5 Secrets to Unlocking Business Agility with AI

In the DoneMaker conversation, I walk you through how Business Agility becomes real when you apply AI the right way. I’m Alakh Verma, CEO of B-Centric, former director at Oracle and Microsoft, and strategic advisor at PwC on AI and emerging tech, and in this piece I’ll take you step-by-step through five practical secrets that help your organization move from curiosity and fear to predictable, profitable Business Agility. If you watched the video, this article expands those ideas into an actionable roadmap you can use right away.

You’ll learn why Business Agility is not a buzzword but a measurable outcome, how data architecture and cloud-first thinking unlock AI value, which processes you should automate first to see quick ROI, and why a phased, supervised-to-autonomous approach is the safest path forward. Throughout, I’ll be speaking directly to you: the decision-maker, leader, or practitioner responsible for turning AI potential into real Business Agility inside your company.

Outline

• 1. Start with governance and strategy: set the north star for Business Agility
• 2. Treat data as the lifeline: build cloud-first architecture and governance
• 3. Map processes, then layer AI: procure-to-pay, order-to-cash, hire-to-retire
• 4. Prioritize customer-facing quick wins and measure ROI
• 5. Choose the right partners, tooling, and a phased deployment plan
• FAQs
• Action checklist and next steps

1. Start with governance and strategy: set the north star for Business Agility

The first secret to unlocking Business Agility with AI is governance and strategy. When you’re trying to change how your organization makes decisions, you need a top-down clarity that connects country-level, economy-level, and business-level goals. You don’t get Business Agility by bolting tools onto chaos. You get it when leaders set a north star and align processes, data, and accountability to achieve it.

Governance matters because AI won’t magically fix poor decision-making frameworks. It will sharpen whatever you put into it. If decision makers rely on whims and gut feel, AI will only accelerate outcomes, good or bad. So you start by defining the decisions you want to enable with AI:

• Which strategic decisions must happen in real time?
• What KPIs define Business Agility for your organization?
• Who owns the data and the model outputs?

Make AI a tool to augment decision makers, not replace accountability. Augmenting human intelligence is what Business Agility is all about: faster, more accurate decisions delivered at the point of choice. When that happens, failure and breakdown fall, checks and balances return, and productivity goes up — measurable and sustained.

Why governance is your first investment

You’ll protect ROI, reduce risk, and accelerate adoption when governance is handled up front. Governance isn’t bureaucracy, it’s the scaffolding that makes Business Agility repeatable. Your board and C-suite should understand the phased plan, the risks, and the measures of success before you deploy agents that automate customer onboarding, invoice payments, or inventory replenishment.

2. Treat data as the lifeline: build cloud-first architecture and governance

Secret number two: data is the lifeline. If you want Business Agility, you must have structured, accessible, and governed data. Too many organizations try to apply advanced AI to poorly organized data and then conclude “AI doesn’t work for us.” That’s not AI’s fault, it’s a data fault.

Start with the basics:

• Identify your transactional systems of record (ERP, CRM, finance systems).
• Ingest systems of engagement (web logs, device telemetry, customer interactions).
• Bring everything onto a cloud-first architecture designed for scale and GPUs.

When you centralize your data and apply strong data governance, you stop leaking insights. Business Agility requires near real-time information at decision points. If intelligence arrives an hour too late, you’ve lost the action. The cloud-first approach gives you the processing power to run multimodal models, knowledge graphs, and enterprise language models, all essential for AI that actually improves operations.

Key components of a data-first approach

Move beyond a traditional data warehouse model and think in terms of:

• Data lakes and curated knowledge graphs for domain context
• Enterprise language models (ELMs) or departmental LLMs that are trained on your proprietary data, not the public internet alone
• Integrated security and governance: access controls, compliance SLAs, and audit trails

When data is organized, you can trust the model outputs. When models are trustworthy, you can embed automation into daily workflows. That chain — data to trust to automation — is the core engine of Business Agility.

3. Map processes, then layer AI: procure-to-pay, order-to-cash, hire-to-retire

The third secret I tell every leader is this: map your processes first, then apply AI. Don’t start with technology. Start with process transformation. When you understand the handoffs, decision points, and information flows in processes like procure-to-pay, order-to-cash, and hire-to-retire, you can identify the highest-impact places to deploy soft agents and automation.

Here’s how I recommend you approach process-centric transformation:

1. Document your end-to-end process flows and the data required at each step.
2. Identify repetitive, high-volume tasks that are ripe for automation (e.g., invoice validation, payment follow-ups, customer retention outreach).
3. Design soft agents to perform sequences of tasks with human-in-the-loop supervision.
4. Measure improvements in cycle time, error rates, and revenue impact.

