5 ways to improve efficiency in the age of AI
For the past two decades, the path to digital transformation and greater efficiency often followed a relatively linear script: digitize paperwork, implement core systems, integrate data, and only then experiment with advanced technologies.
That logic is no longer sufficient on its own. The rise of generative AI, flexible cloud infrastructure and low code development has created additional routes toward greater digital maturity. While core business software remains key in almost every company, there are now multiple paths to enhancing productivity—each with their own benefits, risks and prerequisites.
“What matters most is not the tool itself but the business context: your operating model, your objectives, and the problems or data that are holding your business back,” says Tyler Lockyer, Director, Client Delivery, BDC Advisory Services.
“Businesses still need foundational systems like accounting software, CRM and ERPs. Some organizations are ready to experiment with more advanced approaches. And an increasing number are blending several strategies at once,” says Jill Anderson, Senior Business Advisor, BDC Advisory Services.
What follows is not a checklist of technologies to install, but a way of thinking about five major paths that entrepreneurs are taking today to increase their digital maturity. The right choice depends on where you are starting from—and where you want to go.
AI adoption is not separate from your technology strategy—it is part of it. Businesses still run on core systems. We are not yet in a world where a single search bar or autonomous agent magically replaces accounting software, order management or compliance reporting.
Tyler Lockyer
Director, Client Delivery, BDC Advisory Services
Path A: Buying core business software
For many Canadian SMEs, the most effective and least risky starting point remains the adoption of proven business software: accounting platforms, CRMs, ERPs, inventory management systems or scheduling tools. These systems are not new, and that is precisely their strength.
“AI adoption is not separate from your technology strategy—it is part of it. Businesses still run on core systems. We are not yet in a world where a single search bar or autonomous agent magically replaces accounting software, order management or compliance reporting,” says Lockyer.
These tools provide structure, consistency, auditability and reliability of data, particularly in regulated areas such as finance, tax, payroll and reporting.
From a digital maturity perspective, core business software serves three critical functions.
1. It digitizes operations
Many businesses still handle orders, invoices, or approvals through email, spreadsheets, or even paper and fax. Until information is captured digitally, it is difficult to analyze, automate, or optimize anything downstream.
2. It creates structured data
Modern business systems enforce fields, taxonomies, and workflows. Even if data is siloed, it is at least organized. This is a major reason why surveys often show a high percentage of businesses claiming they “have data”—because data lives inside these operational systems.
3. Modern platforms now embed AI directly into their products
Over time, these tools become more powerful as their embedded AI models learn from the data flowing through them. They provide new insights for the organization. A CRM that understands customer behavior or an accounting platform that flags anomalies can deliver real value without requiring businesses to build anything themselves.
For organizations with low digital maturity, this path is often the safest and most impactful. It reduces manual work, improves reliability, and lays the groundwork for more advanced AI initiatives later. However, it is not a cure all. Buying software without adoption, integration or process change rarely delivers meaningful results.
Path 2: Adopting generative AI for personal productivity
A second path is the use of generative AI for individual and team productivity.
This includes tools that help employees and entrepreneurs draft emails, summarize documents, generate ideas, translate text, or analyze information more quickly.
Both Lockyer and Anderson note that this is frequently an entry point for AI adoption, particularly when organizations apply appropriate guardrails around privacy, security, and data access. Unlike large systems, these tools can deliver immediate, visible benefits with relatively low costs, while helping to build up AI literacy in the business.
Used well, generative AI can speed up routine tasks and free time for higher value work. In theory, even small savings can add up across teams.
However, this path has limits.
Productivity tools do not fundamentally change how a business operates. Writing emails faster does not fix broken machines. Summarizing reports does not replace the need for clear decision-making processes. In some cases, organizations risk automating inefficiency rather than eliminating it.
There is also an adoption paradox. If employees use AI to generate more content—more emails, more documents, more messages—the net productivity gain may be lower than expected.
This path works best when it is intentional and paired with broader process awareness. Generative AI should support clear objectives: faster turnaround, reduced errors, better insights—not just activity for its own sake.
Strategic planning and ideation can happen even in low tech environments. The goal is not to chase AI for its own sake, but to use digital tools—old and new—to achieve meaningful impact.
Jill Anderson
Senior Business Advisor, BDC Advisory Services
Path 3: Creating a “knowledge centre”
A third, and often overlooked, path to digital maturity is the creation of a structured knowledge centre. Unlike accounting or CRM systems, knowledge centres focus on unstructured information: policies, procedures, standard operating procedures (SOPs), manuals and internal best practices.
