Just about every company is using AI to some extent today – whether it’s to automate tedious tasks, improve data analysis, enhance customer service, or improve security. But when it comes to actually scaling AI, enterprise leaders still face a lot of challenges.
Although 92% of companies are planning on increasing their AI spending in the next few years, according to McKinsey, it’s not always as simple as just “buying extra software”. Many companies need to invest in upskilling their teams, updating their governance strategies, and even upgrading their computing power. These challenges often push many companies to put AI strategies on hold.
But with a proactive, strategic approach, scaling AI doesn’t have to be a nightmare – it could be the key to unlocking new productivity, profitability, and performance levels.
Scaling AI: Enterprise Best Practices
When they’re ready to start scaling AI, enterprises can’t just decide to “spend more”. Even new AI initiatives come with challenges to address, from new compliance and security risks to issues with model maintenance and employee resistance.
So, how can companies maintain the momentum of their AI projects?
Step 1: Identifying Scaling Opportunities
Companies can’t afford to embrace every new AI innovation just because it’s new and exciting. Organizations that take a strategic approach to scaling, based on their core business goals, and comprehensive “readiness” assessments are less likely to hit roadblocks.
Start by ensuring you’re actually ready to upgrade your AI strategy. Do you have the right technology infrastructure, data, and talented professionals in place to take the next step? Have you considered any new risks or governance concerns that might emerge from a new AI deployment?
Next, look for use cases that should deliver quick wins. For instance, when scaling AI, enterprise leaders might look at their current AI initiatives and metrics like increased efficiency, productivity, or customer loyalty to pinpoint “low-hanging fruit”. If you know your AI bots are seriously improving customer experiences, maybe you could invest in agentic AI tools for customer support, or create an AI copilot designed to empower service teams.
If you’re still in the early stages of adoption, look at where your teams face the biggest bottlenecks. Are you wasting time and money on repetitive data entry processes or complex coding tasks?
Step 2: AI Model Maintenance
When scaling AI, enterprise leaders don’t necessarily need to invest in brand-new models or systems. You might just use the same solution for a new use case. For example, if you’re already using OpenAI GPTs to support your marketing team, you might be able to create new tools to help product development, sales, and finance professionals.
This strategy makes AI model maintenance critical. You’ll need to ensure the models you’re using are constantly fed with up-to-date data and monitored for accuracy to reduce the risks of potential model degradation, biases, hallucinations, and inefficiencies.
Conduct regular reviews to assess model performance against predefined benchmarks and KPIs. Find a way to consistently infuse your models with the new data your company collects (while adhering to data security and privacy standards).
Involve both IT and business teams in the retraining process or models, too. IT teams can focus on technical fine-tuning, while business teams provide insights into shifting needs and opportunities, ensuring models stay aligned with business objectives.
Step 3: Scaling AI Enterprise Training
If you want your employees to use AI solutions more often, you need to give them the insights and know-how required to navigate new systems effectively. This goes beyond simply providing teams with new documents or videos outlining AI tool features.
Be proactive and intuitive with your training. Empower teams to experiment with AI agents in a safe environment, and ask them to share feedback on their experiences. Create AI champions that can beta-test new systems and guide new staff members.
Offer structured learning opportunities through internal workshops, online courses, or expert-led seminars. These sessions help employees bridge knowledge gaps and stay updated on AI advancements. Remember to consider the different learning preferences of your employees. Some people will prefer courses they can take in their own time, while others need one-on-one instruction.
Plus, don’t forget to update your training resources regularly, particularly as new regulations emerge, and employees need to navigate evolving risks with AI tools.
Step 4: Creating an AI-First Culture Within the Enterprise
This step might seem simple, but it’s one of the toughest to navigate for many enterprises. In fact, when it comes to scaling AI, enterprise leaders often face issues with employee resistance and misalignment between teams. One study even found 42% of enterprise leaders said their AI strategies have “torn their teams apart”.
That’s why it’s so important to nurture a culture where employees feel comfortable and even excited about experimenting with AI. Develop comprehensive change management strategies, focused on transparent communication. Share the benefits of AI tools regularly with teams, and listen to their feedback and concerns.
Encourage experimentation by giving teams access to low-code and no-code tools that allow them to build their own AI tools to automate complex tasks. Ask them to actively make suggestions about where they’d like to see more AI innovation in their workflows.
Identify and celebrate early successes in AI projects to maintain momentum and enthusiasm. Clearly define roles and responsibilities within AI initiatives by assigning ownership to specific leaders or teams. For instance, appointing a senior manager as an “AI Transformation Lead” can help align AI projects closely with strategic business outcomes, fostering accountability and sustained progress.
Step 5: Staying Ahead of Emerging Trends
Going forward, companies will need to continuously scale and improve their AI initiatives as new models and systems emerge. The landscape is changing fast, with new opportunities emerging in agentic AI, multimodal generative AI systems, and even humanoid robots.
Your company might not need to adopt every new technology straight away, but it’s worth keeping a close eye on the emerging opportunities that could have a major impact on your business. Look into purpose-built solutions that can help you side-step common adoption challenges. For instance, investing in a pre-built Copilot for the healthcare industry, like Microsoft’s Dragon Copilot, could save you the effort of building and training your own model.
Experimenting with cost-effective new systems, like Google’s Gemma solution, which can run on a single GPU, could save you money on computing costs. Whenever you discover a new opportunity, start small. Run pilot programs, monitor results, and gradually implement new features to minimize disruption. Additionally, make sure you’re proactively addressing ethical and regulatory challenges each time you invest in a new AI system.
Scaling AI: Enterprise Adoption Done Right
Companies are scaling AI strategies everywhere you look, adopting intelligent models for various new use cases and applications. An agile approach to adoption and growth can help you optimize your return on investment, improve productivity, and enhance performance like never before.
However, strategy is crucial. Enterprises that successfully scale AI don’t just experiment with every new model. They systematically integrate AI into workflows with clear business goals, and a focus on consistent maintenance, upskilling and improvement.
