Feature
Artificial intelligence (AI) is no longer a futuristic concept but a present-day force with the potential to revolutionize business. However, translating this potential into tangible, sustainable success requires more than simply acquiring the latest AI tools. It demands a meticulously developed AI business strategy.
Developing an AI business plan with clear, practical goals for the one, three and five-year horizons is the cornerstone of transforming AI's promise into real-world business results and enduring growth.
Year One: Quick Wins to Prove AI’s Value
The inaugural year of any AI strategy should be dedicated to "proving it," said Dimple Thakkar, CEO at SYNHERGY. This foundational phase is about identifying and implementing AI projects that deliver rapid, measurable impact, primarily by helping business work better and addressing specific, often persistent, business problems.
One example is machine learning-driven tasks such as sales opportunity modeling, said Analytics Consultant Deimante Tankus, drawing on insights from Alexander Group. This technique uses existing firmographic and sales data to find sales potential within current customer bases. Such insights can move sales resources toward accounts that are more likely to grow.
"Seventy-five percent of surveyed GTM leaders deploying opportunity modeling for 6-12 months have already seen positive ROI on the use case,” Tankus said.
Generative AI customer intelligence tools not only provide sellers with actionable insights, but also help prioritize outreach through mechanisms such as lead scoring and recommend the next best actions, Tankus added
These tools make representatives more productive. "About half of surveyed revenue leaders who deployed AI-enabled customer intelligence tools for 6-12 months saw positive ROI within the year,” Tankus reported.
First-year projects should prioritize automation, insights, optimization and operational use cases, said Andrea Morgan-Vandome of Blue Yonder. She advises a pragmatic assessment based on four areas: implementation complexity, decision automation potential, operational leverage and time to value.
"Operational agents typically deliver the fastest measurable impact, often within 4-12 weeks," Morgan-Vandome told Reworked. Furthermore, AI/machine learning (ML)-based forecasting and optimization present opportunities for first-year impact, evidenced by grocers successfully "reducing waste while improving on-shelf availability,” Morgan-Vandome said.
However, the human element is critical in this early stage. "AI that meets people where they’re already at provides the best impact," said Claire Fang, CPO at Fullstory, highlighting the potential pitfalls of solutions that disrupt established, natural workflows.
Henson Tsai, CEO of omnichannel communication platform SleekFlow, advocates for a focus on "pragmatic adoption," such as automating customer service interactions or refining lead scoring processes. Thakkar reinforces this practical approach, emphasizing the value of "unsexy" yet effective projects such as contract summarization, internal FAQs and chatbots, and operational reporting.
Key performance indicators (KPIs) for the first year must reflect these objectives of immediate impact. Tankus suggests aiming for clear ROI on at least one or two pilot projects, while Morgan-Vandome focuses on "business case metrics within a functional area — often efficiency or cost-oriented," alongside "adoption of usage of specific use case" and "changes to specific workflow."
Other examples include external KPIs such as "additional revenue from new AI offerings" and internal metrics such as the "percentage of employees using AI tools on a daily basis," Fang said. Thakkar recommends tracking tangible benefits such as "time saved" and "cost per task reduced."
Year Three: Strategic AI Integration Across Teams
As the AI roadmap moves into its third year, the strategic imperative shifts from initial proof points to scaling successful initiatives, refining AI strategies and making sure AI is aligned with broader business growth and innovation targets. This is the "scale it" phase, where companies begin to "start connecting your AI use cases across departments,” Thakkar said.
"Quick wins are important catalysts for a broader mindset change across the go-to-market organization,” Tankus said. The additional revenue growth or cost savings generated can then fuel "further higher-effort, higher-impact AI developments."
The company's broader growth strategy must lead, with AI investments chosen to accelerate those primary initiatives, Tankus warned. "It is important to avoid letting easy-to-adopt 'shiny' AI tools define the company's AI strategy," she said.
Morgan-Vandome advocates for "a two-prong approach, delivering tangible short-term results while concurrently investing in robust infrastructure, data quality, governance and talent to enable sustainable growth and long-term impact."
When scaling AI agents, for instance, Morgan-Vandome suggests starting with a "common agentic framework, picking a challenge or set of challenges for a role and activating and extending the agents from there."
