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6 steps to build a robust AI implementation strategy in IT

Key strategies for successfully introducing AI in IT, from problem identification to vendor selection and change management.

It was 490 BCE, and Miltiades, the general of the Greek forces, had just accomplished the near impossible. He had marshaled his troops to defeat the much larger and ‘technologically superior’ Persians in the Battle of Marathon. 

Yes, the same place that gave the name to the ultra-long endurance race of modern times. Legend has it that Pheidippides, a Greek herald at the battle, was sent running from Marathon to Athens to announce the victory. 

Anyway, we are sidestepping from our story. 

What happened after the legendary battle is even more astonishing. After the Marathon defeat, the furious Darius-led Persians set sail to Athens to ransack it as it was unprotected. But little did they know that Miltiades was prepared for this. 

The shrewd general had already ordered his forces to “double time” back to Athens. So, when Darius arrived with his troops, the same Greek force was waiting to ‘welcome’ them! 

Now, what does Miltiades have to do with our blog?

Miltiades shows what advanced anticipation and preparation can do. 

One has to plan early to get ahead of the curve, especially when it comes to epochal technology shifts like AI!

According to our ‘State of AI in IT- North America 2024 Edition’, 58% of organizations are doing just that. They are in the early stages of AI adoption. However, how does one plan and get started? 

And mind you, one can’t be waiting for the ‘right’ time. The best time to start is now and, in fact, ‘double time’ it! 

So where to start?

1. Decide on the problem statements

Identify the right challenges

Start by identifying your organization's IT challenges that AI can address effectively. Look for areas where AI can optimize processes, reduce costs, or enhance decision-making. 

More importantly, during the experimentation phase, look for problems that can be a quick win to rally your troops. You can soon start to prioritize problems based on their impact on your business and the feasibility of applying AI solutions.

According to our survey, here are a few potential use cases where AI in IT can help: 

Data analytics and synthesizing insights45%Chatbot for self-service adoption38% Improving employee experience34% Workflow automation and optimization34% Optimizing cost30% Predictive maintenance and security28% IT infrastructure management28% Identifying related tickets for problem management26% Improving knowledge management capabilities24% Consolidating the tech stack16% Triaging and ranking support tickets14% People development and growth12% 

Align with business goals

Ensure that the problems you select align with your organization's broader business goals. These initiatives should not end up as siloed tech projects run by a ‘centralized’ excellence (CoE) team. These problem statements must be integral parts of your business strategy. This alignment ensures that the AI implementation drives tangible business value and supports your organization's long-term objectives.

2. Debate and decide on build vs buy

Assess internal capabilities

You must start with an honest consideration of what’s possible with your current capabilities. Whether to build your AI solution in-house or to purchase from a vendor entirely depends on this assessment. 

Building in-house requires a skilled team, and you really do need folks who’ve worked with AI. Our survey found that 28% of organizations have no dedicated AI professionals in their teams. A highly concerning stat, indeed!

If you are serious about building in-house, you must carefully map the talent you’d need to hire to get this up and running. Building in-house offers customization and control. 

Buying, on the other hand, is often more cost-effective and quicker to deploy but might require slight trade-offs in terms of customization. However, most vendors try their best to tailor their products to ensure business success for their clients. 

Also, according to our survey, the IT team was the originator of AI adoption activities in nearly two-thirds of organizations (61%), with the C-suite accounting for one-quarter (24%).  If one were to remove the “Not applicable” and “Other” responses, the inflated percentages are 72% and 28%, respectively. This clearly shows that in the organizations where the C-suite had originated the need for AI in IT,  it has progressed less than those where the IT team had done this.

With this, we mean to say that it is best that the IT team identifies the problems and takes ownership of the initiative. 

Consider budget, scalability, and integration

Whether building or buying, you must think about how the AI solution will scale with your business and integrate with existing systems. Scalability ensures that the solution remains effective as your business grows, while seamless integration is critical for operational efficiency.

In this stage, you must also understand the monetary resources you have at your disposal so that the initiative is successful. According to our survey, 60% of organizations allocate at least 5% of their IT budget to AI.

3. Select the right vendor 

If you decide to buy, you must evaluate vendors not just on the technology they offer but also on their track record, support, and ability to be a long-term partner. The right vendor should understand your use cases and be committed to evolving their product as your needs change. 

Here is a checklist of questions to consider before zeroing in on the vendor

1. Technology expertise and relevance

   - Does the vendor have proven expertise? 

   - Is their product up-to-date with current AI advancements?

2. Experience in your industry

   - Does the vendor have experience in your specific industry?

   - Can they provide case studies or references from similar organizations they've worked with?

3. Solution customizability

   - How customizable are their solutions to fit your specific business needs?

   - Can they adapt their product to integrate with your existing systems and workflows?

4. Scalability and future-proofing

   - Can their solutions scale as your business grows?

   - Do they invest in R&D to keep their technology relevant for the future?

5. Security and compliance

   - Does the vendor comply with industry-specific regulations and standards?

