A Digital Roadmap for AI Readiness in Your Organization

Oct 5

Organizations today are moving at rapid speeds towards AI readiness. It’s no surprise that recent research shows that 72% of companies are looking to increase their investment in AI each year for the next three years, while nearly 59% cite adoption as critical to their business growth.

AI has been around for a while, but what we are talking about in this leadership resource are why and how organizations need to address the adoption of generative AI. And of course there are many benefits to the adoption of generative AI, including improvement upon customer experience, operational efficiency, profitability, developing new product capabilities, increasing revenue, and all while working to meet the demands of investors.

However, you cannot have robust generative AI capabilities if you are not addressing the core needs of successful AI readiness, data optimization and quality readiness, which has been a core focus for enabling our Kander clients to make strategic and data-driven decisions in their industries, from Fortune 500 companies to global non-profits.

In this resource, I will share my approach to helping organizations create a digital roadmap for AI readiness, as well as considerations to ensure you are optimizing data quality, aligning business goals with security top of mind, and actionable steps to help increase organizational efficiency and profitability. And because in addition to consulting, I also love to travel, we will be using a plane as a metaphor for our flight plan to AI readiness. Let’s get started.

Chartering Your AI Adoption Flight Plan

As a business leader, the first step that you will want to consider before AI adoption is understanding what business processes are you looking to inject efficiencies and process improvement into. However, the success of AI implementation in a company depends largely on the business goals set for it. Organizations must ensure that the purpose of using AI is well-aligned with their key objective priorities, and that AI is the best solution for an issue before actually implementing it.

As a business, think about what specific tasks you need to address and how AI can help you. Questions that you should consider:

What are your main business goals that could be improved with AI?
What processes are business critical for optimization, including streamlining processes, creating customer segments, generating product descriptions or summarizing cases?
Who would benefit from this new solution? Is it your users, your customers, and/or other members of your business ecosystem?
What’s been on the backlog that will impede adoption of integrating new AI solutions?
How will we measure the success of AI implementations, against what KPIs?
How will it increase revenue, improve user experience, and most importantly, do you already have the tools in place that can help you move the needle without paying for additional solutions first?

This last question is really important as from my experience, during discovery we have found that many organizations are paying for redundant technology across teams that could save time and resources if centralized in use across the org. Remember that you will want to add every opportunity to save costs to your digital transformation with AI, but you need to be clear in your immediate priorities for the business (backlogged priorities exist for a reason). Dream big, but plan for small wins first.

Executives are quickly realizing that you can’t take these models off the shelf and expect to get a unique business advantage – you need to first establish business priorities and alignment with areas for AI optimization. You need to first ensure that your data quality will meet the demands of your business growth. And even once you are ready to take a new LLM for a test drive, you will still need to train them to perform for your specific business needs, with your proprietary data.

As the key decision maker in your organization, you are the pilot of your plane and ensuring that your passengers (teammates, users, customers) all meet their intended destinations on time and safely. Data is the fuel that carries your digital journey and just like any flight before take-off, we need to make sure that you have the tools needed to get you there.

Data Quality Debt = Technical Debt

Data is the backbone of all significant decision making, and that means you need to be familiar with where your data comes from. Knowing how information flows through your company is the first step in identifying what you need to make the right decisions. You'll have to collect data from many sources, whether it's structured or unstructured, ranging from sales transactions to call recordings to social media comments.

And to the many organizations that are working through a large backlog of technical debt. Your mission for quality data must be treated with the same sense of business urgency if you are truly going to be AI-ready.

To get the best results out of your AI, it's vital that your data is complete, accurate, reliable, relevant, and timely. Consider where information is being sourced outside of your Salesforce org and take the time to ensure quality data inputs so you can produce top-notch AI outputs. We have been in enough conversations about Generative AI now to know that data that you can trust is essential, but that means so is data hygiene. Data hygiene is the core to building trust.

But who is responsible for maintaining quality data? Well, everyone, but that’s another blog. Let’s talk about the critical and technical maintainers of data in your organization. Questions that you should be considering:

Have you brought them to the table and ensure that they are aligned with the new business priorities and that they have the technical resources to help you execute your vision?
Are you squashing those duplicates in your Salesforce org?
Are you using clear naming conventions to your descriptions?
Are you creating prompt governance to ensure that drifting doesn’t happen when AI is consuming your data? (Remember that your org won’t be ruined, but the data will be wrong and you should understand the consequences of dependencies.)
Have you reviewed your Fields and Objects and deprecated unused elements to avoid creating a biased AI solution?
Have you reviewed packages that you no longer need and uninstalled redundant ones?