This helps make sure that quick wins are inherently aligned with the foundational architecture. Milestones indicating successful AI scaling by year three typically fall into three areas: "Business Case Delivery" measured by tangible KPIs, "Adoption and Organizational Enablement" including process integration and cultural shifts and "Governance Maturity" covering AI frameworks and data readiness, Morgan-Vandome said.
By year three, the organization itself could change, Fang said. "Standard headcount ratios that have been relied upon to define good and bad will look different," she predicted, such as one IT services person per 300 employees instead of 100.
Furthermore, "Annual Recurring Revenue per head should go up," and new hires, or even junior staff, "will have an AI mentor to get them up and running." By this stage, "AI will touch all of your departments, not function in silos," Tsai said. Red flags would include a lack of measurable effect on revenue or customer retention from AI tools or a rich data pipeline that isn't generating regular, actionable insights.
KPIs at the three-year mark become more comprehensive, reflecting broader integration. Tankus expects "widespread ROI and usage." Morgan-Vandome looks for "business case metrics across a functional area — efficiency or cost-oriented, often expanded to revenue improvements," alongside reusability and scale, ROI on investments, mature governance and increased AI literacy.
Year Five: Incorporating AI for Sustainable Growth
Five years into the AI journey, it should be going beyond isolated projects or departmental integration. The goal is to have AI so deeply embedded into the business model, operational processes and organizational culture that it drives long-term leadership and sustainable competitive advantage, Thakkar said. "AI should no longer be an 'initiative,’” Tsai said. “It should be infused into your products, processes and leadership thinking."
This advanced stage means AI strategies need to remain dynamic and continually evolve. Morgan-Vandome stresses the necessity to incorporate new AI capabilities and hence new business challenges, move toward autonomous actions and expand to interoperable workflows. "As the capabilities of AI leap forward, you have to take stock of what just happened and assess the impact on what you’ve already done,” Fang agreed.
"I still don’t think we’ve hit our ‘iPhone moment’ yet" with AI, Fang said, adding that organizations must remain agile when transformative shifts occur. Tsai advised adopting "a model of continuous learning, frequent performance assessments and openness to new models such as generative AI," because "what succeeded two years ago could be outdated tomorrow." e less obsessed with what the AI is and more obsessed with what your customer will need,” Thakkar said.
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A five-year AI plan encompasses five elements: strategic alignment of the AI vision with business goals; a data strategy and governance framework; a talent, change, and organization plan; a structured approach to innovation and experimentation and a commitment to agility and continuous learning, Morgan-Vandome said.
Fang added an emphasis on an "enterprise-wide data and platform strategy," ensuring AI is embedded into core product capabilities, refining organizational design and talent development for AI integration and establishing responsible AI and risk management frameworks.
Other components include AI training plans for non-tech staff, model auditing and responsible AI standards, a cross-functional AI steering committee and a dedicated research and development budget for internal AI tooling, Thakkar added.
By the five-year mark, KPIs should reflect AI's strategic impact. Tankus anticipates "a substantial cultural shift toward leveraging AI tools for everyday productivity improvement, efficiency and insight."
Morgan-Vandome’s year-five metrics include "business case metrics across a functional area – efficiency, cost-oriented or revenue improvements, often expanded to market share gains," alongside innovation capacity, ethical compliance and measurable workforce transformation. Fang’s KPIs at this stage include AI product usage as a percentage of total product usage and: revenue per employee as a fundamental business metric.
By following a phased, pragmatic roadmap, businesses can turn AI from a buzzword into a strategic growth engine. Success doesn’t come from chasing every new tool, but by incorporating AI into how the organization work, for efficiency, innovation and long-term competitive advantage.
Editor's Note: For more on AI strategy, read:
- Klarna Claimed Its AI Was Doing the Work of 700 People. Now It's Rehiring — Klarna has some good company: over 55% of organizations that executed AI-driven layoffs now regret it. Tips on how to avoid the regrets.
- Round Pegs and Square Holes: Why AI Adoption Requires a Focus on Culture — AI’s impact isn't inherent in the technology itself but in how it is deployed. Will it be a means to cut corners, or a catalyst for growth and innovation?
- Rethink Your HR Strategy to Include AI Agents — As AI agents make their way into our workforce, HR is called to adapt its workforce strategy.