   - What measures do they have in place to ensure data security and privacy?

6. Implementation and integration support

   - What level of support does the vendor offer during the implementation phase? Do they offer any white-glove services to ensure better success?

   - Do they provide adequate training and resources for your team?

7. Cost structure and ROI

   - Is the pricing model transparent and predictable?

   - How does the cost compare to the projected return on investment (ROI) and value addition?

8. Vendor reputation and stability

   - What is the vendor’s reputation in the market? How are they rated on review sites? Pro tip- Check for negative reviews first. 

   - Are they financially stable and likely to be a long-term player in the market?

9. Customer support and service

   - What kind of ongoing support and maintenance services do they offer?

   - How responsive and accessible is their customer service?

10. User-friendly interface and usability

    - Is the AI solution user-friendly and easy to use for your team?

    - Does it require extensive technical skills, or is it designed for users with varied technical backgrounds?

11. Partnership approach

    - Is the vendor interested in a long-term partnership, rather than just a vendor-client transaction?

    - Do they show a willingness to understand and adapt to your evolving needs?

12. Innovation and thought leadership

    - Is the vendor recognized as an innovator or thought leader in the AI space?

    - Do they contribute to the larger ecosystem through research, publications, or speaking engagements?

13. Trial and evaluation opportunities

    - Does the vendor offer a trial period or pilot program to evaluate the effectiveness of the solution?

    - Is there a clear process for feedback and iteration during the trial phase?

4. Come up with a roll out plan

Phased implementation

A phased approach to AI implementation helps in managing risks and allows for iterative learning. Start with a pilot program, gather feedback, and progressively scale the AI solution across the organization. 

Training and support

IT leaders need to assuage any fear in the company. They must make it a point to say that AI is a powerful aid and can in no way replace humans. It can only make them better at what they do. 

Leaders should ensure the team is adequately trained and supported throughout the rollout. Continuous learning opportunities and a robust support system are vital for successful adoption.

5. Think about change management

Addressing cultural shifts

Just to reiterate, AI implementation is as much about technology as it is about people. Address potential cultural resistance by communicating the benefits of AI, involving employees in the process, and fostering a culture of innovation and adaptability.

Managing expectations

Set realistic expectations about the outcomes and timelines of AI initiatives. Clear communication about what AI can and cannot do helps in managing expectations and maintaining stakeholder support.

6. Plan on tracking adoption 

Measuring success

Establish clear metrics to measure the success of your AI initiatives. These metrics reflect both the technical performance of the AI and its impact on business objectives. 

Here are a few metrics to consider:

  • Efficiency Gains: Measure the reduction in time and resources required for processes where AI is implemented compared to traditional methods.
  • Accuracy Improvements: Assess the increase in accuracy (and reduction in errors, as a result) for tasks like data analysis, prediction accuracy, or decision-making processes.
  • Automation Rate: Quantify the proportion of tasks or processes that have been automated using AI.
  • Response Time: Evaluate the response time of AI systems, especially in customer-facing applications or support systems.
  • Cost Savings: Calculate cost reductions achieved through AI implementation, such as labor cost savings, reduced downtime, or lower operational expenses.
  • Return on Investment (ROI): Determine the financial return on AI investments compared to the initial and ongoing costs.
  • Revenue Growth: Analyze any direct or indirect impact of AI on revenue growth, such as through enhanced customer experiences or new product offerings.
  • User Adoption Rate: Measure how quickly and extensively users, in this case, employees, are adopting and integrating AI tools into their workflows. Also, gather the user satisfaction ratings. 
  • Speed of Innovation: Measure how AI has impacted the speed of developing and launching new products or services.
  • Predictive Analytics Accuracy: Measure the accuracy of forecasts and predictions made by AI systems.

Continuous improvement

Use the insights gained from adoption tracking for continuous improvement. AI is an evolving field, and staying ahead of the curve requires a commitment to ongoing learning and adaptation.


Remember that AI in IT is a marathon (see, the callback? 😀) and not a sprint!

The journey to integrating AI in IT is multifaceted, requiring careful consideration of many aspects like problem selection, the build vs buy decision, technology selection, rollout planning, change management, and adoption tracking. 

By addressing these areas comprehensively, IT leaders and practitioners can not only stay ahead of the curve but also unlock the full potential of AI to drive business transformation and success. 

A vital stat to highlight from our report is that 75% of survey respondents stated that they’re already using free AI tools like ChatGPT for their work. Our point is that if you are on the fence about why you should think about AI in IT, we hope this stat pushes you to take the leap. If your employees are already looking at AI seriously, maybe the IT team must consider it, too. 

Remember,  the majority of respondents (58%) said their organizations were still in the early stages of AI adoption – either planning (20%), early exploitation (24%), or pilot projects (14%). Only 27% of respondents have progressed past the AI pilot projects stage to have functioning AI capabilities in IT. 

So this means that there is ample opportunity to lead and excel. The time to get started is now! 

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