But now that you have considered your prioritized business goals for continuous positive change with AI and discussed measures to ensure quality data is available, what about security?

AI Readiness Means Creating Governances and Keeping Security Top of Mind

Safely introducing AI to solutions to organizations has been a key focus for our clients, and while it can feel daunting, it’s important to break down measures into small iterative steps that you can test. First, ensure that your system is reliable when it comes to safety, following legal regulations, and providing customers, users, and partners with what they need. Complying with security regulations seems like a no-brainer and yet concerns are warranted. According to a recent study, 79% of IT leaders are worried about the new security risks brought on by generative AI when it comes to company and customer data.

It is very important to make sure your data strategy has privacy and security as its main focus. For instance, a secure system should not grant unrestricted access for anyone to export data and utilize public AI models. To support the implementation of AI, create a robust setup with interchangeable components that can be modified whenever needed. You and your system must be able to manage these changes.

Second, use best practices, such as data encryption, multi-factor authentication and identity and access management to ensure safeguard data security. To protect customers, businesses and their ethical principles, a combination of human judgment and tech solutions should be employed. And once you have zeroed in preferred AI solutions, I would recommend asking yourself the following questions:

Does your desired tool utilize open architecture?
Is there a comprehensive understanding of how people can use the system in different ways?
Is every feature documented thoroughly?
Do you have APIs in place that allow other systems to interconnect with your system?
Do you have a plan in place to allot sandboxes for your teams to safely test new integrated solutions before introducing them to your entire organization?
What are you going to allow and not allow generative AI to be exposed to?
What are the monitoring tools that you will implement to ensure that your teams are not creating any organizational risks?

Remember that you are building out new governance policies while you are introducing AI to your organization. From my 18+ years of senior management consulting, I can tell you that policies and procedures do not work if you do not have a top down and bottom up approach. How is your organization creating safe spaces for individual contributors to share the tools that they are using every day? How is your senior leadership creating a culture of safe experimentation, while reminding your employees about security risks? Are you creating recurring opportunities for your teams to showcase new tools and efficiency improvements?

To successfully utilize AI, craft a secure structure with interchangeable elements that may be adjusted as desired. You and your system should both have the capability to control these modifications. And when planning for AI adoption, be sure to budget in time for testing to improve risk mitigation, while measuring against the business KPIs that you put into place before piloting.

For example, maybe you want to pilot Einstein for Experience Cloud and one of your KPIs was to reduce case resolution time. If it takes you 1.5 minutes to resolve a case and you want to bring it down to 60 seconds, was your implementation of AI a success? Could the process be refined before you introduce Einstein to your other Salesforce Cloud solutions?

When investing in digital transformation, it’s all to provide business value and gaining trust in your solutions and processes before expanding to other teams. But how do you ensure that your data quality, security efforts, and governance of new AI policies are maintained? Enter creating a center of excellence.

Upgrade Your Business Plan with a Center of Excellence

If you want to keep your AI adoption plan in flight, you need to build a center of excellence (COE) to ensure new governance is maintained, while scaling to the needs of your business. Projects can (and will) go off course, as you scale and building a center of excellence will help you create shared accountability and trust in your ever-changing roadmap. If you are leading innovation projects with AI, it’s not possible to scale with just you as the captain - you need your flight crew too!

Establishing a COE is more than just about monitoring tools and procedures, but also creating transparency and education about business priorities and value. Furthermore, the creation of a COE acts as a central location for your global teams to learn from specialized technical resources, ensure that documented standards are evangelized, and that your business priorities are delivered with the levels of efficiency and security that you envisioned when starting your digital journey with AI.

At Kander, we have been consulting with global brands and recently launched a COE service to help organizations to drive user adoption, deploy faster with confidence, but also ensure proper processes and procedures to gain the most value out of your Salesforce investments.

Clients using this premiere service can anticipate increased productivity in each business area, including the completion of organizational projects, fewer risks when deploying new tools, and greater user acceptance. Our services include implementation strategies that match industry standards, technical training for end users, data governance plans, a strategy to manage software releases, and an AI readiness roadmap to help standardize processes across your organization.

Not ready to start introducing AI solutions into your organization yet? No worries. Just remember that your data quality debt is as urgent as your technical debt, and needs to be treated with the highest priority. Maybe you are ready to start building your AI business plan, but not sure where to start?

Get in contact with us for a free discovery call and we can start planning your digital future with AI.