Anderson compares it to creating a kind of internal “Wikipedia” for the organization. “It’s a repository that captures how work actually gets done. What happens when a customer raises a quality issue? How are orders escalated when something goes wrong? What steps should frontline staff follow in common but complex situations?” she says.
This work is not glamorous, and it is rarely driven by technology teams. Yet it is foundational for several reasons.
Knowledge centres address skills gaps and knowledge loss. Many businesses face succession challenges as experienced employees retire or move on. If expertise lives only in people’s heads, it walks out the door with them. Documenting that knowledge improves resilience and continuity.
Second, a knowledge centre can help improve consistency and productivity. Clear SOPs reduce rework, confusion and dependence on informal communication. New employees ramp up faster, and experienced employees spend less time answering the same questions repeatedly.
Third—and increasingly important—knowledge centres make AI more usable. Generative AI systems and their information retrieval models (retrieval-augmented generation or RAG) are far better at working with unstructured text than traditional software, but they still need a reliable source of information. A chatbot or assistant is only as good as the documentation it can access. Without a curated knowledge base, AI risks generating confident but incorrect answers.
In this sense, building a knowledge centre is not an alternative to technology investment; it is a form of data maturity. It prepares the organization for automation, customer support tools, internal agents and future AI-driven applications.
Path 4: Building automation and workflows across core systems
A fourth path to improving digital maturity is to build automations for repeatable tasks and workflows within their core software—or across multiple systems—to reduce manual work, structure operations and improve consistency. Rather than replacing existing tools, automation helps them work better together.
Automation can take many forms. In some cases, it is embedded directly in software platforms such as CRMs, ERPs or accounting systems. In others, it connects multiple systems using workflow and automation tools. For example, when a new client signs a contract, an automated workflow can create the required folders in a cloud repository, assign access rights, generate onboarding documents and notify the appropriate teams—without anyone having to intervene manually.
These types of automations may seem small, but they address a common productivity challenge in growing businesses: high frequency, low value tasks that consume time, introduce errors, and depend on individuals remembering what to do next.
As digital maturity increases, businesses can also begin to use AI agents—software components that follow rules, trigger actions, or move information across systems—to support more complex workflows. Agents can operate within a single application or across multiple platforms, helping coordinate activities such as approvals, data updates, scheduling, or document handling.
This is where tools like workflow automation platforms become particularly valuable. They allow entrepreneurs to design processes that reflect how the business actually operates, without rebuilding entire systems from scratch. Importantly, these workflows help structure data and work, making operations more predictable and measurable.
Automation prepares the organization for more advanced AI use. Well defined workflows, structured data flows, and clear rules give AI powered agents a reliable environment to operate in. Without this foundation, intelligent agents struggle to deliver consistent results.
Automation does not require high digital maturity to get started, but it does require intentionality. Businesses must understand their processes, identify bottlenecks, and focus on outcomes—not just technology. When done well, automation becomes a powerful bridge between core systems and more advanced AI driven approaches.
Path 5: Building custom software using AI
The fifth path is the newest—and the riskiest. Some entrepreneurs are now using AI, cloud platforms, and low code tools to build custom software, agents or middleware tailored to specific business problems.
This is sometimes described as “vibe coding”: rapidly creating lightweight applications that sit alongside existing systems. These tools might automate a narrow but labour intensive task, pull data from multiple platforms or support decision making in a very specific context.
Done well, this approach can be transformative. Relatively small applications can replace large amounts of manual work by targeting a clearly defined problem. The value doesn’t come from the novelty of AI, but from a deep understanding of the process being optimized.
However, this path is not for everyone.
Custom solutions raise serious questions about governance, security, compliance and long term maintenance. Accounting, tax, and reporting functions cannot simply be “hacked together” without risk. There is also the danger of building tools that depend on individuals rather than institutional capability.
This path tends to suit digitally savvy organizations that already understand their processes, data, and constraints—and that are willing to accept experimentation in exchange for differentiation. For the right business, and the right use cases, custom AI solutions open new possibilities that off the shelf software cannot always address.
There is no single sequence
Digital transformation is no longer a simple straight-line progression.
Some businesses may start by buying software. Others may begin by organizing knowledge or using generative AI for quick wins. Still others may jump directly into custom solutions—and then circle back to foundational work when they hit constraints.
What matters is not where you start, but whether your choices are aligned with clear business objectives.
The critical questions remain consistent regardless of the path:
- What problem are you trying to solve?
- Where is productivity being lost today?
- What data, processes or knowledge are missing or fragmented?
- What level of risk is your business willing to tolerate?
Answering these questions requires strategy, not just technology. As Anderson noted, “strategic planning and ideation can happen even in low tech environments. The goal is not to chase AI for its own sake, but to use digital tools—old and new—to achieve meaningful impact.”
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