While large global companies are garnering attention and making headlines with their large-scale AI investments, the most immediate returns will come from other parts of the market.
Small and medium-sized businesses (SMBs) are implementing AI with speed, focus, and purpose, often seeing measurable results within weeks rather than months.
European startups developing AI agents that can be deployed throughout workflows have already raised €1 billion this year, according to data from Sifted.
Samantha Wessels, president of EMEA at intelligent content management platform Box, says small businesses are seeing faster and more valuable returns by deploying pre-built AI.
“These companies are accelerating time to value by skipping the infrastructure stage and deploying turnkey AI to instantly solve specific resource constraints,” she says.
When using AI in their work, Wessels says small and medium-sized businesses often move from pilot to production faster than larger companies, which suffer from so-called “data gravity.”
“Large enterprises are bottlenecked by decades of fragmented content across disconnected systems and are stuck in AI prototype purgatory with endless pilots that never make it to production,” she says.
While businesses undertake broader transformation programs, SMBs can scale with tight scope by starting with one workflow, proving value, and expanding from there. There.
Small businesses can classify data more quickly by deploying existing digital native stacks like Slack or Salesforce within their workflows, but large businesses often spend significant time building their own platforms.
While complex organizational structures have traditionally slowed AI adoption within large enterprises, Wessels says smaller companies with flatter structures often have the agility to work around these hurdles.
“While enterprises undertake broader transformation programs, small businesses can narrow their scope and scale by starting with one workflow, proving value, and expanding from there,” she says.
The companies that can move forward with AI are those that are redesigning their workflows, and this is often easier for small and medium-sized businesses that can stay agile.
For large companies, this may mean moving away from staged pilot projects and designing a complete overhaul of business processes.
To transform business processes, companies should consider starting by giving AI repetitive tasks that are often time-consuming. SMBs have quickly found success by automating these types of tasks across financial processing, HR onboarding, and sales.
Wessels said live entertainment producer RWS Global is one company showing how small and medium-sized businesses can effectively integrate AI. We work with thousands of performers on a contract basis, automating contract approval workflows and saving time on manual processing.
The company didn’t build custom infrastructure and instead leveraged Box’s existing tools to automate approvals and processing times. Box’s ability to centralize digital asset libraries allows RWS Global’s team to search, collaborate, and distribute content without having to switch between disconnected systems.
To transform workflows, it’s important to get employees used to using AI. Companies, large and small, should strive to align the views of senior management with the reality at lower levels within the company.
Box takes a four-step approach and encourages companies to use the same framework. The four stages are ideation (identifying AI opportunities by exploring pain points and inefficiencies), piloting (building and testing a set of agents over 3-6 months), rollout (transforming a successful pilot into a solution), and scale adoption (maximizing adoption through change management and workflow redesign).
guardrail
But moving fast doesn’t mean moving recklessly. SMBs must balance speed with responsible adoption by putting guardrails in place from day one.
AI tools should also provide citations that link to the source files for easy human verification. output.
According to Wessels, core Day-One guardrails include zero-training policies (AI providers that don’t use customer prompts or data to update base models), permission-based AI (AI systems that enforce strict data access controls and restrictions), and citations and transparency.
“AI should strictly adhere to existing user access controls and prevent internal leaks by only analyzing files that users can already view,” she says. “AI tools should also provide citations that link to the source files so that humans can easily verify the output.”
Wessels also adds that there always needs to be a “human stakeholder.” Technology must source information and summarize it, but humans must constantly validate and make the final decisions.
Competitive advantage is shifting from “who has the most computing power” to “who can adapt their workflows” Fastest”.
Wessels believes that over the next five to 10 years, the AI gap between small and medium-sized businesses and enterprises will narrow significantly. Workplaces are adapting to using agent AI from simply being assistants to using them as colleagues.
“Agent AI that not only answers questions but actively performs tasks is being built natively into everyday SaaS platforms,” she says.
“Competitive advantage is shifting from who has the most computing power to who can adapt to workflows the fastest. This means small and medium-sized businesses are freed from the burden of strict change management and can effortlessly inherit enterprise-class intelligence and operate at scale.”
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