems
Managing identity, permissions, and agent authority
Evaluating models and vendors
Monitoring performance, cost, and risk
Training employees to use AI effectively
Moving successful pilots into production

This is where decisive leadership matters. Some foundational decisions will be imperfect because the market is moving quickly. Waiting for absolute certainty, is often the more expensive one.

The goal is not to predict every future requirement. It is to establish a durable foundation that lets the organization learn and adapt without rebuilding everything each time.

#2 Only Business Impact Matters

AI creates excitement very easily. Generating material business value is harder.

Too many programs begin with a tool, a model, or a broad mandate to “use AI.” That reverses the order. Start with a strategically relevant business capability, a measurable pain point, and an accountable owner. Then determine whether AI is the best way to improve it.

During the session, I used a simple example: an AI meeting notetaker connected to a CRM. The system transcribes sales calls, extracts decisions and action items, and updates the customer record automatically.

It is not the most futuristic use case in the world, and that is exactly the point.

Imagine eight sales professionals each spending 90 minutes a day capturing notes, extracting follow-ups, and updating the CRM. At a loaded labor cost of $45 per hour, that inefficiency represents roughly $140,400 a year. If implementation and licensing cost $9,000 in the first year, the potential net savings are $131,400, with a simple payback of less than a month.

Those figures are illustrative, of course. Every organization should use its own data and validate whether saved time is actually converted into more selling, better customer coverage, higher-quality information, or another measurable outcome.

But the discipline is universal. Before approving an AI initiative, leaders should be able to answer:

What business outcome are we changing?
Who owns that outcome?
What is the current baseline?
How will AI change the economics or experience?
How will we measure adoption and realized value?
What must change in the surrounding process for the benefit to stick?

If the team cannot answer those questions, it does not yet have an AI business case. It has an interesting idea.

#3 AI Architecture Defines Winners and Losers

Architecture may not generate the same excitement as a new model demonstration, but it determines whether an organization can scale AI affordably, securely, and consistently.

Without a shared architecture, teams buy overlapping tools, duplicate data pipelines, create inconsistent controls, and lock valuable business logic inside isolated solutions. One pilot may work, but the organization cannot efficiently turn that success into ten more.

A practical AI-native architecture includes several connected layers:

Extensible AI infrastructure: cloud, edge, and local computing selected according to workload, cost, latency, privacy, and resilience requirements.
AI-enabled data services: trusted data, semantic models, ontologies, and knowledge graphs that give AI the business context it needs.
Agents and services: a governed library of frontier models, open-weight models, specialized agents, and reusable services.
An orchestration platform: workflow, model routing, tool use, observability, and the deliberate combination of deterministic and probabilistic techniques.
Composable business solutions: low-code applications, agentic workflows, and extensions embedded directly into how people work.

The important word here is shared. A common platform does not mean forcing every use case onto one model or vendor. It means establishing reusable capabilities, standards, and guardrails so teams can innovate quickly without creating a new technology island each time.

It also gives the organization leverage. Models will change. Vendors will come and go. Pricing will move. Regulation will evolve. A composable architecture makes it possible to adapt without rewriting the business around every new technology wave.

In other words, build for change not for the model that happens to be popular this quarter.

#4 Strategy Without Execution Is a Hallucination

This line got a few smiles during the workshop, but I mean it seriously.

An AI strategy that never changes funding, priorities, capabilities, processes, or accountability is not a strategy. It is a presentation.

Moving from ambition to execution requires a transformation framework that connects market disruption to concrete business initiatives. The approach I shared has four stages:

Explore, challenge, and ideate. Start by understanding the forces reshaping your industry. Challenge current assumptions, explore new customer and employee experiences, and generate opportunities without allowing today’s constraints to shut down the conversation too early.
Rationalize the strategy. Not every promising idea belongs in the portfolio. Test opportunities against strategic objectives, customer needs, value streams, business capabilities, feasibility, and timing. This is where an imaginative concept becomes a strategically relevant option.
Value the capabilities and define value realization. Quantify benefits, costs, dependencies, and risk. Identify which business and technology capabilities must change. Define leading indicators, not just lagging financial results so leaders can tell whether adoption and operational change are moving in the right direction.
Build transformation roadmaps. Convert the strongest opportunities into actionable initiatives. Create options rather than a single brittle plan. Sequence dependencies, identify decision points, assign accountable owners, and make trade-offs visible.

The output should not be a giant list of AI use cases. It should be a focused portfolio linked to business outcomes, with a roadmap the organization can actually execute.

This is also why AI transformation cannot belong only to IT. Technology leaders are essential, but business leaders must own the workstreams where value will be created. AI changes decisions, roles, operating processes, customer experiences, and sometimes the business model itself.

Those are business changes enabled by technology, not technology projects looking for business sponsorship.

#5 Don’t Get Sued. Govern Your AI.

The title is intentionally direct because governance is too often treated as optional until something goes wrong.

In a high-risk AI environment, governance is not a policy document sitting in a shared folder. It is the combination of leadership, decision rights, operational controls, technical controls, workforce practices, and evidence required to use AI responsibly.

Good governance answers practical questions:

Which use cases are allowed, restricted, or prohibited?
What data can a model or agent access?
When is human approval required?
Who is accountable for an AI-assisted decision?
How are models, prompts, tools, and outputs monitored?
What happens when performance degrades or an agent acts outside its intended scope?
Can the organization reconstruct what happened after the fact?

Governance should be proportional to risk. An assistant summarizing an internal meeting should not face the same controls as an agent approving a financial transaction, influencing patient care, or changing a manufacturing process. But both need clear boundaries.

Below is an abstracted framework for blending innovation, governance, and execution.

The most effective approach is to embed governance into delivery rather than bolt it on at the end. That includes program steering, communications and knowledge management, value realization, organizational change, and business change management. It also means designing technical guardrails into the platform itself.

Done well, governance does not slow innovation. It creates the trust required to scale it.

What AI-Native Really Looks Like

An AI-native enterprise is not defined by the number of copilots it has licensed or pilots it has launched. It is defined by whether AI is connected to the organization's strategy, architecture, data, operating model, and governance.

The organizations that pull ahead will do five things well:

Translate strategy into funded, accountable execution with an AI value realization approach.
Build a culture of innovation and evergreen change management.
Build optionality and flexibility into your AI architecture.
Govern AI with a risk adjusted value approach that governs AI efforts in proportion to the actual impacts to the business.
Identify and remove universal blockers quickly and enable with scalable AI infrastructure.

This shift will not happen through a single workshop, platform purchase, or executive announcement. It happens through a disciplined cycle of learning, building, measuring, and adapting.

My practical advice is to begin with one business capability that matters. Establish the baseline. Select a use case with visible value. Put the necessary controls in place. Deliver it. Measure what changed. Then reuse what you learned to scale the next opportunity.

That is how experimentation becomes capability. Capability becomes transformation and transformation becomes an AI-native business.

Let's Continue the Conversation

Thank you to the Columbus AI Week community and everyone who joined the workshop, challenged the ideas, and shared experiences from inside their own organizations.

If your team is trying to move from AI experimentation to scaled business value, I offer executive briefings, business-led agentic AI workshops, and deeper AI transformation engagements focused on opportunity portfolios, roadmaps, capability development, governance, and measurable outcomes.