[guided]At this point we know that $T-T_i$ is positive and that its quadratic form tends to zero on each fixed vector. We must turn this scalar convergence into norm convergence of vectors. Fix $\xi\in H$ and define, for each $i\in I$,
\begin{align*}A_i=T-T_i.\end{align*}
The previous step proves that $A_i$ is positive. Also,
\begin{align*}(A_i\xi,\xi)_H=((T-T_i)\xi,\xi)_H=(T\xi,\xi)_H-(T_i\xi,\xi)_H=q(\xi)-q_\xi(i).\end{align*}
Since $q_\xi(i)\to q(\xi)$, it follows that
\begin{align*}(A_i\xi,\xi)_H\to 0.\end{align*}
The missing point is why this forces $\|A_i\xi\|_H\to 0$. Positivity gives the Cauchy-Schwarz estimate for the positive sesquilinear form $(u,v)\mapsto (A_i u,v)_H$:
\begin{align*}|(A_i u,v)_H|^2\le (A_i u,u)_H(A_i v,v)_H\end{align*}
for all $u,v\in H$. Applying this with $u=\xi$ and $v=A_i\xi$ gives
\begin{align*}
\|A_i\xi\|_H^4=|(A_i\xi,A_i\xi)_H|^2\le (A_i\xi,\xi)_H(A_i^2\xi,A_i\xi)_H.
\end{align*}
Using the operator norm bound on $A_i$ in the last factor gives the standard estimate
\begin{align*}
\|A_i\xi\|_H^2\le \|A_i\|_{\mathcal{L}(H)}(A_i\xi,\xi)_H.
\end{align*}
It remains to check that the factor $\|A_i\|_{\mathcal{L}(H)}$ is uniformly bounded. Since $\|T\|_{\mathcal{L}(H)}\le C$ and $\|T_i\|_{\mathcal{L}(H)}\le C$, the triangle inequality gives
\begin{align*}\|A_i\|_{\mathcal{L}(H)}=\|T-T_i\|_{\mathcal{L}(H)}\le 2C.\end{align*}
Therefore
\begin{align*}\|(T-T_i)\xi\|_H^2=\|A_i\xi\|_H^2\le 2C(A_i\xi,\xi)_H\to 0.\end{align*}
Thus $T_i\xi\to T\xi$ in $H$ for the fixed vector $\xi$. Since $\xi$ was arbitrary, the convergence is strong operator convergence.[/guided]