Examples that deliver measurable Business Agility

Let me give you concrete cases where AI-driven agents increase agility:

• Accounts payable agent: validates invoices, matches PO lines, triggers payments, and follows up on exceptions. Result: faster payments, fewer errors, predictable cash flow.
• Order-to-cash agent: automatically issues invoices, chases late payments with personalized messages, and routes disputes to the right human agent. Result: improved DSO and happier customers.
• Procurement agent: analyzes demand forecasts and recommends vendor sourcing based on cost, lead time, and quality. Result: optimized inventory and reduced stockouts.
• Customer lifecycle agent: segment-specific outreach to champions, at-risk customers, and churned accounts with personalized content. Result: higher retention, greater lifetime value.

These examples are not theoretical. You can implement them now if you have structured data and a process map. Process-first AI deployments are how you move from pilot projects to company-wide Business Agility.

4. Prioritize customer-facing quick wins and measure ROI

Secret number four is pragmatic: start with customer-facing, measurable use cases. If you want executive buy-in and momentum, pick wins that show ROI quickly. Marketing, sales, and service are usually the fastest places you’ll see impact.

Why customer-facing first?

• You can measure revenue impact directly (campaign conversion rates, retention, upsell).
• Customer-facing improvements reduce friction — better onboarding, personalized offers, faster issue resolution.
• These wins build trust across the organization and reduce FOMO-driven churn into low-value experiments.

How to run a customer-facing pilot that proves Business Agility

1. Define a single use case (e.g., reduce onboarding time for new customers by 50%).
2. Map the data and integrate the necessary systems (CRM, website, KYC engines).
3. Deploy a supervised agent for a fixed period, measure outcomes, iterate.
4. Move to semi-autonomous and then autonomous modes as accuracy improves.

Remember: supervised → semi-autonomous → autonomous. This is the passage from training and trust to autonomous Business Agility. Think of it like self-driving cars: you don’t go from zero to driverless overnight. You test in controlled environments, you measure, you adjust, and then you expand. The same progression keeps your organization safe while unlocking speed and scale.

5. Choose the right partners, tooling, and a phased deployment plan

The fifth and final secret: you don’t have to invent everything. Choose the right partners and remain pragmatic about tooling. But you must be selective. The market is flooded with startups and “shiny” point solutions. Look for providers and consultants that demonstrate three things:

• Domain experience and a track record of delivering measurable Business Agility
• A roadmap for data governance, model training, and phased deployment (quarter-by-quarter milestones)
• Clear cost-benefit analysis including cloud compute, GPU needs, and long-term maintenance

Cloud-first is non-negotiable. Large models and multimodal workflows require GPU-based processing and integrated governance. Bring your on-premise and cloud workloads together, identify which models are appropriate for your domain (departmental LLMs vs. public LLMs), and model costs before you deploy.

Pragmatic partner checklist

• Can they map your processes from the ground up?
• Do they deliver in three-month sprints with measurable KPIs?
• Can they design enterprise language models trained on your private data with clear governance?
• Do they provide a migration path from supervised to autonomous operations?

If you do this with a credible advisor, you remove a lot of the fear and FOMO. You don’t need to buy every gadget. You need a methodical approach that drives Business Agility and revenue growth.

Embedding AI into your organization: practical steps to begin today

Here’s a compact playbook you can follow starting this week to begin unlocking Business Agility with AI:

1. Run an executive workshop to define what Business Agility means for your organization — and agree KPIs.
2. Inventory your data: transactional systems, engagement systems, and unstructured sources.
3. Choose one customer-facing pilot that will deliver measurable ROI in 90 days.
4. Select a cloud-first platform to centralize data and enable GPU workloads.
5. Engage a trusted advisor to map processes and design supervised agent workflows.
6. Measure, iterate, and expand: supervised → semi-autonomous → autonomous.

Follow these steps and you’ll avoid the common trap of splashy pilots that fail to scale. The market is littered with startups offering point solutions; only those who deliver sustainable productivity gains and tie solutions back to business KPIs will survive and help you scale.

How Business Agility shows up in numbers

When done right, Business Agility is measurable. You’ll see improvements in:

• Time-to-decision (minutes instead of hours or days)
• Cycle time reductions in procure-to-pay and order-to-cash
• Lower failure and breakdown rates in manufacturing and operations
• Improved cash flow and reduced Days Sales Outstanding (DSO)
• Higher customer retention and lifetime value

Those outcomes compound. Improved cash flow funds more innovation. Better customer retention increases lifetime value. That’s why national leaders talk about AI and Business Agility: economies grow when businesses run smarter and faster.

