Regardless of the size of your company, you must be looking to leverage AI to drive business value, or you will be left behind. Do you remember when the internet was new? Who needed a website to connect with customers and prospective employees? What was digital transformation, customer portal, or mobile app?
Companies that didn’t start adopting these strategies and technologies fell far behind their competitors — or went out of business altogether. Today, can you imagine not having a website or ecommerce for your company? We are at the start of the AI revolution. Sooner than you realize, it will be hard to imagine a world in which AI adoption is a “nice-to-have.” Good news, adoption is not as hard as you might think.
AI Technologies: Truth Behind the Hype
The hype around artificial intelligence focuses on technical capabilities – and for good reason. Sensational language about large language models, data analytics, and security breaches lends itself to catchy headlines.
Make no mistake, AI isn’t just a passing fad. There will be inevitable bumps in the road that will seem to halt momentum, but AI will revolutionize the world and provide competitive advantage to those who adopt it, much like the internet did. It will change everyday life, and it will change how we do business, from the global leaders to the mom-and-pops.
AI can – and does – improve customer experience, increase employee productivity and deliver bottom line results, just as the headlines announce.
But the media focus on the large-scale implications of AI doesn’t paint the full picture or range of use cases, especially for small and mid-tier businesses who stand to gain the most. While today it may feel like AI is just one more item on a never-ending to-do list, for businesses that want to stay competitive, it will be a must-have.
The pressure to act on AI is ubiquitous. It’s a safe bet that 95% of companies have “do something with AI” on the top of their 2025 list of business goals. We see many companies worried that their competitors are building a comprehensive AI strategy, and they’re being left behind. It’s a fair concern: Creating a comprehensive AI strategy takes time, yet “low hanging fruit” and proof of value projects don’t need to be complicated nor take long to implement. These early adopter projects can be implemented within weeks or just a couple of months and have an investment payback well within one year. And we see most AI projects are expected to return 200-500% within 2-5 years of implementation. Every month you delay is opportunity left on the table.
But for all the urgency, there’s not always a clear way forward – and that’s because few businesses know exactly what AI could do for them, let alone how to get started.
What is Artificial Intelligence?
Artificial Intelligence is the attempt to simulate human intelligence. It’s a broad category for any technique that allows computers to mimic human behavior. Under the broad AI umbrella are capabilities such as machine learning that use algorithms trained to learn from data and improve their performance over time. Think of the business process improvement realm, for example automating the routing of a document for approval and recognizing uniquenesses within that document to determine whether it should go one path or route to another path.
With improved data process capabilities over the years, computer systems now have a capability called deep learning. Now, rather than depending on simple if/then statements or organized data sets, deep learning can engage multiple data sources of large unstructured data, to do things such as image recognition and support autonomous vehicles. A special category, enabled by deep learning, is generative AI (or gen AI), which uses neural networks and large language models to create original content in response to a user’s prompt or request, for example, advanced chatbots that generate human-like responses.
With all these different facets to AI, it becomes essential for any business to make sure leadership is on the same page when it comes to assessing needs and opportunities.
Here are the steps to successful AI adoption:
1. AI Awareness
A successful AI journey starts by getting stakeholders on the same page in terms of goals, priorities and expectations. It means answering the question, “What do we really want AI to do for us?”
Start your AI journey with awareness — breaking down the categories of AI-driven outcomes and how they might meet your business needs. These include:
- Individual productivity: content generation, task automation, personalized learning, time management
- Example: Using tools like Microsoft Copilot or ChatGPT to draft content, emails, identify trends in spreadsheets and prepare for meetings by gathering information about participants and prior meeting content.
- Team productivity: collaboration and communication, knowledge management, project management, onboarding and training, automated meeting scheduling
- Example: Scheduling and transcription tools to eliminate friction before, during and after meetings, leveraging AI whiteboarding tools for idea generation to better engage customers or define a new product line.
- Enterprise productivity: data analytics and reporting, customer service automation, supply chain optimization, process automation, fraud detection and risk management
- Example: Workflow automation to eliminate repetitive or error-prone actions and data analysis to provide notification of leading operational key performance indicators (KPIs).
- New markets: Driving new sales channels and revenue sources by creating new or incorporating into existing products or services AI capabilities. Using AI as the primary marketable feature
- Example: Many SAAS (software-as-a-service) products are touting their AI capabilities: ServiceNow, OpenAI ChatGPT and other industry-specific AI applications.
Once you understand the parameters of AI as they pertain to your business, it’s time to roll up your sleeves and focus on identifying target opportunities that will improve your business.
2. AI Discovery
If you start an AI journey with the urgent feeling, “We need to leverage AI,” a discovery process will help change the thinking into a focused question: “What are our business opportunities and inefficiencies holding back our growth?” This part of the process works well as a workshop – or a series of working sessions – to brainstorm ideas and consider the full realm of possibilities.
You may end up with hundreds of ideas – that doesn’t mean every one of them needs AI or automation, now (or ever). Take your wish list and prioritize: What are the repetitive tasks that lend themselves to automation? Where does human error risk hurting customer satisfaction?
At this stage, your list may still be way too long – and not grounded in the reality of your business objectives. A technology assessment is a central part of discovery, as it helps you understand how to make the most of your existing tools and systems. After all, why build when you don’t have to? On the flip side, a technology assessment will help avoid any unintended consequences – for example, turning on an AI assistant and giving it access to sensitive client or HR information.
A two-to-four-week discovery process for most mid-sized businesses will result in a roadmap of recommendations with some low-hanging fruit to pick right away and areas to improve in for both the medium and long term.
3. AI Implementation
Once you reach the implementation phase, you’ll have identified some quick wins to adopt right away. We typically see a mix of business process automation (BPA) and personal productivity (such as Copilot or private ChatGPT). Often, a quick win and an opportunity to build momentum is to provide employees and customers with an AI-driven Chatbot making it easy to find and understand company policies, specific company research, or product information.
In addition to adding these new elements to the business, part of implementation is looking at a company’s data, cleaning it up, making sure it has the right security in place and creating an ongoing strategy for maintenance. Data management is more than just getting your house in order, because you are going to make incremental improvements as you advance an initiative that implements efficiencies.
You should look at implementation as an AI Journey: start with a small use case, measure value, and move towards larger use cases as you learn.
4. Advanced AI Solutions – Predictive Analytics Use Cases, Models and Tools
As you progress along the AI journey beyond quick wins and data cleanup, you can set your sights on more impactful projects, ones that drive customer growth or create significant efficiencies. AI can help businesses answer bigger, more complex questions around supply chain, staffing needs, and more.
Take for example a mid-size construction and engineering firm who wanted to proactively adopt AI for competitive advantage. With the help of our AI consulting services, they chose personal productivity and team-based collaboration tools for their early AI adoption projects, with the idea of testing and learning from this initial effort.
They implemented a secure chatbot for all employees, providing a personal assistant to quickly find answers to company policies, processes, and critical information. In addition to increasing employee efficiency, they were able to streamline new employee onboarding by providing easy access to their internal resources.
As part of this initial phase, they also adopted a Copilot center-of-excellence with the objective to find new ways to work and improve operational process along with individual productivity.
The early adopter project of building a virtual assistant served a dual purpose. The task of validating the official documents and security settings that would be part of the chatbot’s dataset laid the technical groundwork for further AI development projects. The project also contributed to change management, and provided an opportunity to gauge how employees were adopting these personal and team productivity tools, which would inform the design and rollout of future initiatives.
By scoring some quick wins, they were able to build excitement for further adoption while tackling data management. They are now well on their way to utilizing their data to leverage AI development projects focused on reducing manual efforts to create proposals, reducing risk and time with legal contract reviews, ongoing cybersecurity monitoring, improving executive level KPI dashboards, and alerting on real-time leading indicators and business metrics.
AI is What You Make of It
From splashy headlines to the top of every company’s list of must-haves, AI can seem daunting, vague, and expensive. But scoring an AI win is easier (and more affordable) than you think. It starts with getting your fundamentals in order: What business and operational problems are you trying to solve? What opportunities and limitations do you have in your current technology? It’s an iterative process that requires ongoing strategy, governance and security management – but there’s also a lot of incremental improvements and immediate impacts you can see along the way.
Create a strong foundation and put a use-case to the test, and AI can set your business into a virtuous cycle, compounding returns year after year.
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