Common pitfalls and how to avoid them

Here are mistakes I see repeatedly — and simple fixes:

• Pitfall: Starting with the wrong shiny tool. Fix: Start with use cases and processes, then pick the tool that fits.
• Pitfall: Using public LLMs as a crutch for domain-specific problems. Fix: Build enterprise or departmental language models trained on your data.
• Pitfall: Ignoring cloud costs and GPU consumption. Fix: Model costs up front and optimize model complexity for the use case.
• Pitfall: Deploying fully autonomous agents too quickly. Fix: Supervision first — measure outcomes — then scale autonomy.

What to expect in 2026 and beyond

Looking forward to 2026, the trend is clear: only solutions that deliver measurable productivity will survive. Think back to the dot-com era when countless apps appeared — most dissolved into platform features that scaled. The same consolidation will happen with AI tools. The winners will be those who integrate AI into end-to-end processes and demonstrate persistent Business Agility improvements.

Key themes you’ll see in 2026:

• Consolidation of point tools into comprehensive platforms and enterprise-grade copilots
• Wider adoption of departmental LLMs and knowledge graphs for domain accuracy
• More emphasis on data governance, security, and vendor SLAs
• Investors favoring startups with strong business metrics — not gimmicks

If you want to win in 2026, focus on sustainable productivity gains today. Build Business Agility deliberately and measure everything.

FAQ

What is Business Agility and how does AI enable it?

Business Agility is the ability to make fast, accurate decisions and execute quickly across your organization. AI enables Business Agility by providing real-time insights, automating routine tasks, and augmenting human decision-making with data-driven recommendations. When processes, data, and governance are aligned, AI reduces failure rates, shortens cycle times, and increases productivity.

Where should I begin if my company has no data strategy?

Begin with a simple inventory of systems and a workshop to define key business decisions. Move to a cloud-first architecture to centralize high-value data, apply governance, and choose a measurable customer-facing pilot. Engage an advisor to map processes and design a supervised agent for the pilot.

Are public LLMs like ChatGPT enough for business use-cases?

Public LLMs are great for general tasks but often lack domain-specific accuracy. For Business Agility you typically need enterprise or departmental language models trained on your curated data and knowledge graphs to ensure accuracy and remove bias.

How fast can I expect ROI from an AI pilot?

Choose a customer-facing use case for the fastest ROI — many pilots show measurable results within 90 days when properly scoped and supervised. The key is to focus on use cases with direct revenue or cost implications like onboarding, retention, and accounts receivable.

How do I address employees’ fear of being replaced?

Make augmentation the narrative. Show how AI reduces repetitive work and enables employees to focus on higher-value activities. Use supervised deployments first so humans remain in the loop and see improvements firsthand. Training, transparency, and measurable productivity gains will drive acceptance.

Should I build AI capabilities in-house or buy them?

There’s no one-size-fits-all answer. For strategic core capabilities, build or co-develop enterprise models using your data. For non-core or commodity features, partner with credible vendors. Always choose partners that align to your roadmap for Business Agility and can support phased deployments and governance.

Action checklist: Your first 90 days to Business Agility

1. Executive alignment: define what Business Agility means and agree KPIs.
2. Data inventory: list transactional systems, engagement sources, and unstructured data.
3. Pick a fast-win pilot: one customer-facing use case with clear ROI targets.
4. Cloud-first: choose a platform that supports GPUs and knowledge graphs.
5. Engage a trusted advisor: map processes and design a supervised agent.
6. Run a 90-day sprint: measure, iterate, and prepare to scale successful pilots.

If you follow these steps, you’ll be on a clear path to Business Agility: not the vague promise of future benefit, but measurable outcomes that change how your organization operates and competes.

Final thoughts

Business Agility isn’t a magic button. It’s a journey across governance, data, process mapping, and pragmatic deployment. Start with data and processes, pick customer-facing pilots, use a supervised-to-autonomous progression, and work with credible partners who measure outcomes. Do that and AI will stop being a source of fear and become your most reliable engine for faster decisions, lower failure, and higher profitability.

If you want to discuss your company’s roadmap to Business Agility, find me on LinkedIn or reach out via the channels shared in the DoneMaker video description. Start with one pilot, measure the impact, and expand. The future belongs to those who build Business Agility now.

Watch the full podcast here: 5 Secrets to Unlocking Business Agility with AI